Kernel Synthesis (KS) in CT is an advanced image processing technique that involves the conversion of an image acquired with one specific convolution kernel into an image that appears as if it were acquired with a different kernel. This process doesn't require raw sinogram or reconstruction algorithm to generate images. While kernel synthesis methods exhibit promise, they can introduce noise and artifacts during transformation, highlighting the importance of effective noise and artifact management in the input data before synthesis. In this work, we represent kernel synthesis as a deep neural network regularized inverse problem. We optimize the convolutional neural network (CNN) weights in a data-consistent manner where CNN acts as an implicit prior to regularize the solution. Since, the learned CNN priors are more generic than hand crafted priors (like sparsity and total-variation), they helps control artifacts while preserving the details in the image. Experimental results for a low-resolution kernel (STND) data to a high resolution kernel (LUNG) data conversion clearly indicates that the output images from the proposed method resemble closely with images reconstructed using high-resolution kernel (LUNG) while simultaneously reducing the noise and clutter.
Following the acquisition of images in CT, a crucial post-processing step involves orienting the volumetric image to align with standard viewing planes, facilitating the assessment of disease extent and other pathologies. However, manual alignment is not only time-consuming but can also pose challenges in achieving consistent standard plane views, particularly for lesser skilled technologists. Existing automated solutions, primarily based on registration techniques, encounter reduced accuracy in cases involving significant rotations, pediatric patients, and instances with pronounced pathological effects. This limitation arises due to their reliance on symmetry. In severe scenarios, registration-based methods can exacerbate image misalignment compared to the original input. To address these concerns, this study introduces a landmark-based automated image alignment method. This method presents three key advantages: robust alignment across diverse data variations, the capability to identify algorithm failures and gracefully terminate, and the ability to align images with different standard planes. The effectiveness of our method is showcased through a comparative evaluation with registration-based approaches. The evaluation employs a test dataset comprising various head cases across different age groups, reaffirming the effectiveness of our proposed method.
Dynamic Contrast Enhanced MRI (DCE-MRI) is being increasingly used as a method for studying the tumor
vasculature. It is also used as a biomarker to evaluate the response to anti-angiogenic therapies and the efficacy of a
therapy. The uptake of contrast in the tissue is analyzed using pharmacokinetic models for understanding the perfusion
characteristics and cell structure, which are indicative of tumor proliferation. However, in most of these 4D acquisitions
the time required for the complete scan are quite long as sufficient time must be allowed for the passage of contrast
medium from the vasculature to the tumor interstitium and subsequent extraction. Patient motion during such long scans
is one of the major challenges that hamper automated and robust quantification. A system that could automatically detect
if motion has occurred during the acquisition would be extremely beneficial. Patient motion observed during such 4D
acquisitions are often rapid shifts, probably due to involuntary actions such as coughing, sneezing, peristalsis, or jerk due
to discomfort. The detection of such abrupt motion would help to decide on a course of action for correction for motion
such as eliminating time frames affected by motion from analysis, or employing a registration algorithm, or even
considering the exam us unanalyzable. In this paper a new technique is proposed for effective detection of motion in 4D
medical scans by determination of the variation in the signal characteristics from multiple regions of interest across time.
This approach offers a robust, powerful, yet simple technique to detect motion.
KEYWORDS: Human-machine interfaces, Virtual reality, Visualization, 3D acquisition, Optical tracking, Mirrors, Error analysis, Medical imaging, 3D modeling, Data modeling
An exploration of techniques for developing intuitive, and efficient user interfaces for virtual reality systems.
Work seeks to understand which paradigms from the better-understood world of 2D user interfaces remain
viable within 3D environments. In order to establish this a new user interface was created that applied
various understood principles of interface design. A user study was then performed where it was compared
with an earlier interface for a series of medical visualization tasks.
As the imaging modalities used in medicine transition to increasingly three-dimensional data the question of
how best to interact with and analyze this data becomes ever more pressing. Immersive virtual reality
systems seem to hold promise in tackling this, but how individuals learn and interact in these environments is
not fully understood. Here we will attempt to show some methods in which user interaction in a virtual reality
environment can be visualized and how this can allow us to gain greater insight into the process of
interaction/learning in these systems. Also explored is the possibility of using this method to improve
understanding and management of ergonomic issues within an interface.
An established challenge in the field of image analysis has been the registration of images having a large initial
misalignment. For example in chemo and Radiation Therapy Planning (RTP), there is often a need to register an image
delineating a specific anatomy (usually in the surgery position) with that of a whole body image (obtained preoperatively).
In such a scenario, there is room for a large misalignment between the two images that are required to be
aligned. Large misalignments are traditionally handled in two ways: 1) Semi-automatically with a user initialization or 2)
With the help of the origin fields in the image header. The first approach is user dependant and the second method can be
used only if the two images are obtained from the same scanner with consistent origins. Our methodology extends a
typical registration framework by selecting components that are capable of searching a large parameter space without
settling on local optima. We have used an optimizer that is based on an Evolutionary Scheme along with an information
theory based similarity metric that can address these needs. The attempt in this study is to convert a large misalignment
problem to a small misalignment problem that can then be handled using application specific registration algorithms.
Further improvements along local areas can be obtained by subjecting the image to a non-rigid transformation. We have
successfully registered the following pairs of images without any user initialization: CTAC - simCT (neuro, lungs); MRPET/
CT (neuro, liver); T2-SPGR (neuro).
In this paper, we present a framework that one could use to set optimized parameter values, while performing
image registration using mutual information as a metric to be maximized. Our experiment details these steps
for the registration of X-ray Computer Tomography (CT) images with Positron Emission Tomography (PET)
images. Selection of different parameters that influence the mutual information between two images is crucial
for both accuracy and speed of registration. These implementation issues need to be handled in an orderly
fashion by designing experiments in their operating ranges. The conclusions from this study seem vital towards
obtaining allowable parameter range for a fusion software.
Volume rendering has high utility in visualization of segmented datasets. However, volume rendering of the segmented labels along with the original data causes undesirable intermixing/bleeding artifacts arising from interpolation at the sharp boundaries. This issue is further amplified in 3D textures based volume rendering due to the inaccessibility of the interpolation stage. We present an approach which helps minimize intermixing artifacts while maintaining the high performance of 3D texture based volume rendering - both of which are critical for intra-operative visualization. Our approach uses a 2D transfer function based classification scheme where label distinction is achieved through an encoding that generates unique gradient values for labels. This helps ensure that labelled voxels always map to distinct regions in the 2D transfer function, irrespective of interpolation. In contrast to previously reported algorithms, our algorithm does not require multiple passes for rendering and supports greater than 4 masks. It also allows for real-time modification of the colors/opacities of the segmented structures along with the original data. Additionally, these capabilities are available with minimal texture memory requirements amongst comparable algorithms. Results are presented on clinical and phantom data.
Medical image fusion is increasingly enhancing diagnostic accuracy
by synergizing information from multiple images, obtained by the
same modality at different times or from complementary modalities
such as structural information from CT and functional from PET. An
active, crucial research topic in fusion is validation of the registration (point-to-point correspondence) used. Phantoms and
other simulated studies are useful in the absence of, or as a preliminary to, definitive clinical tests. Software phantoms in
specific have the added advantage of robustness, repeatability and
reproducibility. Our virtual-lung-phantom-based scheme can test
the accuracy of any registration algorithm and is flexible enough
for added levels of complexity (addition of blur/anti-alias, rotate/warp, and modality-associated noise) to help evaluate the
robustness of an image registration/fusion methodology. Such a
framework extends easily to different anatomies. The feature of
adding software-based fiducials both within and outside simulated
anatomies prove more beneficial when compared to experiments using
data from external fiducials on a patient. It would help the diagnosing clinician make a prudent choice of registration algorithm.
In this paper, we propose a study, which investigates the first-order and second-order distributions of T2 images from a magnetic resonance (MR) scan for an age-matched data set of 24 Alzheimer's disease and 17 normal patients. The study is motivated by the desire to analyze the brain iron uptake in the hippocampus of Alzheimer's patients, which is captured by low T2 values. Since, excess iron deposition occurs locally in certain regions of the brain, we are motivated to investigate the spatial distribution of T2, which is captured by higher-order statistics. Based on the first-order and second-order distributions (involving gray level co-occurrence matrix) of T2, we show that the second-order statistics provide features with sensitivity >90% (at 80% specificity), which in turn capture the textural content in T2 data. Hence, we argue that different texture characteristics of T2 in the hippocampus for Alzheimer's and normal patients could be used as an early indicator of Alzheimer's disease.
Radiologists perform a CT Angiography procedure to examine vascular structures and associated pathologies such as aneurysms. Volume rendering is used to exploit volumetric capabilities of CT that provides complete interactive 3-D visualization. However, bone forms an occluding structure and must be segmented out. The anatomical
complexity of the head creates a major challenge in the segmentation of bone and vessel. An analysis of the head volume reveals varying spatial relationships between vessel and bone that can be separated into three sub-volumes: “proximal”, “middle”, and “distal”. The “proximal” and “distal” sub-volumes contain good spatial separation between bone and vessel (carotid referenced here). Bone and vessel appear contiguous in the “middle” partition that
remains the most challenging region for segmentation. The partition algorithm is used to automatically identify these partition locations so that different segmentation methods can be developed for each sub-volume. The partition locations are computed using bone, image entropy, and sinus profiles along with a rule-based method. The algorithm is validated on 21 cases (varying volume sizes, resolution, clinical sites, pathologies) using ground truth identified
visually. The algorithm is also computationally efficient, processing a 500+ slice volume in 6 seconds (an impressive 0.01 seconds / slice) that makes it an attractive algorithm for pre-processing large volumes. The partition algorithm is integrated into the segmentation workflow. Fast and simple algorithms are implemented for processing
the “proximal” and “distal” partitions. Complex methods are restricted to only the “middle” partition. The partitionenabled
segmentation has been successfully tested and results are shown from multiple cases.
All known methods of lossless or reversible data embedding that exist today suffer from two major disadvantages: 1) The embedded image suffers from distortion, however small it may be by the very process of embedding and 2) The requirement of a special parser (decoder), which is necessary for the client to remove the embedded data in order to obtain the original image (lossless). We propose a novel lossless data embedding method where both these disadvantages are circumvented. Zero-distortion lossless data embedding (ZeroD-LDE) claims 'zero-distortion' of the embedded image for all viewing purposes and further maintaining that clients without any specialized parser can still recover the original image losslessly but would not have direct access to the embedded data. The fact that not all gray levels are used by most images is exploited to embed data by selective lossless compression of run-lengths of zeros (or any compressible pattern). Contiguous runs of zeros are changed such that the leading zero is made equal to the maximum original intensity plus the run-length and the succeeding zeros are converted to the embedded data (plus maximum original intensity) thus achieving extremely high
embedding capacities. This way, the histograms of the host-data and the embedded data do not overlap and hence we can obtain zero-distortion by using the window-level setting of standard DICOM viewers. The embedded image is thus not only DICOM compatible but also zero-distortion visually and requires no clinical validation.
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