Accurate segmentation of bladder cancer is the basis for determining the staging of bladder cancer. In our previous study, we have segmented the inner and outer surface of bladder wall and obtained the candidate region of bladder cancer, however, it is hard to segment the cancer region from the candidate region. To segment the cancer region accurately, we proposed a voxel-feature-based method and extracted 1159 features from each voxel of candidate region. After feature extraction, the recursive feature elimination-based support vector machine classifier (SVM-RFE) method was adopted to obtain an optimal feature subset for the classification of the cancer and the wall regions. According to feature selection and ranking, 125 top-ranked features were selected as the optimal subset, with an area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity of 1, 99.99%, 99.98%, and 1. Using the optimal subset, we calculated the probability value of each voxel belonging to the cancer region, then obtained the boundary to separate the tumor and wall regions. The mean DSC of the segmentation results in the testing set is 0.9127, indicating that the proposed method can accurately segment the bladder cancer region.
The detection of non-polypoid colorectal lesions (e.g., the flat and small sessile polyps) is still a challenging task for the computer-aided detection (CADe) method. Different from previous CADe method, we proposed a new scheme to detect the lesion using the texture feature extracted from intact colon wall, since texture feature is sensitivity to detect subtle lesion and all the available information of the lesion imbeds in the colon wall. In this scheme, the inner and outer wall surface were segmented. Then, for each voxel of inner surface, a fixed size neighborhood was projected onto the colon model and the intersection volume of the projection through the colon model was selected as the volume of interest (VOI). From each VOI, three images were obtained: the original CT intensity and its gradient and curvature maps, Gray-scale co-occurrence matrices (CMs) were calculated from these 3 volumetric images, respectively. A total of 196 texture features (60 Haralick features and 6 CT histogram features extracted from each CM) were used to detect initial polyp candidates by a piecewise anomaly detection method of isolation forest, followed by a supervised classification (random Forests) for false positive (FP) reduction. The detection performance was evaluated by a 10-fold cross-validation and free-response receiver operating characteristics analysis. We evaluated our method via 10 patients with 36 confirmed flat and small sessile polyps were collected, including 16 flat, 18 sessile, and 2 pedunculated polyps. The presented detection method achieved 80% sensitivity with 9.98 FPs per dataset. The experiment results demonstrate that our method is a potential way to detect nonpolypoid polyps, particularly flat and depressed ones.
The triple network model provides a common framework for understanding affective and neurocognitive dysfunctions across multiple disorders, including central executive network (CEN), default mode network (DMN), and salience network (SN). Considering the effect of traumatic experience on post-traumatic stress disorder (PTSD), this study aims to explore the alteration of triple network connectivity in a specific PTSD induced by a single prolonged trauma exposure. With arterial spin labeling sequence, three networks were identified using independent component analysis in 10 PTSD patients and 10 healthy survivors, who experienced the same coal mining flood disaster. In PTSD patients, decreased connectivity was identified in left middle frontal gyrus of CEN, left precuneus and bilateral superior frontal gyrus of DMN, and right anterior insula of SN. The decreased connectivity in left middle frontal gyrus was identified to associate with clinical severity. These results indicated the decreased triple network connectivity, which not only supported the proposal of the triple network model, but also prompted possible neurobiology mechanism of cognitive dysfunction for this kind of PTSD.
Arterial spin labeling (ASL) provides a noninvasive measurement of cerebral blood flow (CBF). Due to relatively low spatial resolution, the accuracy of CBF measurement is affected by the partial volume (PV) effect. To obtain accurate CBF estimation, the contribution of each tissue type in the mixture is desirable. In general, this can be obtained according to the registration of ASL and structural image in current ASL studies. This approach can obtain probability of each tissue type inside each voxel, but it also introduces error, which include error of registration algorithm and imaging itself error in scanning of ASL and structural image. Therefore, estimation of mixture percentage directly from ASL data is greatly needed. Under the assumption that ASL signal followed the Gaussian distribution and each tissue type is independent, a maximum a posteriori expectation-maximization (MAP-EM) approach was formulated to estimate the contribution of each tissue type to the observed perfusion signal at each voxel. Considering the sensitivity of MAP-EM to the initialization, an approximately accurate initialization was obtain using 3D Fuzzy c-means method. Our preliminary results demonstrated that the GM and WM pattern across the perfusion image can be sufficiently visualized by the voxel-wise tissue mixtures, which may be promising for the diagnosis of various brain diseases.
Purpose: To 1) find effective texture features from multimodal MRI that can distinguish IDH mutant and wild status, and 2) propose a radiomic strategy for preoperatively detecting IDH mutation patients with glioma. Materials and Methods: 152 patients with glioma were retrospectively included from the Cancer Genome Atlas. Corresponding T1-weighted image before- and post-contrast, T2-weighted image and fluid-attenuation inversion recovery image from the Cancer Imaging Archive were analyzed. Specific statistical tests were applied to analyze the different kind of baseline information of LrGG patients. Finally, 168 texture features were derived from multimodal MRI per patient. Then the support vector machine-based recursive feature elimination (SVM-RFE) and classification strategy was adopted to find the optimal feature subset and build the identification models for detecting the IDH mutation. Results: Among 152 patients, 92 and 60 were confirmed to be IDH-wild and mutant, respectively. Statistical analysis showed that the patients without IDH mutation was significant older than patients with IDH mutation (p<0.01), and the distribution of some histological subtypes was significant different between IDH wild and mutant groups (p<0.01). After SVM-RFE, 15 optimal features were determined for IDH mutation detection. The accuracy, sensitivity, specificity, and AUC after SVM-RFE and parameter optimization were 82.2%, 85.0%, 78.3%, and 0.841, respectively. Conclusion: This study presented a radiomic strategy for noninvasively discriminating IDH mutation of patients with glioma. It effectively incorporated kinds of texture features from multimodal MRI, and SVM-based classification strategy. Results suggested that features selected from SVM-RFE were more potential to identifying IDH mutation. The proposed radiomics strategy could facilitate the clinical decision making in patients with glioma.
Ischemic stroke has great correlation with carotid atherosclerosis and is mostly caused by vulnerable plaques. It’s
particularly important to analysis the components of plaques for the detection of vulnerable plaques. Recently plaque
analysis based on multi-contrast magnetic resonance imaging has attracted great attention. Though multi-contrast MR
imaging has potentials in enhanced demonstration of carotid wall, its performance is hampered by the misalignment of
different imaging sequences. In this study, a coarse-to-fine registration strategy based on cross-sectional images and wall
boundaries is proposed to solve the problem. It includes two steps: a rigid step using the iterative closest points to register
the centerlines of carotid artery extracted from multi-contrast MR images, and a non-rigid step using the thin plate spline
to register the lumen boundaries of carotid artery. In the rigid step, the centerline was extracted by tracking the crosssectional
images along the vessel direction calculated by Hessian matrix. In the non-rigid step, a shape context descriptor
is introduced to find corresponding points of two similar boundaries. In addition, the deterministic annealing technique is
used to find a globally optimized solution. The proposed strategy was evaluated by newly developed three-dimensional,
fast and high resolution multi-contrast black blood MR imaging. Quantitative validation indicated that after registration,
the overlap of two boundaries from different sequences is 95%, and their mean surface distance is 0.12 mm. In conclusion,
the proposed algorithm has improved the accuracy of registration effectively for further component analysis of carotid
plaques.
To explore the alteration in cerebral blood flow (CBF) and functional connectivity between survivors with recent onset post-traumatic stress disorder (PTSD) and without PTSD, survived from the same coal mine flood disaster. In this study, a processing pipeline using arterial spin labeling (ASL) sequence was proposed. Considering low spatial resolution of ASL sequence, a linear regression method was firstly used to correct the partial volume (PV) effect for better CBF estimation. Then the alterations of CBF between two groups were analyzed using both uncorrected and PV-corrected CBF maps. Based on altered CBF regions detected from the CBF analysis as seed regions, the functional connectivity abnormities in PTSD patients was investigated. The CBF analysis using PV-corrected maps indicates CBF deficits in the bilateral frontal lobe, right superior frontal gyrus and right corpus callosum of PTSD patients, while only right corpus callosum was identified in uncorrected CBF analysis. Furthermore, the regional CBF of the right superior frontal gyrus exhibits significantly negative correlation with the symptom severity in PTSD patients. The resting-state functional connectivity indicates increased connectivity between left frontal lobe and right parietal lobe. These results indicate that PV-corrected CBF exhibits more subtle perfusion changes and may benefit further perfusion and connectivity analysis. The symptom-specific perfusion deficits and aberrant connectivity in above memory-related regions may be putative biomarkers for recent onset PTSD induced by a single prolonged trauma exposure and help predict the severity of PTSD.
To explore the alteration in white matter between survivors with recent onset post-traumatic stress disorder (PTSD) and without PTSD, who survived from the same coal mine flood disaster, the diffusion tensor imaging (DTI) sequences were analyzed using DTI studio and statistical parametric mapping (SPM) packages in this paper. From DTI sequence, the fractional anisotropy (FA) value describes the degree of anisotropy of a diffusion process, while the apparent diffusion coefficient (ADC) value reflects the magnitude of water diffusion. The DTI analyses between PTSD and non-PTSD indicate lower FA values in the right caudate nucleus, right middle temporal gyrus, right fusiform gyrus, and right superior temporal gyrus, and higher ADC values in the right superior temporal gyrus and right corpus callosum of the subjects with PTSD. These results are partly in line with our previous volume and cortical thickness analyses, indicating the importance of multi-modality analysis for PTSD.
Differentiating bladder tumors from wall tissues is of critical importance for the detection of invasion depth and cancer staging. The textural features embedded in bladder images have demonstrated their potentials in carcinomas detection and classification. The purpose of this study was to investigate the feasibility of differentiating bladder carcinoma from bladder wall using three-dimensional (3D) textural features extracted from MR bladder images. The widely used 2D Tamura features were firstly wholly extended to 3D, and then different types of 3D textural features including 3D features derived from gray level co-occurrence matrices (GLCM) and grey level-gradient co-occurrence matrix (GLGCM), as well as 3D Tamura features, were extracted from 23 volumes of interest (VOIs) of bladder tumors and 23 VOIs of patients’ bladder wall. Statistical results show that 30 out of 47 features are significantly different between cancer tissues and wall tissues. Using these features with significant differences between these two types of tissues, classification performance with a supported vector machine (SVM) classifier demonstrates that the combination of three types of selected 3D features outperform that of using only one type of features. All the observations demonstrate that significant textural differences exist between carcinomatous tissues and bladder wall, and 3D textural analysis may be an effective way for noninvasive staging of bladder cancer.
The ever-growing death rate and the high recurrence of bladder cancer make the early detection and appropriate followup procedure of bladder cancer attract more attention. Compare to optical cystoscopy, image-based studies have revealed its potentials in non-invasive observations of the abnormities of bladder recently, in which MR imaging turns out to be a better choice for bladder evaluation due to its non-ionizing and high contrast between urine and wall tissue. Recent studies indicate that bladder wall thickness tends to be a good indicator for detecting bladder wall abnormalities. However, it is difficult to quantitatively compare wall thickness of the same subject at different filling stages or among different subjects. In order to explore thickness variations at different bladder filling stages, in this study, we preliminarily investigate the relationship between bladder wall thickness and bladder volume based on a MRI database composed of 40 datasets acquired from 10 subjects at different filling stages, using a pipeline for thickness measurement and analysis proposed in our previous work. The Student’s t-test indicated that there was no significant different on wall thickness between the male group and the female group. The Pearson correlation analysis result indicated that negative correlation with a correlation coefficient of -0.8517 existed between the wall thickness and bladder volume, and the correlation was significant(p <0.01). The corresponding linear regression equation was then estimated by the unary linear regression. Compared to the absolute value of wall thickness, the z-score of wall thickness would be more appropriate to reflect the thickness variations. For possible abnormality detection of a bladder based on wall thickness, the intra-subject and inter-subject thickness variation should be considered.
KEYWORDS: Lawrencium, Photovoltaics, Expectation maximization algorithms, 3D image processing, Statistical analysis, Data corrections, Image segmentation, In vivo imaging, 3D modeling, Spatial resolution
Arterial spin labeling (ASL) provides a noninvasive measurement of cerebral blood flow (CBF). Due to relatively low spatial resolution, the accuracy of CBF measurement is affected by the partial volume (PV) effect. In general ASL sequence, multiple scans of perfusion image pairs are acquired temporally to improve the signal to noise ratio. Several spatial PV correction methods have been proposed for the simple averaging of pair-difference images, while the perfusion information of gray matter and white matter existed in multiple image pairs was totally ignored. In this study, a statistical model of perfusion mixtures inside each voxel for the 4D ASL sequence is first proposed. To solve the model, a simplified method is proposed, in which the linear regression (LR) method is first used to obtain initial estimates of spatial correction, then an EM (expectation maximization) method is used to obtain accurate estimation using temporal information. The combination of LR and EM method (EM-LR) can effectively utilize the spatial-temporal information of ASL data for PV correction and provide a theoretical solution to estimate the perfusion mixtures. Both simulated and in vivo data were used to evaluate the performance of proposed method, which demonstrated its superiority on PV correction, edge preserving, and noise suppression.
The majority of studies on posttraumatic stress disorder (PTSD) so far have focused on delineating patterns of activations during cognitive processes. Recently, more and more researches have started to investigate functional connectivity in PTSD subjects using BOLD-fMRI. Functional connectivity analysis has been demonstrated as a powerful approach to identify biomarkers of different brain diseases. This study aimed to detect resting-state functional connectivity abnormities in patients with PTSD using arterial spin labeling (ASL) fMRI. As a completely non-invasive technique, ASL allows quantitative estimates of cerebral blood flow (CBF). Compared with BOLD-fMRI, ASL fMRI has many advantages, including less low-frequency signal drifts, superior functional localization, etc. In the current study, ASL images were collected from 10 survivors in mining disaster with recent onset PTSD and 10 survivors without PTSD. Decreased regional CBF in the right middle temporal gyrus, lingual gyrus, and postcentral gyrus was detected in the PTSD patients. Seed-based resting-state functional connectivity analysis was performed using an area in the right middle temporal gyrus as region of interest. Compared with the non-PTSD group, the PTSD subjects demonstrated increased functional connectivity between the right middle temporal gyrus and the right superior temporal gyrus, the left middle temporal gyrus. Meanwhile, decreased functional connectivity between the right middle temporal gyrus and the right postcentral gyrus, the right superior parietal lobule was also found in the PTSD patients. This is the first study which investigated resting-state functional connectivity in PTSD using ASL images. The results may provide new insight into the neural substrates of PTSD.
High-speed motion analysis system could record images up to 12,000fps and analyzed with the image processing system.
The system stored data and images directly in electronic memory convenient for managing and analyzing. The high-speed
motion analysis system and the X-ray radiography system were established the high-speed real-time X-ray
radiography system, which could diagnose and measure the dynamic and high-speed process in opaque. The image
processing software was developed for improve quality of the original image for acquiring more precise information.
The typical applications of high-speed motion analysis system on solid rocket motor (SRM) were introduced in the
paper. The research of anomalous combustion of solid propellant grain with defects, real-time measurement experiment
of insulator eroding, explosion incision process of motor, structure and wave character of plume during the process of
ignition and flameout, measurement of end burning of solid propellant, measurement of flame front and compatibility
between airplane and missile during the missile launching were carried out using high-speed motion analysis system.
The significative results were achieved through the research. Aim at application of high-speed motion analysis system on
solid rocket motor, the key problem, such as motor vibrancy, electrical source instability, geometry aberrance, and yawp
disturbance, which damaged the image quality, was solved. The image processing software was developed which
improved the capability of measuring the characteristic of image. The experimental results showed that the system was a
powerful facility to study instantaneous and high-speed process in solid rocket motor. With the development of the
image processing technique, the capability of high-speed motion analysis system was enhanced.
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