This PDF file contains the front matter associated with SPIE Proceedings Volume 8317, including the Title Page, Copyright information, Table of Contents, and the Conference Committee listing.

Through cognitive tasks certain brain areas are activated and also receive increased blood to them. This is modeled through a state system consisting of two separate parts one that deals with the neural node stimulation and the other blood response during that stimulation. The rationale behind using this state system is to validate existing analysis methods such as DCM to see what levels of noise they can handle. Using the forward Euler's method this system was approximated in a series of difference equations. What was obtained was the hemodynamic response for each brain area and this was used to test an analysis tool to estimate functional connectivity between each brain area with a given amount of noise. The importance of modeling this system is to not only have a model for neural response but also to compare to actual data obtained through functional imaging scans.

A novel MRI protocol has been developed to investigate the differential effects of glucose or fructose consumption on whole-brain functional brain connectivity. A previous study has reported a decrease in the fMRI blood oxygen level dependent (BOLD) signal of the hypothalamus following glucose ingestion, but due to technical limitations, was restricted to a single slice covering the hypothalamus, and thus unable to detect whole-brain connectivity. In another previous study, a protocol was devised to acquire whole-brain fMRI data following food intake, but only after restricting image acquisition to an MR sampling or repetition time (TR) of 20s, making the protocol unsuitable to detect functional connectivity above 0.025Hz. We have successfully implemented a continuous 36-min, 40 contiguous slices, whole-brain BOLD acquisition protocol on a 3T scanner with TR=4.5s to ensure detection of up to 0.1Hz frequencies for whole-brain functional connectivity analysis. Human data were acquired first with ingestion of water only, followed by a glucose or fructose drink within the scanner, without interrupting the scanning. Whole-brain connectivity was analyzed using standard correlation methodology in the 0.01-0.1 Hz range. The correlation coefficient differences between fructose and glucose ingestion among targeted regions were converted to t-scores using the water-only correlation coefficients as a null condition. Results show a dramatic increase in the hypothalamic connectivity to the hippocampus, amygdala, insula, caudate and the nucleus accumben for fructose over glucose. As these regions are known to be key components of the feeding and reward brain circuits, these results suggest a preference for fructose ingestion.

Real-time fMRI (rtfMRI) is a new technology which allows human subjects to observe and control their own BOLD signal change from one or more localized brain regions during scanning. Current rtfMRI-neurofeedback studies mainly focused on the target region itself without considering other related regions influenced by the real-time feedback. However, there always exits important directional influence between many of cooperative regions. On the other hand, rtfMRI based on motor imagery mainly aimed at somatomotor cortex or primary motor area, whereas supplement motor area (SMA) was a relatively more integrated and pivotal region. In this study, we investigated whether the activities of SMA can be controlled utilizing different motor imagery strategies, and whether there exists any possible impact on an unregulated but related region, primary motor cortex (M1). SMA was first localized using overt finger tapping task, the activities of SMA were feedback to subjects visually on line during each of two subsequent imagery motor movement sessions. All thirteen healthy participants were found to be able to successfully control their SMA activities by self-fit imagery strategies which involved no actual motor movements. The activation of right M1 was also found to be significantly reduced in both intensity and extent with the neurofeedback process targeted at SMA, suggestive that not only the part of motor cortex activities were influenced under the regulation of a key region SMA, but also the increased difference between SMA and M1 might reflect the potential learning effect.

While the use of acupuncture as a complementary therapeutic method for treating MCI is popular in certain parts of the world, the underlying mechanism is still elusive. In the current study, we adopted multivariate Granger causality analysis (mGCA) to explore the causal interactions of brain networks involving acupuncture in mild cognitive impairment (MCI) patients compared to healthy controls (HC). The fMRI experiment was performed with two different paradigms: namely, deep acupuncture (DA) and superficial acupuncture (SA) at acupoint KI3. Results demonstrated that deep acupuncture could modulate the abnormal regions in MCI group. These regions are implicated in memory encoding and retrieving. This may relate to the purported therapeutically beneficial effects of acupuncture for the treatment of MCI. However, the most significant causal interactions were found in the sensorimotor regions in HC group. This may because acupuncture has a greater modulatory effect on patients with a pathological imbalance. This paper provides the preliminary neurophysiological evidence for the potential efficacy effect of acupuncture on MCI.

Independent component analysis (ICA) is a data-driven approach that has been widely applied to functional magnetic resonance imaging (fMRI) data analysis. As an exploratory technique, traditional ICA does not require any prior information about the sources and the mixing matrix. However, it has been demonstrated that incorporating paradigm information into the ICA analysis can improve the performance of traditional ICA. In 2005, Calhoun proposed semi-blind ICA which improved the robustness of Infomax ICA in the presence of noises by regulating the estimated time courses with paradigm information. Different from the Infomax ICA algorithm, FastICA is able to estimating independent components one by one. If the target component can be estimated earlier, the FastICA algorithm can be terminated beforehand. Therefore, the order of the target component is important for FastICA to reduce computational time during one-to-one hierarchical estimation. In this paper, we proposed semi-blind FastICA by adding regularization of the first estimated time course using the paradigm information to the FastICA algorithm. We demonstrated the feasibility and effectiveness of our approach in extracting the task-related component from single-task fMRI datasets of block design. Results of both simulated and real fMRI data suggest that (1) In contrast to FastICA, the time of extracting the target component by semi-blind FastICA is largely reduced;(2) Semi-blind FastICA can accurately extract the task-related IC as the first one; (3) Semi-blind FastICA can estimate more accurate time course of the task-related component than FastICA.

The corpus callosum (CC) is a structure of interest in many neuroimaging studies of neuro-developmental pathology such as autism. It plays an integral role in relaying sensory, motor and cognitive information from homologous regions in both hemispheres. We have developed a framework that allows automatic segmentation of the corpus callosum and its lobar subdivisions. Our approach employs constrained elastic deformation of flexible Fourier contour model, and is an extension of Szekely's 2D Fourier descriptor based Active Shape Model. The shape and appearance model, derived from a large mixed population of 150+ subjects, is described with complex Fourier descriptors in a principal component shape space. Using MNI space aligned T1w MRI data, the CC segmentation is initialized on the mid-sagittal plane using the tissue segmentation. A multi-step optimization strategy, with two constrained steps and a final unconstrained step, is then applied. If needed, interactive segmentation can be performed via contour repulsion points. Lobar connectivity based parcellation of the corpus callosum can finally be computed via the use of a probabilistic CC subdivision model. Our analysis framework has been integrated in an open-source, end-to-end application called CCSeg both with a command line and Qt-based graphical user interface (available on NITRC). A study has been performed to quantify the reliability of the semi-automatic segmentation on a small pediatric dataset. Using 5 subjects randomly segmented 3 times by two experts, the intra-class correlation coefficient showed a superb reliability (0.99). CCSeg is currently applied to a large longitudinal pediatric study of brain development in autism.

Diffusion tensor MR imaging (DTI) provides information on diffusion anisotropy in vivo, which can be exhibited three-dimensional white matter tractography. Five healthy volunteers and five right-hand affected patients with early subacute ischaemic infarction involving the posterior limb of the internal capsule or corona radiate were recruited in this study. We used 3D white matter tractography to show the corticospinal tract in both volunteer group and stroke group. Then we compared parameters of the corticospinal tract in patients with that in normal subjects and assessed the relationships between the fiber number of the corticospinal tract in ipsilesional hemisphere and indicators of the patients' rehabilitation using Pearson correlation analysis. The fractional anisotropy (FA) values and apparent diffusion coefficient (ADC) values in the ipsilesional corticospinal tract may significantly reduce comparing with the volunteer group. In addition, the stroke patient with less fiber number of the ipsilesional corticospinal tract may bear more possibilities of better motor rehabilitation. The FA values, ADC values and fiber number of the corticospinal tract in the ipsilesional hemisphere might be helpful to the prognosis and prediction of clinical treatment in stroke patients.

As with other mental disorders, the causes of borderline personality disorder (BPD) are complex and not fully understood. In this study we aimed to determine whether adults with BPD exhibit microstructural abnormalities using diffusion tensor imaging (DTI). 56 female right-handed individuals (age range, 14-18 years), 19 with a DSM-IV diagnosis of BPD, 18 patients with a DSM-IV defined current psychiatric disorder and 19 healthy control subjects were included. Groups were matched for age and IQ. DTI Images were analyzed using Tract-Based Spatial Statistics (TBSS). The analysis revealed significanty reduced fractional anisotropy (FA) values in the group of BPD patients compared to the normal controls. Similar FA reductions could not be found comparing BPD patients to clinical controls. Several clusters of increased radial (DR), axial (DA), and mean (MD) diffusivity were consistently identified when comparing the BPD patients to clinical as well as to healthy controls. None of the measures showed significant differences between the clinical and healthy controls. Diverse possible factors have been suggested to play a role in the disease, including environmental factors, neurobiological factors, or brain abnormalities. The presented results may play an important role in this ongoing debate.

A negative blood oxygen level - dependent (BOLD) has been associated with a high concentration of GABA using Magnetic Resonance Spectroscopy and fMRI. Subjects with long-allele carriers have seen with high concentration of serotonin in Rostral Subgenual portion of the anterior cingulate cortex (rACC). In this paper, we investigate the effect of serotonin concentration on hemodynamic responses. Our results show a negative BOLD signal in rACC in the subjects with long-allele carriers. In contrast, the subjects with short-allele carriers showed positive BOLD signals in rACC. These results suggest that the serotonin transporter gene impacts the neuronal activity and eventually the BOLD signal similar to GABA.

The factors behind the neural mechanisms that motivate food choice and obesity are not well known. Furthermore, it is not known when these neural mechanisms develop and how they are influenced by both genetic and environmental factors. This study uses fMRI together with clinical data to shed light on the aforementioned questions by investigating how appetite-related activation in the brain changes with low versus high caloric foods in pre-pubescent girls. Previous studies have shown that obese adults have less striatal D2 receptors and thus reduced Dopamine (DA) signaling leading to the reward-deficit theory of obesity. However, overeating in itself reduces D2 receptor density, D2 sensitivity and thus reward sensitivity. The results of this study will show how early these neural mechanisms develop and what effect the drastic endocrinological changes during puberty has on these mechanisms. Our preliminary results showed increased activations in the Putamen, Insula, Thalamus and Hippocampus when looking at activations where High Calorie > Low Calorie. When comparing High Calorie > Control and Low Calorie > Control, the High > Control test showed increased significant activation in the frontal lobe. The Low > Control also yielded significant activation in the Left and Right Fusiform Gyrus, which did not appear in the High > Control test. These results indicate that the reward pathway activations previously shown in post-puberty and adults are present in pre-pubescent teens. These results may suggest that some of the preferential neural mechanisms of reward are already present pre-puberty.

Real-time functional magnetic resonance imaging (rtfMRI) can be used to train the subjects to selectively control activity of specific brain area so as to affect the activation in the target region and even to improve cognition and behavior. So far, whether brain activity in posterior cingulate cortex (PCC) can be regulated by rtfMRI has not been reported. In the present study, we aimed at investigating whether real-time regulation of activity in PCC can change the functional connectivity between PCC and other brain regions. A total of 12 subjects underwent two training runs, each lasts 782s. During the training, subjects were instructed to down regulate activity in PCC by imagining right hand finger movement with the sequence of 4-2-3-1-3-4-2 during task and relax as possible as they can during rest. To control for any effects induced by repeated practice, another 12 subjects in the control group received the same experiment procedure and instruction except with no feedback during training. Experiment results show that increased functional connectivity of PCC with medial frontal cortex (MFC) was observed in both groups during the two training runs. However, PCC of the experimental group is correlated with larger areas in MFC than the control group. Because the positive correlation between task performance and MFC to PCC connectivity has been demonstrated previously, we infer that the stronger connectivity between PCC and MFC in the experimental group may suggest that the experimental group with neurofeedback can more efficiently regulate PCC than the control group without neurofeedback.

Intracranial aneurysms and artery stenosis are vascular diseases with different pathophysiological characteristics. However, although unusual, aneurysms may coexist in up to 5% of patients with stenotic plaque, according to a previous study. Another study showed that incidental detection of cerebral aneurysm in the same cerebral circulation as the stenotic plaque was less than 2%. Patients with concomitant carotid artery stenosis and unruptured intracranial aneurysms pose a difficult management decision for the physician. Case reports showed patients who died due to aneurysm rupture months after endarterectomy but before aneurysm clipping, while others did not show any change in the aneurysm after plaque removal, having optimum outcome after aneurysm coiling. The purpose of this study is to investigate the intraaneurysmal hemodynamic changes before and after treatment of stenotic plaque. Idealized models were constructed with different stenotic grade, distance and relative position to the aneurysm. Digital removal of the stenotic plaque was performed in the reconstructed model of a patient with both pathologies. Computational fluid dynamic simulations were performed using a finite element method approach. Blood velocity field and hemodynamic forces were recorded and analyzed. Changes in the flow patterns and wall shear stress values and distributions were observed in both ideal and image-based models. Detailed investigation of wall shear stress distributions in patients with both pathologies is required to make the best management decision.

This paper describes an experimental system for validation of an approach to non-invasive determination of pressure gradients in stenotic flows as encountered in peripheral arterial disease. Pressure gradient across a Gaussian-shaped 87% area stenosis phantom was estimated by solving the pressure Poisson equation (PPE) for a steady flow mimicking the blood flow through the human iliac artery. The velocity field needed to solve the pressure equation was obtained using Phase-Contrast MRI (PC-MRI) and Stereoscopic Particle Image Velocimetry (SPIV). Steady flow rate of 46.9 ml/s was used, which corresponds to a Reynolds number of 188 and 595 at the inlet and stenosis throat, respectively (in the range of mean Reynolds number encountered, in-vivo). Results of PC-MRI and SPIV have been compared to the pressures measured directly by a pressure catheter transducer. The reconstructed pressure drop along the centerline overestimates the catheter reference pressure drop by 8.5% and 10.5% for PC_MRI and SPIV methods, respectively.

Intracranial aneurysm treatment with flow diverters (FD) is a new minimally invasive approach, recently approved for use in human patients. Attempts to correlate the flow reduction observed in angiograms with a parameter related to the FD structure have not been totally successful. To find the proper parameter, we investigated four porous-media flow models. The models describing the relation between the pressure drop and flow velocity that are investigated include the capillary theory linear model (CTLM), the drag force linear model (DFLM), the simple quadratic model (SQM) and the modified quadratic model (MQM). Proportionality parameters are referred to as permeability for the linear models and resistance for the quadratic ones. A two stage experiment was performed. First, we verified flow model validity by placing six different stainless-steel meshes, resembling FD structures, in known flow conditions. The best flow model was used for the second stage, where six different FD's were inserted in aneurysm phantoms and flow modification was estimated using angiographically derived time density curves (TDC). Finally, TDC peak variation was compared with the FD parameter. Model validity experiments indicated errors of: 70% for the linear models, 26% for the SQM and 7% for the MQM. The resistance calculated according to the MQM model correlated well with the contrast flow reduction. Results indicate that resistance calculated according to MQM is appropriate to characterize the FD and could explain the flow modification observed in angiograms.

Acute pulmonary embolism (APE) is known as one of the major causes of sudden death. However, high level of mortality caused by APE can be reduced, if detected in early stages of development. Hence, biomarkers capable of early detection of APE are of utmost importance. This study investigates how APE affects the biomechanics of the cardiac right ventricle (RV), taking one step towards developing functional biomarkers for early diagnosis and determination of prognosis of APE. To that end, we conducted a pilot study in pigs, which revealed the following major changes due to the severe RV afterload caused by APE: (1) waving paradoxical motion of the RV inner boundary, (2) decrease in local curvature of the septum, (3) lower positive correlation between the movement of inner boundaries of the septal and free walls of the RV, (4) slower blood ejection by the RV, and (5) discontinuous movement observed particularly in the middle of the RV septal wall.

Late stent graft failure is a serious complication in endovascular repair of aortic aneurysms. Better understanding of the motion characteristics of stent grafts will be beneficial for designing future devices. In addition, analysis of stent graft movement in individual patients in vivo can be valuable for predicting stent graft failure in these patients. To be able to gather information on stent graft motion in a quick and robust fashion, an automatic segmentation method is required. In this work we compare two segmentation methods that produce a geometric model in the form of an undirected graph. The first method tracks along the centerline of the stent and segments the stent in 2D slices sampled orthogonal to it. The second method used a modified version of the minimum cost path (MCP) method to segment the stent directly in 3D. Using annotated reference data both methods were evaluated in an experiment. The results show that the centerline-based method and the MCP-based method have an accuracy of approximately 65% and 92%, respectively. The difference in accuracy can be explained by the fact that the centerline method makes assumptions about the topology of the stent which do not always hold in practice. This causes difficulties that are hard and sometimes impossible to overcome. In contrast, the MCP-based method works directly in 3D and is capable of segmenting a large variety of stent shapes and stent types.

Carotid artery total plaque volume (TPV) is a three-dimensional (3D) ultrasound (US) imaging measurement of carotid atherosclerosis, providing a direct non-invasive and regional estimation of atherosclerotic plaque volume - the direct determinant of carotid stenosis and ischemic stroke. While 3DUS measurements of TPV provide the potential to monitor plaque in individual patients and in populations enrolled in clinical trials, until now, such measurements have been performed manually which is laborious, time-consuming and prone to intra-observer and inter-observer variability. To address this critical translational limitation, here we describe the development and application of a semi-automated 3DUS plaque volume measurement. This semi-automated TPV measurement incorporates three user-selected boundaries in two views of the 3DUS volume to generate a geometric approximation of TPV for each plaque measured. We compared semi-automated repeated measurements to manual segmentation of 22 individual plaques ranging in volume from 2mm³ to 151mm³. Mean plaque volume was 43±40mm³ for semi-automated and 48±46mm³ for manual measurements and these were not significantly different (p=0.60). Mean coefficient of variation (CV) was 12.0±5.1% for the semi-automated measurements.

The manuscript presents the automated detection and segmentation of hepatic tumors from abdominal CT images with variable acquisition parameters. After obtaining an initial segmentation of the liver, optimized graph cuts segment the liver tumor candidates using shape and enhancement constraints. One hundred and fifty-seven features are computed for the tumor candidates and support vector machines are used to select features and separate true and false detections. Training and testing are performed using leave-one-patientout on 14 patients with a total of 79 tumors. After selection, the feature space is reduced to eight. The resulting sensitivity for tumor detection was 100% at 2.3 false positives/case. For the true tumors, 74.1% overlap and 1.6mm average surface distance were recorded between the ground truth and the results of the automated method. Results from test data demonstrate the method's robustness to analyze livers from difficult clinical cases to allow the diagnoses and temporal monitoring of patients with hepatic cancer.

In the last years, it has become increasingly clear that neurodegenerative diseases involve protein aggregation, a process often used as disease progression readout and to develop therapeutic strategies. This work presents an image processing tool to automatic segment, classify and quantify these aggregates and the whole 3D body of the nematode Caenorhabditis Elegans. A total of 150 data set images, containing different slices, were captured with a confocal microscope from animals of distinct genetic conditions. Because of the animals' transparency, most of the slices pixels appeared dark, hampering their body volume direct reconstruction. Therefore, for each data set, all slices were stacked in one single 2D image in order to determine a volume approximation. The gradient of this image was input to an anisotropic diffusion algorithm that uses the Tukey's biweight as edge-stopping function. The image histogram median of this outcome was used to dynamically determine a thresholding level, which allows the determination of a smoothed exterior contour of the worm and the medial axis of the worm body from thinning its skeleton. Based on this exterior contour diameter and the medial animal axis, random 3D points were then calculated to produce a volume mesh approximation. The protein aggregations were subsequently segmented based on an iso-value and blended with the resulting volume mesh. The results obtained were consistent with qualitative observations in literature, allowing non-biased, reliable and high throughput protein aggregates quantification. This may lead to a significant improvement on neurodegenerative diseases treatment planning and interventions prevention.

Description of purpose: The NA-MIC SPHARM-PDM Toolbox represents an automated set of tools for the computation of 3D structural statistical shape analysis. SPHARM-PDM solves the correspondence problem by defining a first order ellipsoid aligned, uniform spherical parameterization for each object with correspondence established at equivalently parameterized points. However, SPHARM correspondence has shown to be inadequate for some biological shapes that are not well described by a uniform spherical parameterization. Entropy-based particle systems compute correspondence by representing surfaces as discrete point sets that does not rely on any inherent parameterization. However, they are sensitive to initialization and have little ability to recover from initial errors. By combining both methodologies we compute reliable correspondences in topologically challenging biological shapes. Data: Diverse subcortical structures cohorts were used, obtained from MR brain images. Method(s): The SPHARM-PDM shape analysis toolbox was used to compute point based correspondent models that were then used as initializing particles for the entropy-based particle systems. The combined framework was implemented as a stand-alone Slicer3 module, which works as an end-to-end shape analysis module. Results: The combined SPHARM-PDM-Particle framework has demonstrated to improve correspondence in the example dataset over the conventional SPHARM-PDM toolbox. Conclusions: The work presented in this paper demonstrates a two-sided improvement for the scientific community, being able to 1) find good correspondences among spherically topological shapes, that can be used in many morphometry studies 2) offer an end-to-end solution that will facilitate the access to shape analysis framework to users without computer expertise.

In SPECT imaging, patient respiratory and body motion can cause artifacts that degrade image quality. Developing and evaluating motion correction algorithms are facilitated by simulation studies where a numerical phantom and its motion are precisely known, from which image data can be produced. Previous techniques to test motion correction methods generated XCAT phantoms modeled from MRI studies and motion tracking but required manually segmenting the major structures within the whole upper torso, which can take 8 hours to perform. Additionally, segmentation in two dimensional MRI slices and interpolating into three dimensional shapes can lead to appreciable interpolation artifacts as well as requiring expert knowledge of human anatomy in order to identify the regions to be segmented within each slice. We propose a new method that mitigates the long manual segmentation times for segmenting the upper torso. Our interactive method requires that a user provide only an approximate alignment of the base anatomical shapes from the XCAT model with an MRI data. Organ boundaries from aligned XCAT models are warped with displacement fields generated from registering a baseline MR image to MR images acquired during pre-determined motions, which amounts to automated segmentation each organ of interest. With our method we can show the quality of segmentation is equal that of expert manual segmentation does not require a user who is an expert in anatomy, and can be completed in minutes not hours. In some instances, due to interpolation artifacts, our method can generate higher quality models than manual segmentation.

Methods for 3D histology reconstruction from sparse 2D digital histology images depend on knowledge about the positions, orientations, and deformations of tissue slices due to the histology process. This work quantitatively evaluates typical assumptions about the position and orientation of whole-mount prostate histology sections within coarsely sliced tissue blocks and about the deformation of tissue during histological processing and sectioning. 3-5 midgland tissue blocks from each of 7 radical prostatectomy specimens were imaged using magnetic resonance imaging before histology processing. After standard whole-mount paraffin processing and sectioning, the resulting sections were digitised. Homologous anatomic landmarks were identified on 22 midgland histology and MR images. Orientations and depths of sections relative to the front faces of the tissue blocks were measured based on the best-fit plane through the landmarks on the MR images. The mean±std section orientation was 1.7±1.1° and the mean±std depth of the sections was 1.0±0.5 mm. Deformation was assessed by using four transformation models (rigid, rigid+scale, affine and thin-plate-spline (TPS)) to align landmarks from histology and MR images, and evaluating each by measuring the target registration error (TRE) using a leave-one-out cross-validation. The rigid transformation model had higher mean TRE (p<0.001) than the other models, and the rigid+scale and affine models had higher mean TRE than the TPS model (p<0.001 and p<0.01 respectively). These results informed the design and development of a method for 3D prostate histology reconstruction based on extrinsic strand-shaped fiducial markers which yielded a 0.7±0.4 mm mean±std TRE.

Magnetic spectroscopy of nanoparticle Brownian motion, MSB, uses the magnetization produced by magnetic nanoparticles in a sinusoidal magnetic field, which can be observed remotely at low enough concentrations to enable it to be used for "molecular imaging". The MSB signal is sensitive to chemical binding, temperature and viscosity. If the MSB signals from nanoparticles in multiple bound states are known, a mixture model can be used to find the concentration of nanoparticles in each bound state. The accuracy has been shown to be high for two and three bound states. However, if the bound states are not accurately known, as is often the case in vivo, the model is perturbed significantly. Using simulations of two bound states based on the effective field approximation to the Fokker-Planck equations, we show that the error in the bound fraction is roughly proportional to the error in the bound state relaxation time. The errors in bound fraction were roughly proportional to the error in the relaxation time for the bound state used in the mixture model. The largest errors occurred for short relaxation time bound states. But for all bound state relaxation times, 10% errors in the relaxation time of the bound state resulted in errors in the bound fraction of less than 10%.

Recent advances in nanotechnology have allowed for the effective use of iron oxide nanoparticles (IONPs) for cancer imaging and therapy. When activated by an alternating magnetic field (AMF), intra-tumoral IONPs have been effective at controlling tumor growth in rodent models. To accurately plan and assess IONP-based therapies in clinical patients, noninvasive and quantitative imaging technique for the assessment of IONP uptake and biodistribution will be necessary. Proven techniques such as confocal, light and electron microscopy, histochemical iron staining, ICP-MS, fluorescent labeled mNPs and magnetic spectroscopy of Brownian motion (MSB), are being used to assess and quantify IONPs in vitro and in ex vivo tissues. However, a proven noninvasive in vivo IONP imaging technique has not yet been developed. In this study we have demonstrated the shortcomings of computed tomography (CT) and magnetic resonance imaging (MRI) for effectively observing and quantifying iron /IONP concentrations in the clinical setting. Despite the poor outcomes of CT and standard MR sequences in the therapeutic concentration range, ultra-short T2 MRI methods such as, Sweep Imaging With Fourier Transformation (SWIFT), provide a positive iron contrast enhancement and a reduced signal to noise ratio. Ongoing software development and phantom and in vivo studies, will further optimize this technique, providing accurate, clinically-relevant IONP biodistribution information.

Beside the original scanner geometry for Magnetic Particle Imaging (MPI) introduced by Gleich et. al. in 2005,¹ alternative scanner geometries have been introduced.^2-4 In excess of the opportunities in medical application offered by MPI itself, these new scanner geometries permit additional medical application scenarios. Here, the single-sided scanner geometry is implemented as imaging device for supporting the sentinel lymph node biopsy concept. In this contribution, the medical application is outlined, and the geometry of the scanner device is presented together with first simulation results providing information about the achievable image quality.

Phase-Locking value (PLV) is used to measure phase synchrony of narrowband signals, therefore, it is able to provide a measure of dynamic functional connectivity (DFC) of brain interactions. Currently used PLV methods compute the convolution of the signal at the target frequency with a complex Gabor wavelet centered at that frequency. The phase of this convolution is extracted for all time-bins over trials for a pair of neural signals. These time-bins set a limit on the temporal resolution for PLV, hence, for DFC. Therefore, these methods cannot provide a true DFC in a strict sense. PLV is defined as the absolute value of the characteristic function of the difference of instantaneous phases (IP) of two analytic signals evaluated at s = 1. It is a function of the time. For the narrowband signal in the stationary Gaussian white noise, we investigated statistics of (i) its phase, (ii) the maximum likelihood estimate of its phase, and (iii) the phase-lock loop (PLL) measurement of its phase, derived the analytic form of the probability density function (pdf) of the difference of IP, and expressed this pdf in terms of signal-to-noise ratio (SNR) of signals. PLV is finally given by analytic formulas in terms of SNRs of a pair of neural signals under investigation. In this new approach, SNR, hence PLV, is evaluated at any time instant over repeated trials. Thus, the new approach can provide a true DFC via PLV. This paper presents detailed derivations of this approach and results obtained by using simulations for magnetoencephalography (MEG) data.

As an ancient therapeutic technique in Traditional Chinese Medicine, acupuncture has been used increasingly in modern society to treat a range of clinical conditions as an alternative and complementary therapy. However, acupoint specificity, lying at the core of acupuncture, still faces many controversies. Considering previous neuroimaging studies on acupuncture have mainly employed functional magnetic resonance imaging, which only measures the secondary effect of neural activity on cerebral metabolism and hemodynamics, in the current study, we adopted an electrophysiological measurement technique named magnetoencephalography (MEG) to measure the direct neural activity. 28 healthy college students were recruited in this study. We filtered MEG data into 5 consecutive frequency bands (delta, theta, alpha, beta and gamma band) and grouped 140 sensors into 10 main brain regions (left/right frontal, central, temporal, parietal and occipital regions). Fast Fourier Transformation (FFT) based spectral analysis approach was further performed to explore the differential band-limited power change patterns of acupuncture at Stomach Meridian 36 (ST36) using a nearby nonacupoint (NAP) as control condition. Significantly increased delta power and decreased alpha as well as beta power in bilateral frontal ROIs were observed following stimulation at ST36. Compared with ST36, decreased alpha power in left and right central, right parietal as well as right temporal ROIs were detected in NAP group. Our research results may provide additional evidence for acupoint specificity.

In this pilot study, we explored the potential of using a diffuse reflectance imaging system to extract interictal pathophysiological characteristics of epileptic cortex in an intraoperative setting. The imaging system was able to simultaneously measure diffuse reflectance signals at two distinct wavelengths (500 and 700 nm) from the entire exposed cortical surface. It was used to study ten pediatric patients during their epilepsy surgery. Diffuse reflectance images, Rd(x,y,λ,t) at 500 nm and 700 nm, were acquired at a 5 Hz rate for at least 200 seconds. Post imaging analysis identified a unique local frequency oscillation (LFO), below respiration rate, existed in Rd(x,y,500 nm,t) and Rd(x,y,700 nm,t). Mapping the spectral densities of LFOs over the cortical surface identified the spatial distribution of the LFOs. In almost all ten patients studied, the location demonstrating strong LFOs coincided with the epileptic cortex determined using ECoG. However, some LFOs were found in close proximity to functional areas according to fMRI. We further used the correlation coefficient map to identify those pixels with similar waveforms for better demarcation. These preliminary results support the feasibility of using wavelength-dependent diffuse reflectance imaging to intra-operatively detect epileptic cortex.

This paper focuses on discovering statistical biomarkers (features) that are predictive of schizophrenia, with a particular focus on topological properties of fMRI functional networks. We consider several network properties, such as node (voxel) strength, clustering coefficients, local efficiency, as well as just a subset of pairwise correlations. While all types of features demonstrate highly significant statistical differences in several brain areas, and close to 80% classification accuracy, the most remarkable results of 93% accuracy are achieved by using a small subset of only a dozen of most-informative (lowest p-value) correlation features. Our results suggest that voxel-level correlations and functional network features derived from them are highly informative about schizophrenia and can be used as statistical biomarkers for the disease.

Autobiographical memory is the ability to recollect past events from one's own life. Both emotional tone and memory remoteness can influence autobiographical memory retrieval along the time axis of one's life. Although numerous studies have been performed to investigate brain regions involved in retrieving processes of autobiographical memory, the effect of emotional tone and memory age on autobiographical memory retrieval remains to be clarified. Moreover, whether the involvement of hippocampus in consolidation of autobiographical events is time dependent or independent has been controversial. In this study, we investigated the effect of memory remoteness (factor1: recent and remote) and emotional valence (factor2: positive and negative) on neural correlates underlying autobiographical memory by using functional magnetic resonance imaging (fMRI) technique. Although all four conditions activated some common regions known as "core" regions in autobiographical memory retrieval, there are some other regions showing significantly different activation for recent versus remote and positive versus negative memories. In particular, we found that bilateral hippocampal regions were activated in the four conditions regardless of memory remoteness and emotional valence. Thus, our study confirmed some findings of previous studies and provided further evidence to support the multi-trace theory which believes that the role of hippocampus involved in autobiographical memory retrieval is time-independent and permanent in memory consolidation.

With the accumulation of knowledge for the intimate molecular mechanisms governing the processes inside the living cells in the later years, the ability to characterize the performance of elementary genetic circuits and parts at the single-cell level is becoming of crucial importance. Biological science is arriving to the point where it can develop hypothesis for the action of each molecule participating in the biochemical reactions and need proper techniques to test those hypothesis. Microfluidics is emerging as the technology that combined with high-magnification microscopy will allow for the long-term single-cell level observation of bacterial physiology. In this study we design, build and characterize the gene dynamics of genetic circuits as one of the basic parts governing programmed cell behavior. We use E. coli as model organism and grow it in microfluidics chips, which we observe with epifluorescence microscopy. One of the most invaluable segments of this technology is the consequent image processing, since it allows for the automated analysis of vast amount of single-cell observation and the fast and easy derivation of conclusions based on that data. Specifically, we are interested in promoter activity as function of time. We expect it to be oscillatory and for that we use GFP (green fluorescent protein) as a reporter in our genetic circuits. In this paper, an automated framework for single-cell tracking in phase-contrast microscopy is developed, combining 2D segmentation of cell time frames and graph-based reconstruction of their spatiotemporal evolution with fast tracking of the associated fluorescence signal. The results obtained on the investigated biological database are presented and discussed.

Western blotting electrophoretic sequencing is an analytical technique widely used in Functional Proteomics to detect, recognize and quantify specific labelled proteins in biological samples. A commonly used label for western blotting is Enhanced ChemiLuminescence (ECL) reagents based on fluorescent light emission of Luminol at 425nm. Film emulsion is the conventional detection medium, but is characterized by non-linear response and limited dynamic range. Several western blotting digital imaging systems have being developed, mainly based on the use of cooled Charge Coupled Devices (CCDs) and single avalanche diodes that address these issues. Even so these systems present key drawbacks, such as a low frame rate and require operation at low temperature. Direct optical detection using Complementary Metal Oxide Semiconductor (CMOS) Active Pixel Sensors (APS)could represent a suitable digital alternative for this application. In this paper the authors demonstrate the viability of direct chemiluminescent light detection in western blotting electrophoresis using a CMOS APS at room temperature. Furthermore, in recent years, improvements in fabrication techniques have made available reliable processes for very large imagers, which can be now scaled up to wafer size, allowing direct contact imaging of full size western blotting samples. We propose using a novel wafer scale APS (12.8 cm×13.2 cm), with an array architecture using two different pixel geometries that can deliver an inherently low noise and high dynamic range image at the same time representing a dramatic improvement with respect to the current western blotting imaging systems.

The proposed macroscopic optical histopathology includes a broad-band light source which is selected to illuminate the tissue glass slide of suspicious pathology, and a hyperspectral camera that captures all wavelength bands from 450 to 950 nm. The system has been trained to classify each histologic slide based on predetermined pathology with light having a wavelength within a predetermined range of wavelengths. This technology is able to capture both the spatial and spectral data of tissue. Highly metastatic human head and neck cancer cells were transplanted to nude mice. After 2- 3 weeks, the mice were euthanized and the lymph nodes and lung tissues were sent to pathology. The metastatic cancer is studied in lymph nodes and lungs. The pathological slides were imaged using the hyperspectral camera. The results of the proposed method were compared to the pathologic report. Using hyperspectral images, a library of spectral signatures for different tissues was created. The high-dimensional data were classified using a support vector machine (SVM). The spectra are extracted in cancerous and non-cancerous tissues in lymph nodes and lung tissues. The spectral dimension is used as the input of SVM. Twelve glasses are employed for training and evaluation. The leave-one-out cross-validation method is used in the study. After training, the proposed SVM method can detect the metastatic cancer in lung histologic slides with the specificity of 97.7% and the sensitivity of 92.6%, and in lymph node slides with the specificity of 98.3% and the sensitivity of 96.2%. This method may be able to help pathologists to evaluate many histologic slides in a short time.

Functional near infrared spectroscopy (fNIRS) is an optical technique measuring hemoglobin oxygenation and deoxygenation concentrations of the brain cortex with higher temporal resolution than current alternative techniques. The high temporal resolution enables collecting abundant brain functional information. However, the information collected by fNIRS is correlated and mixed with a variety of physiological signals. Due to the mixture effect, activation detection is one of challenges in fNIRS based studies of the brain functional activities. To achieve a better detection of activated brain regions from the complicated information measures, we present a multi-scale analysis method based on a wavelet coherence measure. In particular, the paradigm of an experiment is used as the reference signal. The coherence of the signal with data measured by fNIRS at each channel is calculated and summed up to evaluate the activation level. Experiments on simulated and real data have demonstrated that the proposed method is efficient and effective to detect activated brain regions covered by the fNIRS probe.

Fluorescence molecular tomography (FMT) can three-dimensionally resolve molecular activities in in vivo small animal through the reconstruction of the distribution of fluorescent probes. Due to large number of unknowns and limited measurements from the surfaces of small animals, the FMT problem is often ill-posed and ill-conditioned. Though various L2-norm regularizations can make the solution stable, they usually make the solution over-smoothed. During the early stages of tumor detection, fluorescent sources that indicate the distribution of tumors are usually small and sparse, which can be regarded as a type of a priori information. L1-norm regularizations have been incorporated to promote the sparsity of optical tomographic problems. In this paper, an efficient method with the L1-norm regularization based on coordinate descent is proposed to solve the FMT problem with extremely limited measurements. The proposed method minimizes the objective by solving a sequence of scalar minimization subproblems in multi-variable minimization. Each subproblem improves the estimate of the solution via minimizing along a determined coordinate with all other coordinates fixed. This algorithm first updates the coordinate that makes the energy decrease the most. Non-existence of matrix-vector multiplication in the iteration process makes the proposed algorithm time-efficient. To evaluate this method, we compare it to the iterated-shrinkage-based algorithm with L1-norm regularization in numerical experiments. The proposed algorithm is able to obtain satisfactory reconstruction results even when the measurements are very limited. Besides, the proposed algorithm is about two orders of magnitude faster than the iterated-shrinkage-based algorithm, which enables the proposed algorithm into practical applications.

Tomographic bioluminescence imaging (TBI), with visible light emission in living organisms, is an effective way of molecular imaging, which allows for the study of ongoing tumor biological processes in vivo and non-invasively. This newly developed technology enables three-dimensional accuracy localization and quantitative analysis of the target tumor cells in small animal via reconstructing the images acquired by the high-resolution imaging system. Due to the difficulty of reconstruction, which is often referred to an ill-posed inverse problem, continuous efforts are still made to find more practical and efficient approaches. In this paper, an iteratively re-weighted minimization (IRM) has been applied to reconstruct the entire source distribution, which is known as sparse signals, inside the target tissue with the limited outgoing photon density on its boundary. By introducing a weight function into the objective function, we convert the l_p norm problem into a more simple form of l₂ norm to reduce the computational complexity. The weight function is updated in each iterative step to compute the final optimal solution more efficiently. This method is proved to be robust to different parameters, and mouse experiments are conducted to validate the feasibility of IRM approach, which is also reliable at whole-body imaging.

Cells exhibit the ability to sense and respond to local mechanical stimuli, leading to changes in function. This capability, referred to as mechanotransduction, is essential to normal tissue function, but the exact mechanisms by which cells sense local forces (strain, shear, compression and vibration) remain unclear. Recent studies in small animals and humans indicate that the frequency of cyclic mechanical stimuli is important, with physiological responses observed for stimuli ranging between 1 and 90 Hz. To better understand the cellular and molecular mechanisms underlying mechanotransduction, it will be important to observe cells in real time, using optical microscopy during high-frequency mechanical stimulation. We have developed a motion-control platform that can produce sinusoidal vibration of live cells during simultaneous high-speed microscopy and fluorimetry, at frequencies up to 100 Hz with peak acceleration up to 9.8 m s^-2. The platform is driven by a voice coil and acceleration is measured with an accelerometer (Dytran 7521A1). The motion waveform was verified by high-speed imaging, using a digital camera (Casio EX-F1) operating at 1200 frames s^-1 attached to an inverted microscope (Nikon Diaphot). When operating at 45 Hz and 2.94 m s^-2 peak acceleration, the observed motion waveform exhibited sinusoidal behaviour, with measured peak-to-peak amplitude of 72 μm. Cultured osteoblast-like cells (UMR-106) were subjected to 2.94 m s^-2 vibration at 45 Hz and remained attached and viable. This device provides - for the first time - the capability to mechanically stimulate living cells while simultaneously observing responses with optical microscopy.

We analyze μ-CT tomographic images of human trabecular bone in vitro. We consider a sample consisting of 201 bone specimens harvested from six different skeletal sites with bone fraction in the range BV/TV ε [0.04, 0.075]. Using the local characterization of the bone trabecular network given by isotropic and anisotropic scaling indices, we apply classification algorithms in order to reveal structural similarities in the sample. The classification procedures based on isotropic and anisotropic scaling indices lead to different clustering solutions. This comparison helps revealing interesting site specific structural features connected to the intrinsic anisotropy of the trabecular network.

Bone microarchitecture is the predictor of bone quality or bone disease. It can only be measured on a bone biopsy, which is invasive and not available for all clinical situations. Texture analysis on radiographs is a common way to investigate bone microarchitecture. But relationships between three-dimension histomorphometric parameters and two-dimension texture parameters are not always well known, with poor results. The aim of this paper is twofold : to study one classical parameter namely the fractal dimension which is easily computed on the 2D binary texture and to explore its relationships with the microarchitecture. We performed several experiments in order to check from ground truth the different possible values and their possible explanations. The results show great variations of the fractal dimension according to the size of the window and its location. These variations can be explained both by a misuse of the algorithm and by the number of trabecular and their characteristics inside the window where the fractal dimension is computed. This study also shows a specific interest to work with dual fractal dimension of the bone-spongious tissues.

According to Wolff's law bone remodels in response to the mechanical stresses it experiences so as to produce a minimal-weight structure that is adapted to its applied stresses. Consequently, the inner bone structure should show signs of adaptation to external forces acting on the bone. To test this paradigm, we investigate the relations between bone volume and structure for the trabecular bone using 3D μCT images taken from two different sites in the femur in vitro, namely from the femoral neck (88 specimens) and femoral trochanter (126 specimens). We determine the local structure of the trabecular network as well as its alignment with the direction of the external force acting on the bone by calculating isotropic (α) and anisotropic scaling indices (αz). Comparing global structure measures derived from the scaling indices (mean, variance) with the bone mass (BV/TV) we find that all correlations obey very accurately power laws with scaling exponents of 0.48 and 0.45 (<α>), -1.45 and -1.59 (var(μz)), 0.50 and 0.44 (<α>) and -1,47 and -1.32 (var(μz)) (neck and trochanter respectively). Thus, the relations for the isotropic scaling indices turn out to be siteindependent, albeit the mechanical stress to which the femoral neck is exposed is much larger than that for the trochanter. We find, however, differences in the degree of alignment of the trabeculae as reflected by the moments of the distribution of the anisotropic scaling indices. In summary, the mass-structure scaling relations of the bone probes taken from the two different sites of the femur show surprisingly small variations. Thus, a naïve interpretation of Wolff's law may not universally valid.

The squamous cell carcinomas of the upper aero-digestive tract represent about ten percent of cancers. External radiation therapy leads to esthetic and functional consequences, and to a decrease of the bone mechanical abilities. For these patients, the oral prosthetic rehabilitation, including possibilities of dental implant placement, is difficult. The effects of radiotherapy on bone microarchitecture parameters are not well known. Thus, the purpose of this study is to assess the effects of external radiation on bone micro architecture in an experimental model of 25 rats using micro CT. 15 rats were irradiated on the hind limbs by a single dose of 20 Grays, and 10 rats were non irradiated. Images of irradiated and healthy bone were compared. Bone microarchitecture parameters (including trabecular thickness, trabecular number, trabecular separation, connectivity density and tissue and bone volume) between irradiated and non-irradiated bones were calculated and compared using a Mann and Whitney test. After 7 and 12 weeks, images of irradiated and healthy bone are different. Differences on the irradiated and the healthy bone populations exhibit a statistical significance. Trabecular number, connectivity density and closed porosity are less important on irradiated bone. Trabecular thickness and separation increase for irradiated bone. These parameters indicate a decrease of irradiated bone properties. Finally, the external irradiation induces changes on the bone micro architecture. This knowledge is of prime importance for better oral prosthetic rehabilitation, including implant placement.

Failure of the spine's structural integrity from metastatic disease can lead to both pain and neurologic deficit. Fractures that require treatment occur in over 30% of bony metastases. Our objective is to use computed tomography (CT) in conjunction with analytic techniques that have been previously developed to predict fracture risk in cancer patients with metastatic disease to the spine. Current clinical practice for cancer patients with spine metastasis often requires an empirical decision regarding spinal reconstructive surgery. Early image-based software systems used for CT analysis are time consuming and poorly suited for clinical application. The Biomedical Image Resource (BIR) at Mayo Clinic, Rochester has developed an image analysis computer program that calculates from CT scans, the residual load-bearing capacity in a vertebra with metastatic cancer. The Spine Cancer Assessment (SCA) program is built on a platform designed for clinical practice, with a workflow format that allows for rapid selection of patient CT exams, followed by guided image analysis tasks, resulting in a fracture risk report. The analysis features allow the surgeon to quickly isolate a single vertebra and obtain an immediate pre-surgical multiple parallel section composite beam fracture risk analysis based on algorithms developed at Mayo Clinic. The analysis software is undergoing clinical validation studies. We expect this approach will facilitate patient management and utilization of reliable guidelines for selecting among various treatment option based on fracture risk.

A semi-automated method for generating hyperpolarized helium-3 (³He) measurements of individual slice (2D) or whole lung (3D) gas distribution was developed. ³He MRI functional images were segmented using two-dimensional (2D) and three-dimensional (3D) hierarchical K-means clustering of the ³He MRI signal and in addition a seeded region-growing algorithm was employed for segmentation of the ¹H MRI thoracic cavity volume. ³He MRI pulmonary function measurements were generated following two-dimensional landmark-based non-rigid registration of the ³He and ¹H pulmonary images. We applied this method to MRI of healthy subjects and subjects with chronic obstructive lung disease (COPD). The results of hierarchical K-means 2D and 3D segmentation were compared to an expert observer's manual segmentation results using linear regression, Pearson correlations and the Dice similarity coefficient. 2D hierarchical K-means segmentation of ventilation volume (VV) and ventilation defect volume (VDV) was strongly and significantly correlated with manual measurements (VV: r=0.98, p<.0001; VDV: r=0.97, p<.0001) and mean Dice coefficients were greater than 92% for all subjects. 3D hierarchical K-means segmentation of VV and VDV was also strongly and significantly correlated with manual measurements (VV: r=0.98, p<.0001; VDV: r=0.64, p<.0001) and the mean Dice coefficients were greater than 91% for all subjects. Both 2D and 3D semi-automated segmentation of ³He MRI gas distribution provides a way to generate novel pulmonary function measurements.

Although 3He MRI permits compelling visualization of the pulmonary air spaces, quantitation of absolute ventilation is difficult due to confounds such as field inhomogeneity and relative intensity differences between image acquisition; the latter complicating longitudinal investigations of ventilation variation with respiratory alterations. To address these potential difficulties, we present a 4-D segmentation and normalization approach for intra-subject quantitative analysis of lung hyperpolarized 3He MRI. After normalization, which combines bias correction and relative intensity scaling between longitudinal data, partitioning of the lung volume time series is performed by iterating between modeling of the combined intensity histogram as a Gaussian mixture model and modulating the spatial heterogeneity tissue class assignments through Markov random field modeling. Evaluation of the algorithm was retrospectively applied to a cohort of 10 asthmatics between 19-25 years old in which spirometry and 3He MR ventilation images were acquired both before and after respiratory exacerbation by a bronchoconstricting agent (methacholine). Acquisition was repeated under the same conditions from 7 to 467 days (mean ± standard deviation: 185 ± 37.2) later. Several techniques were evaluated for matching intensities between the pre and post-methacholine images with the 95th percentile value histogram matching demonstrating superior correlations with spirometry measures. Subsequent analysis evaluated segmentation parameters for assessing ventilation change in this cohort. Current findings also support previous research that areas of poor ventilation in response to bronchoconstriction are relatively consistent over time.

Radiation induced pulmonary diseases can change the tissue material properties of lung parenchyma and the mechanics of the respiratory system. Recent advances in multi-detector-row CT (MDCT), 4DCT respiratory gating methods, and image processing techniques enable us to follow and measure those changes noninvasively during radiation therapy at a regional level. This study compares the 4DCT based ventilation measurement with the results from hyperpolarized helium-3 MR using the cumulative distribution function maps and the relative overlap (RO) statistic. We show that the similarity between the two measurements increases as the increase of the B-Spline grid spacing and Laplacian weighting which result a smoother ventilation map. The best similarity is found with weighting of 0.5 for linear elasticity and B-Spline grid spacing of 32 mm. Future work is to improve the lung image registration algorithm by incorporating hyperpolarized helium-3 MR information so as to improve its physiological modeling of the lung tissue deformation.

The accuracy of optical flow estimation algorithms has been improving steadily by refining the objective function which should be optimized. A novel energy function for computing optical flow from volumetric X-ray CT images is presented. One advantage of the optical flow framework is the possibility to enforce physical constraints on the numerical solutions. The physical constraints which have been included here are: brightness constancy, gradient constancy, continuity equation based on mass conservation, and discontinuity-preserving spatio-temporal smoothness. The method has been evaluated on POPI-model and the evaluation demonstrates that the method results in significantly better accuracy than previous optical flow techniques for estimation of deformable lung motion.

The treatment plan evaluation for lung cancer patients involves pre-treatment and post-treatment volume CT imaging of the lung. However, treatment of the tumor volume lung results in structural changes to the lung during the course of treatment. In order to register the pre-treatment volume to post-treatment volume, there is a need to find robust and homologous features which are not affected by the radiation treatment along with a smooth deformation field. Since airways are well-distributed in the entire lung, in this paper, we propose use of airway tree bifurcations for registration of the pre-treatment volume to the post-treatment volume. A dedicated and automated algorithm has been developed that finds corresponding airway bifurcations in both images. To derive the 3-D deformation field, a B-spline transformation model guided by mutual information similarity metric was used to guarantee the smoothness of the transformation while combining global information from bifurcation points. Therefore, the approach combines both global statistical intensity information with local image feature information. Since during normal breathing, the lung undergoes large nonlinear deformations, it is expected that the proposed method would also be applicable to large deformation registration between maximum inhale and maximum exhale images in the same subject. The method has been evaluated by registering 3-D CT volumes at maximum exhale data to all the other temporal volumes in the POPI-model data.

MOTIVATION: The lobes of the lungs slide relative to each other during breathing. Quantifying lobar sliding can aid in better understanding lung function, better modeling of lung dynamics, and a better understanding of the limits of image registration performance near fissures. We have developed a method to estimate lobar sliding in the lung from image registration of CT scans. METHODS: Six human lungs were analyzed using CT scans spanning functional residual capacity (FRC) to total lung capacity (TLC). The lung lobes were segmented and registered on a lobe-by-lobe basis. The displacement fields from the independent lobe registrations were then combined into a single image. This technique allows for displacement discontinuity at lobar boundaries. The displacement field was then analyzed as a continuum by forming finite elements from the voxel grid of the FRC image. Elements at a discontinuity will appear to have undergone significantly elevated 'shear stretch' compared to those within the parenchyma. Shear stretch is shown to be a good measure of sliding magnitude in this context. RESULTS: The sliding map clearly delineated the fissures of the lung. The fissure between the right upper and right lower lobes showed the greatest sliding in all subjects while the fissure between the right upper and right middle lobe showed the least sliding.

Dual energy CT imaging is expected to play a major role in the diagnostic arena as it provides material decomposition on an elemental basis. The purpose of this work is to investigate the use of dual energy micro-CT for the estimation of vascular, tissue, and air fractions in rodent lungs using a post-reconstruction three-material decomposition method. We have tested our method using both simulations and experimental work. Using simulations, we have estimated the accuracy limits of the decomposition for realistic micro-CT noise levels. Next, we performed experiments involving ex vivo lung imaging in which intact lungs were carefully removed from the thorax, were injected with an iodine-based contrast agent and inflated with air at different volume levels. Finally, we performed in vivo imaging studies in (n=5) C57BL/6 mice using fast prospective respiratory gating in endinspiration and end-expiration for three different levels of positive end-expiratory pressure (PEEP). Prior to imaging, mice were injected with a liposomal blood pool contrast agent. The mean accuracy values were for Air (95.5%), Blood (96%), and Tissue (92.4%). The absolute accuracy in determining all fraction materials was 94.6%. The minimum difference that we could detect in material fractions was 15%. As expected, an increase in PEEP levels for the living mouse resulted in statistically significant increases in air fractions at end-expiration, but no significant changes in end-inspiration. Our method has applicability in preclinical pulmonary studies where various physiological changes can occur as a result of genetic changes, lung disease, or drug effects.

Pulmonary hypertension is a common cause of death among patients with sickle cell disease. This study investigates the use of pulmonary vein analysis to assist the diagnosis of pulmonary hypertension non-invasively with CT-Angiography images. The characterization of the pulmonary veins from CT presents two main challenges. Firstly, the number of pulmonary veins is unknown a priori and secondly, the contrast material is degraded when reaching the pulmonary veins, making the edges of these vessels to appear faint. Each image is first denoised and a fast marching approach is used to segment the left atrium and pulmonary veins. Afterward, a geodesic active contour is employed to isolate the left atrium. A thinning technique is then used to extract the skeleton of the atrium and the veins. The locations of the pulmonary veins ostia are determined by the intersection of the skeleton and the contour of the atrium. The diameters of the pulmonary veins are measured in each vein at fixed distances from the corresponding ostium, and for each distance, the sum of the diameters of all the veins is computed. These indicators are shown to be significantly larger in sickle-cell patients with pulmonary hypertension as compared to controls (p-values < 0.01).

The signal curves in perfusion dynamic contrast enhanced MRI (DCE-MRI) of cancerous breast tissue reveal valuable information about tumor angiogenesis. Pathological studies have illustrated that breast tumors consist of different subregions, especially with more homogeneous properties during their growth. Differences should be identifiable in DCEMRI signal curves if the characteristics of these sub-regions are related to the perfusion and angiogenesis. We introduce a stepwise clustering method which in a first step uses a new similarity measure. The new similarity measure (PM) compares how parallel washout phases of two curves are. To distinguish the starting point of the washout phase, a linear regression method is partially fitted to the curves. In the next step, the minimum signal value of the washout phase is normalized to zero. Finally, PM is calculated according to maximal variation among the point wise differences during washout phases. In the second step of clustering the groups of signal curves with parallel washout are clustered using Euclidean distance. The introduced method is evaluated on 15 DCE-MRI breast datasets with different types of breast tumors. The use of our new heterogeneity analysis is feasible in single patient examination and improves breast MR diagnostics.

Microwave imaging for biomedical applications, especially for early detection of breast cancer and effective treatment monitoring, has attracted increasing interest in last several decades. This fact is due to the high contrast between the dielectric properties of the normal and malignant breast tissues at microwave frequencies. The available range of dielectric properties for different soft tissue can provide important functional information about tissue health. Nonetheless, one of the limiting weaknesses of microwave imaging is that unlike conventional modalities, such as X-ray CT or MRI, it inherently cannot provide high-resolution images. The conventional modalities can produce highly resolved anatomical information but often cannot provide the functional information required for diagnoses. Previously, we have developed a regularization strategy that can incorporate prior anatomical information from MR or other sources and use it in a way to refine the resolution of the microwave images, while also retaining the functional nature of the reconstructed property values. In the present work, we extend the use of prior structural information in microwave imaging from 2D to 3D. This extra dimension adds a significant layer of complexity to the entire image reconstruction procedure. In this paper, several challenges with respect to the 3D microwave imaging will be discussed and the results of a series of 3D simulation and phantom experiments with prior structural information will be studied.

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is used to image high-risk patients for breast cancer because of its higher sensitivity to tumors (approaching 100%) than traditional x-ray mammography. We focus on Near Infrared Spectroscopy (NIRS) as an emerging functional and molecular imaging technique that non-invasively quantifies optical properties of total hemoglobin, oxygen saturation, water content, scattering, and lipid concentration to increase the relatively low specificity of DCE-MRI. Our optical imaging system combines six frequency domain wavelengths, measured using PMT detectors with three continuous wave wavelengths measured using CCD/spectrometers. We present methods on combining the synergistic attributes of DCE-MR and NIRS for in-vivo imaging of breast cancer in three dimensions using a custom optical MR breast coil and diffusion based light modeling software, NIRFAST. We present results from phantom studies, healthy subjects, and breast cancer patients. Preliminary results show contrast recovery within 10% in phantoms and spatial resolution less than 5mm. Images from healthy subjects were recovered with properties similar to literature values and previous studies. Patient images have shown elevated total hemoglobin values and water fraction, agreeing with histology and previous results. The additional information gained from NIRS may improve the ability to distinguish between malignant and benign lesions during MR imaging. These dual modality instruments will provide complex anatomical and molecular prognostic information, and may decrease the number of biopsies, thereby improving patient care.

Most medical imaging is inherently three-dimensional (3D) but for validation of pathological findings, histopathology is commonly used and typically histopathology images are acquired as twodimensional slices with quantitative analysis performed in a single dimension. Histopathology is invasive, labour-intensive, and the analysis cannot be performed in real time, yet it remains the gold standard for the pathological diagnosis and validation of clinical or radiological diagnoses of disease. A major goal worldwide is to improve medical imaging resolution, sensitivity and specificity to better guide therapy and biopsy and to one day delay or replace biopsy. A key limitation however is the lack of tools to directly compare 3D macroscopic imaging acquired in patients with histopathology findings, typically provided in a single dimension (1D) or in two dimensions (2D). To directly address this, we developed methods for 2D histology slice visualization/registration to generate 3D volumes and quantified tissue components in the 3D volume for direct comparison to volumetric micro-CT and clinical CT. We used the elastase-instilled mouse emphysema lung model to evaluate our methods with murine lungs sectioned (5 μm thickness/10 μm gap) and digitized with 2μm in-plane resolution. 3D volumes were generated for wildtype and elastase mouse lung sections after semi-automated registration of all tissue slices. The 1D mean linear intercept (Lm) for wildtype (WT) (47.1 μm ± 9.8 μm) and elastase mouse lung (64.5 μm ± 14.0 μm) was significantly different (p<.001). We also generated 3D measurements based on tissue and airspace morphometry from the 3D volumes and all of these were significantly different (p<.0001) when comparing elastase and WT mouse lung. The ratio of the airspace-to-lung volume for the entire lung volume was also significantly and strongly correlated with L_m.

Support Vector Machine (SVM) is an accurate pattern recognition method which has been widely used in functional MRI (fMRI) data classification. Voxel selection is a very important part in classification. In general, voxel selection is based on brain regions associated with activation caused by different experiment conditions or stimulations. However, negative blood oxygenation level-dependent responses (deactivation) which have also been found in humans or animals contribute to the classification of different cognitive tasks. Different from traditional studies which focused merely on the activation voxel selection methods, our aim is to investigate the deactivation voxel selection methods in the classification of fMRI data using SVM. In this study, three different voxel selection methods (deactivation, activation, the combination of deactivation and activation) are applied to decide which voxel is included in SVM classifier with linear kernel in classifying 4-category objects on fMRI data. The average accuracies of deactivation classification were 73.36%(house vs. face), 60.34%(house vs. car), 60.94%(house vs. cat), 71.43%(face vs. car), 63.17%(face vs. cat) and 61.61%(car vs. cat). The classification results of deactivation were significantly above the chance level which implies the deactivation is informative. The accuracies of combination of activation and deactivation method were close to that of activation method, and it was even better for some representative subjects. These results suggest deactivation provides useful information in the object category classification on fMRI data and the method of voxel selection based on both activation and deactivation will be a significant method in classification in the future.

Mild cognitive impairment (MCI) was recognized as the prodromal stage of Alzheimer's disease (AD). Recent researches have shown that cognitive and memory decline in AD patients is coupled with losses of small-world attributes. However, few studies pay attention to the characteristics of the whole brain networks in MCI patients. In the present study, we investigated the topological properties of the whole brain networks utilizing graph theoretical approaches in 16 MCI patients, compared with 18 age-matched healthy subjects as a control. Both MCI patients and normal controls showed small-world architectures, with large clustering coefficients and short characteristic path lengths. We detected significantly longer characteristic path length in MCI patients compared with normal controls at the low sparsity. The longer characteristic path lengths in MCI indicated disrupted information processing among distant brain regions. Compared with normal controls, MCI patients showed decreased nodal centrality in the brain areas of the angular gyrus, heschl gyrus, hippocampus and superior parietal gyrus, while increased nodal centrality in the calcarine, inferior occipital gyrus and superior frontal gyrus. These changes in nodal centrality suggested a widespread rewiring in MCI patients, which may be an integrated reflection of reorganization of the brain networks accompanied with the cognitive decline. Our findings may be helpful for further understanding the pathological mechanisms of MCI.

Emotional tasks may result in a strong blood oxygen level-dependent (BOLD) signal in the amygdala in 5- HTTLRP short-allele. Reduced anterior cingulate cortex (ACC)-amygdala connectivity in short-allele provides a potential mechanistic account for the observed increase in amygdala activity. In our study, fearful and threatening facial expressions were presented to two groups of 12 subjects with long- and short-allele carriers. The BOLD signals of the left amygdala of each group were averaged to increase the signal-to-noise ratio. A Bayesian approach was used to estimate the model parameters to elucidate the underlying hemodynamic mechanism. Our results showed a positive BOLD signal in the left amygdala for short-allele individuals, and a negative BOLD signal in the same region for long-allele individuals. This is due to the fact that short-allele is associated with lower availability of serotonin transporter (5-HTT) and this leads to an increase of serotonin (5-HT) concentration in the cACC-amygdala synapse.

Acute stroke is a major cause for death and disability among adults in the western hemisphere. Time-resolved perfusion-weighted (PWI) and diffusion-weighted (DWI) MR datasets are typically used for the estimation of tissue-at-risk, which is an important variable for acute stroke therapy decision-making. Although several parameters, which can be estimated based on PWI concentration curves, have been proposed for tissue-at-risk definition in the past, the time-to-peak (TTP) or time-to-max (Tmax) parameter is used most frequently in recent trials. Unfortunately, there is no clear consensus which method should be used for estimation of Tmax or TTP maps. Consequently, tissue-at-risk estimations and following treatment decision might vary considerably with the method used. In this work, 5 PWI datasets of acute stroke patients were used to calculate TTP or Tmax maps using 10 different estimation techniques. The resulting maps were segmented using a typical threshold of +4s and the corresponding PWI-lesions were calculated. The first results suggest that the TTP or Tmax method used has a major impact on the resulting tissue-at-risk volume. Numerically, the calculated volumes differed up to a factor of 3. In general, the deconvolution-based Tmax techniques estimate the ischemic penumbra rather smaller compared to direct TTP based techniques. In conclusion, the comparison of different methods for TTP or Tmax estimation revealed high variations regarding the resulting tissue-at-risk volume, which might lead to different therapy decisions. Therefore, a consensus how TTP or Tmax maps should be calculated seems necessary.

Susceptibility weighted imaging (SWI) takes advantage of the local variation in susceptibility between different tissues to enable highly detailed visualization of the cerebral venous system and sensitive detection of intracranial hemorrhages. Thus, it has been increasingly used in magnetic resonance imaging studies of traumatic brain injury as well as other intracranial pathologies. In SWI, magnitude information is combined with phase information to enhance the susceptibility induced image contrast. Because of global susceptibility variations across the image, the rate of phase accumulation varies widely across the image resulting in phase wrapping artifacts that interfere with the local assessment of phase variation. Homodyne filtering is a common approach to eliminate this global phase variation. However, filter size requires careful selection in order to preserve image contrast and avoid errors resulting from residual phase wraps. An alternative approach is to apply phase unwrapping prior to high pass filtering. A suitable phase unwrapping algorithm guarantees no residual phase wraps but additional computational steps are required. In this work, we quantitatively evaluate these two phase processing approaches on both simulated and real data using different filters and cutoff frequencies. Our analysis leads to an improved understanding of the relationship between phase wraps, susceptibility effects, and acquisition parameters. Although homodyne filtering approaches are faster and more straightforward, phase unwrapping approaches perform more accurately in a wider variety of acquisition scenarios.

Magnetic resonance diffusion tensor imaging (DTI) is a kind of effective measure to do non-invasive investigation on brain fiber structure at present. Studies of fiber tracking based on DTI showed that there was structural connection of white matter fiber among the nodes of resting-state functional network, denoting that the connection of white matter was the basis of gray matter regions in functional network. Nevertheless, relationship between these structure connectivity regions and functional network has not been clearly indicated. Moreover, research of fMRI found that activation of default mode network (DMN) in Alzheimer's disease (AD) was significantly descended, especially in hippocampus and posterior cingulated cortex (PCC). The relationship between this change of DMN activity and structural connection among functional networks needs further research. In this study, fast marching tractography (FMT) algorithm was adopted to quantitative calculate fiber connectivity value between regions, and hippocampus and PCC which were two important regions in DMN related with AD were selected to compute white matter connection region between them in elderly normal control (NC) and AD patient. The fiber connectivity value was extracted to do the correlation analysis with activity intensity of DMN. Results showed that, between PCC and hippocampus of NC, there exited region with significant high connectivity value of white matter fiber whose performance has relatively strong correlation with the activity of DMN, while there was no significant white matter connection region between them for AD patient which might be related with reduced network activation in these two regions of AD.

It is widely accepted that complexity in the flow pattern at the anterior communicating artery (AComA) is associated with the high rate of aneurysm formation in that location observed in large studies. A previous computational hemodynamic study showed a possible association between high maximum intraaneurysmal wall shear stress (WSS) at the systolic peak with rupture in a cohort of AComA aneurysms. In another study it was observed a connection between location of aneurysm blebs and regions of high WSS in models where blebs were virtually removed. However, others reported associations between low WSS and either rupture or blister formation. The purpose of this work is to study associations between hemodynamic patterns and AComA aneurysm initiation by comparing hemodynamics in the aneurysm and the normal model where the aneurysm was computationally removed. Vascular models of both right and left circulation were independently reconstructed from three-dimensional rotational angiography images using deformable models, and fused using a surface merging algorithm. The geometric models were then used to generate high-quality volumetric finite element grids of tetrahedra with an advancing front technique. For each patient, the second anatomical model was created by digitally removing the aneurysm. It was iteratively achieved by applying a Laplacian smoothing filter and remeshing the surface. Finite element blood flow numerical simulations were performed. It was observed that aneurysms initiated in regions of high and moderate WSS in the counterpart normal models. Adjacent or close to those regions, low WSS portions of the arterial wall were not affected by the disease.

In recent nanotechnology development, iron-based magnetic nanoparticles (MNPs) have been used in several investigations on biomedical research for small animal experiments. Their important applications include targeted drug delivery for therapeutic purpose, contrast agent for magnetic resonance imaging, and hyperthermia treatment for tumors. These MNPs can be guided by an external magnetic field due to their physical characteristics of superparamagnetism. In a recent report, authors indicated that covalently bound recombinant tissue plasminogen activator (rtPA) to MNP (MNPrtPA) with preserved enzyme activity may be guided by a bar magnet and induce target thrombolysis in an embolic model in rats. Delivery of rtPA by binding the thrombolytic drug to MNPs will improve the possibility of the drug to be delivered under magnetic guidance and retained in a local targeted area in the circulation system. In this work, an ex vivo intravascular thrombolysis model was developed to study the impact of external magnetic field on the penetration of MNP-rtPA in the blood clot samples. The samples were then scanned by a micro CT system for quantification. Images of MNPs show strong contrast with their surrounding blood clot materials. The optimum drug loading was found when 0.5 mg/ml rtPA is conjugated with 10 mg SiO2-MNP where 98% drug was attached to the carrier with full retention of its thrombolytic activity. Effective thrombolysis with tPA bound to SiO2-MNP under magnetic guidance was demonstrated in our ex vivo model where substantial reduction in time for blood clot lysis was observed compared with control groups without magnetic field application.

Magnetic particle imaging (MPI) was introduced in 2005 and is one of the very few imaging methods capable of sensitivities that allow the term "molecular imaging" to be applied. Estimates of sensitivity allow nanograms of iron oxide nanoparticles to be imaged. MPI cyclically saturates the nanoparticles with an alternating magnetic field termed the drive field. The signal from the harmonics of the drive frequency is recorded. Localization is achieved by saturating the nanoparticles outside a "field free point." We present an alternative method of encoding the position of the magnetic nanoparticles. Signal is generated at the 2nd harmonic of the drive field only when a static magnetic field is present. Localization is achieved by placing a small static magnetic field gradient across the sample and the phase of the signal depends on the sign of the static field. The response of the nanoparticles at different static fields provides the localization. The localization can be modeled as a wavelet transform if the gradient is approximately linear. Smaller field gradients are required than in MPI. The sensitivity is potentially significantly higher than that of MPI; when minimum bandwidths are employed to achieve the maximum SNR, the SNR is 85% larger for new method. Variable resolution can be achieved. This is the first method capable of imaging the signal from a single harmonic independently of other harmonics. The new method has promise for low cost screening applications where only coarse localization might be required.

Tumor cell morphology is closely related to its invasiveness characteristics and migratory behaviors. An invasive tumor cell has a highly irregular shape, whereas a spherical cell is non-metastatic. Thus, quantitative analysis of cell features is crucial to determine tumor malignancy or to test the efficacy of anticancer treatment. We use phase-contrast microscopy to analyze single cell morphology and to monitor its change because it enables observation of long-term activity of living cells without photobleaching and phototoxicity, which is common in other fluorescence-labeled microscopy. Despite this advantage, there are image-level drawbacks to phase-contrast microscopy, such as local light effect and contrast interference ring, among others. Thus, we first applied a local filter to compensate for non-uniform illumination. Then, we used intensity distribution information to detect the cell boundary. In phase-contrast microscopy images, the cell normally appears as a dark region surrounded by a bright halo. As the halo artifact around the cell body is minimal and has an asymmetric diffusion pattern, we calculated the cross-sectional plane that intersected the center of each cell and was orthogonal to the first principal axis. Then, we extracted the dark cell region by level set. However, a dense population of cultured cells still rendered single-cell analysis difficult. Finally, we measured roundness and size to classify tumor cells into malignant and benign groups. We validated segmentation accuracy by comparing our findings with manually obtained results.

Aim: The use of conventional mirror images does not adequately guide surgeons on the correction of facial asymmetries. The purpose of this study was to evaluate the utility of an individualized atlas as a template for corrective surgeries for patients suffering from mandibular asymmetry. The patientspecific atlas is calculated from both the original asymmetric mandible and the mirror of the same mandible registered on the cranial base. Material and Method: Three patients with history of favorable clinical outcome of the correction of their mandibular asymmetry were chosen for this pilot study. CBCT were taken before and 6 weeks after corrective surgery using NewTom 3G. Each volume was mirrored and rigidly registered on the cranial base. Surface models for both the mandible and its registered mirror were used to compute an atlas using deformable fluid registration. Corrective surgery was simulated based of the resulting atlas. Differences between the virtual simulated outcome and the actual surgical outcome were computed using UNC SPHARM-PDM toolbox. Results: The detected differences between the virtual simulated outcome and the actual surgical outcome, as characterized in 6 degrees of freedom, were smaller than 2 mm of translation and 5 degrees of rotation. This indicates that the location of the synthesized template is similar to the desired clinical outcome. Conclusions: The construction of patient-specific atlases using non-rigid registration has the potential to optimize and increase the predictability of the outcome of craniofacial corrective surgeries for asymmetric patients.

Osteoporosis is a frequent skeletal disease characterised both by loss of bone mineral mass and deterioration of cancellous bone micro-architecture. It can be caused by mechanical disuse, estrogen deficiency or natural age-related resorption process. Numerical analysis of high-resolution images of the trabecular network is recognised as a powerful tool for assessment of structural characteristics. Using μCT images of 73 thoracic and 78 lumbar human vertebral specimens in vitro with isotropic resolution of 26μm we simulate bone atrophy as random resorption of bone surface voxels. Global morphological and topological characteristics provided by four Minkowski Functionals (MF) are calculated for two numerical resorption models with and without conservation of global topological connectivity of the trabecular network, which simulates different types of bone loss in osteoporosis, as it has been described in males and females. Diagnostic performance of morphological and topological characteristics as a function of relative bone loss is evaluated by a correlation analysis with respect to experimentally measured Maximum Compressive Strength (MCS). In both resorption models the second MF, which coincides with bone surface fraction BS/TV, demonstrates almost constant value of Pearson's correlation coefficient with respect to the relative bone loss ▵BV/TV. This morphological characteristic does not vary considerably under age-related random resorption and can be used for predicting bone strength in the elderly. The third and fourth MF demonstrate an increasing correlation coefficients with MCS after applying random bone surface thinning without preserving topological connectivity, what can be used for improvement of evaluation of the current state of the structure.

The current approach to evaluating cartilage degeneration at the knee joint requires visualization of the joint space on radiographic images where indirect cues such as joint space narrowing serve as markers for osteoarthritis. A recent novel approach to visualizing the knee cartilage matrix using phase contrast CT imaging (PCI-CT) was shown to allow direct examination of chondrocyte cell patterns and their subsequent correlation to osteoarthritis. This study aims to characterize chondrocyte cell patterns in the radial zone of the knee cartilage matrix in the presence and absence of osteoarthritic damage through both gray-level co-occurrence matrix (GLCM) derived texture features as well as Minkowski Functionals (MF). Thirteen GLCM and three MF texture features were extracted from 404 regions of interest (ROI) annotated on PCI images of healthy and osteoarthritic specimens of knee cartilage. These texture features were then used in a machine learning task to classify ROIs as healthy or osteoarthritic. A fuzzy k-nearest neighbor classifier was used and its performance was evaluated using the area under the ROC curve (AUC). The best classification performance was observed with the MF features 'perimeter' and 'Euler characteristic' and with GLCM correlation features (f3 and f13). With the experimental conditions used in this study, both Minkowski Functionals and GLCM achieved a high classification performance (AUC value of 0.97) in the task of distinguishing between health and osteoarthritic ROIs. These results show that such quantitative analysis of chondrocyte patterns in the knee cartilage matrix can distinguish between healthy and osteoarthritic tissue with high accuracy.

Kidney stones were induced in 5 rats by treating them with 1% ethylene glycol and 1% ammonium chloride through free drinking water for six weeks. The animals were anesthetized and imaged in vivo before the treatment at week 0, to obtain baseline data, then at weeks 2 and 6 to monitor the kidney stone formation. Micro-CT imaging was performed with x-ray tube voltage of 90 kV and a current of 40 mA. At week 2, kidney stone formation was observed. A micro-computed tomography methodology of estimating the volume and hydroxyapatite-equivalent mineral content of the kidney stone is presented. It determines the threshold CT number (390 HU) that separates the kidney stone from the tissue. The mean volume of the stones in the 10 kidneys significantly increased from 3.81±0.72 mm3 at week 2 to 23.96±9.12 mm³ at week 6 (p<0.05, r²=0.34). Measurement precision error was about 4%. This method allows analysis of the kidney stone formation to be carried out in vivo, with fewer experimental animals compared with other ex vivo methods, in which animals are sacrificed. It is precise, accurate, non-destructive, and could be used in pre-clinical research to study the formation of kidney stones in live small animals.

Targeted fluorescence imaging agents such as IntegriSense 680 can be used to label integrin α_vβ₃ expressed in tumor cells and to distinguish tumor from normal tissues. Coupled with endomicroscopy and image-guided intervention devices, fluorescence contrast captured from the fiber-optic imaging technique can be used in a Minimally Invasive Multimodality Image Guided (MIMIG) system for on-site peripheral lung cancer diagnosis. In this work, we propose an automatic quantification approach for IntegriSense-based fluorescence endomicroscopy image sequences. First, a sliding time-window is used to calculate the histogram of the frames at a given timepoint, also denoted as the IntegriSense signal. The intensity distributions of the endomicroscopy image sequences can be briefly classified into three groups: high, middle and low intensities, which might correspond to tumor, normal tissue, and background (air) tissues within the lungs, respectively. At a given time-point, the histogram calculated from the sliding time-window is fit with a Gaussian mixture model, and the average and standard deviation (std), as well as the weight of each Gaussian distribution can be identified. Finally, a threshold can be used to the weighting parameter of the high intensity group for tumor information detection. This algorithm can be used as an automatic tumor detection tool from IntegriSense-based endomicroscopy. In experiments, we validated the algorithm using 20 IntegriSense-based fluorescence endomicroscopy image sequences collected from 6 rabbit experiments, where VX2 tumor was implanted into the lung of each rabbit, and image-guided endomicroscopy was performed. The automatic classification results were compared with manual results, and high sensitivity and specificity were obtained.

In this study we present a computational method of CT examination classification into visual assessed emphysema severity. The visual severity categories ranged from 0 to 5 and were rated by an experienced radiologist. The six categories were none, trace, mild, moderate, severe and very severe. Lung segmentation was performed for every input image and all image features are extracted from the segmented lung only. We adopted a two-level feature representation method for the classification. Five gray level distribution statistics, six gray level co-occurrence matrix (GLCM), and eleven gray level run-length (GLRL) features were computed for each CT image depicted segment lung. Then we used wavelets decomposition to obtain the low- and high-frequency components of the input image, and again extract from the lung region six GLCM features and eleven GLRL features. Therefore our feature vector length is 56. The CT examinations were classified using the support vector machine (SVM) and k-nearest neighbors (KNN) and the traditional threshold (density mask) approach. The SVM classifier had the highest classification performance of all the methods with an overall sensitivity of 54.4% and a 69.6% sensitivity to discriminate "no" and "trace visually assessed emphysema. We believe this work may lead to an automated, objective method to categorically classify emphysema severity on CT exam.

Geometric analysis of the left atrium and pulmonary veins is important for studying reverse structural remodeling following cardiac ablation therapy. It has been shown that the left atrium decreases in volume and the pulmonary vein ostia decrease in diameter following ablation therapy. Most analysis techniques, however, require laborious manual tracing of image cross-sections. Pulmonary vein diameters are typically measured at the junction between the left atrium and pulmonary veins, called the pulmonary vein ostia, with manually drawn lines on volume renderings or on image cross-sections. In this work, we describe a technique for making semi-automatic measurements of the left atrium and pulmonary vein ostial diameters from high resolution CT scans and multi-phase datasets. The left atrium and pulmonary veins are segmented from a CT volume using a 3D volume approach and cut planes are interactively positioned to separate the pulmonary veins from the body of the left atrium. The cut plane is also used to compute the pulmonary vein ostial diameter. Validation experiments are presented which demonstrate the ability to repeatedly measure left atrial volume and pulmonary vein diameters from high resolution CT scans, as well as the feasibility of this approach for analyzing dynamic, multi-phase datasets. In the high resolution CT scans the left atrial volume measurements show high repeatability with approximately 4% intra-rater repeatability and 8% inter-rater repeatability. Intra- and inter-rater repeatability for pulmonary vein diameter measurements range from approximately 2 to 4 mm. For the multi-phase CT datasets, differences in left atrial volumes between a standard slice-by-slice approach and the proposed 3D volume approach are small, with percent differences on the order of 3% to 6%.

The real-time monitoring of different molecular interactions can be used as a lower cost tool for genetic diagnosis. The extraction of the hybridization signal allows the estimation of the association/dissociation constants, the affinity of biomolecular components (target/probe) that interact and then characterize their activities and functions. This extraction of the biological information is based on the analysis of images acquired by a CCD camera during the course of the experiment and a self-calibration of the data obtained. Until now, the processing of these images was post experimental and concerned different stages of analysis: the detection of spots region, spatiotemporal segmentation of areas of interaction and eventually the quantification of these areas using the kinetic response measured. The challenging issue is to continue to improve the automatic extraction of the interaction signal and develop a processing tool applied in real-time as the image acquisition progresses. The advantage of such treatment is to allow the prediction of the evolution of the interaction, especially in the case of genetic diagnosis. It may also detect any malfunction that may arise during the interaction and allow the experimenter to decide whether to continue or interrupt the experience. This paper proposes a new approach for the real-time analysis of the image data provided by the SPR. A self-calibration step allows the correction of microarray design flaws or of temporal artifacts. Once the data are normalized, 3D morphological operators are used to extract the binary mask that will allow detecting all regions of interest for dynamic segmentation. This segmentation is then used in a spatio-temporal classification to extract the effective signal within each detected spot. The resulting real-time analysis approach presents a great interest in genetic diagnosis applications.

Prostate cancer is the second common cancer among men worldwide and remains the second leading cancer-related cause of death in mature men. The disease can be cured if it is detected at early stage. This implies that prostate cancer detection at early stage is very critical for desirable treatment outcome. Conventional techniques of prostate cancer screening and detection, such as Digital Rectal Examination (DRE), Prostate-Specific Antigen (PSA) and Trans Rectal Ultra-Sonography (TRUS), are known to have low sensitivity and specificity. Elastography is an imaging technique that uses tissue stiffness as contrast mechanism. As the association between the degree of prostate tissue stiffness alteration and its pathology is well established, elastography can potentially detect prostate cancer with a high degree of sensitivity and specificity. In this paper, we present a novel elastography technique which, unlike other elastography techniques, does not require displacement data acquisition system. This technique requires the prostate's pre-compression and postcompression transrectal ultrasound images. The conceptual foundation of reconstructing the prostate's normal and pathological tissues elastic moduli is to determine these moduli such that the similarity between calculated and observed shape features of the post compression prostate image is maximized. Results indicate that this technique is highly accurate and robust.