Ophthalmic examinations, including fundus and optical coherence tomography (OCT) imaging, are integral in diagnosing systemic diseases affecting the eye, such as diabetic retinopathy. Recent studies have highlighted the related pathological features between retinal and cerebral small vessels, suggesting that retinal microvascular changes could reflect the status of cerebral small vessel disease (CSVD). In this research, we assessed patient’s fundus and OCT images, with a focus on CSVD severity. By incorporating patient demographic data, we developed a sparse Bayesian-based model using retinal and fundus vascular imaging for CSVD diagnosis, achieving an accuracy (ACC) of 71.71% and an area under the curve (AUC) of 73.35%. Our findings indicate that ophthalmic examinations can be a cost-effective screening method for CSVD. Implementing this methodology in optician and eye clinics may substantially lessen societal and familial impacts relative to conventional magnetic resonance imaging (MRI) diagnostics. Additionally, this study identified crucial early warning signs of severe CSVD, such as global venous width, and provided valuable insights into the nature of the disease.
Tourette syndrome (TS) is a childhood-onset neurobehavioral disorder and its pathophysiological mechanism remains elusive. At present, TS-related abnormalities in either structural connectivity (SC) or functional connectivity (FC) have extensively been described, and discrepancies were apparent between the SC and FC studies. However, abnormalities in the SC-FC correlation for early TS children remain poorly understood. In our study, we used probabilistic diffusion tractography and resting-state FC to construct large-scale structural and functional brain networks for 34 drug-naive TS children and 42 healthy children. Graph theoretical approaches were employed to divide the group-averaged FC networks into functional modules. The Pearson correlation between the entries of SC and FC were estimated as SC-FC coupling within whole-brain and each module. Although five common functional modules (including the sensorimotor, default-mode, fronto-parietal, temporo-occipital and subcortical modules) were identified in both groups, we found SC– FC coupling in TS exhibited increased at the whole-brain and functional modular level, especially within sensorimotor and subcortical modules. The increased SC-FC coupling may suggest that TS pathology leads to functional interactions that are more directly related to the underlying SC of the brain and may be indicative of more stringent and less dynamic brain function in TS children. Together, our study demonstrated that altered whole-brain and module-dependent SC-FC couplings may underlie abnormal brain function in TS, and highlighted the potential for using multimodal neuroimaging biomarkers for TS diagnosis as well as understanding the pathophysiologic mechanisms of TS.
Tourette syndrome (TS) is a childhood-onset neurobehavioral disorder. At present, the topological disruptions of the whole brain white matter (WM) structural networks remain poorly understood in TS children. Considering the unique position of the topologically central role of densely interconnected brain hubs, namely the rich club regions, therefore, we aimed to investigate whether the rich club regions and their related connections would be particularly vulnerable in early TS children. In our study, we used diffusion tractography and graph theoretical analyses to explore the rich club structures in 44 TS children and 48 healthy children. The structural networks of TS children exhibited significantly increased normalized rich club coefficient, suggesting that TS is characterized by increased structural integrity of this centrally embedded rich club backbone, potentially resulting in increased global communication capacity. In addition, TS children showed a reorganization of rich club regions, as well as significantly increased density and decreased number in feeder connections. Furthermore, the increased rich club coefficients and feeder connections density of TS children were significantly positively correlated to tic severity, indicating that TS may be characterized by a selective alteration of the structural connectivity of the rich club regions, tending to have higher bridging with non-rich club regions, which may increase the integration among tic-related brain circuits with more excitability but less inhibition for information exchanges between highly centered brain regions and peripheral areas. In all, our results suggest the disrupted rich club organization in early TS children and provide structural insights into the brain networks.
Tourette syndrome (TS) is a childhood-onset neurobehavioral disorder. To date, TS is still misdiagnosed due to its varied presentation and lacking of obvious clinical symptoms. Therefore, studies of objective imaging biomarkers are of great importance for early TS diagnosis. As tic generation has been linked to disturbed structural networks, and many efforts have been made recently to investigate brain functional or structural networks using machine learning methods, for the purpose of disease diagnosis. However, few studies were related to TS and some drawbacks still existed in them. Therefore, we propose a novel classification framework integrating a multi-threshold strategy and a network fusion scheme to address the preexisting drawbacks. Here we used diffusion MRI probabilistic tractography to construct the structural networks of 44 TS children and 48 healthy children. We ameliorated the similarity network fusion algorithm specially to fuse the multi-threshold structural networks. Graph theoretical analysis was then implemented, and nodal degree, nodal efficiency and nodal betweenness centrality were selected as features. Finally, support vector machine recursive feature extraction (SVM-RFE) algorithm was used for feature selection, and then optimal features are fed into SVM to automatically discriminate TS children from controls. We achieved a high accuracy of 89.13% evaluated by a nested cross validation, demonstrated the superior performance of our framework over other comparison methods. The involved discriminative regions for classification primarily located in the basal ganglia and frontal cortico-cortical networks, all highly related to the pathology of TS. Together, our study may provide potential neuroimaging biomarkers for early-stage TS diagnosis.
Tourette syndrome (TS) is a developmental neuropsychiatric disorder with the cardinal symptoms of motor and vocal tics which emerges in early childhood and fluctuates in severity in later years. To date, the neural basis of TS is not fully understood yet and TS has a long-term prognosis that is difficult to accurately estimate. Few studies have looked at the potential of using diffusion tensor imaging (DTI) in conjunction with machine learning algorithms in order to automate the classification of healthy children and TS children. Here we apply Tract-Based Spatial Statistics (TBSS) method to 44 TS children and 48 age and gender matched healthy children in order to extract the diffusion values from each voxel in the white matter (WM) skeleton, and a feature selection algorithm (ReliefF) was used to select the most salient voxels for subsequent classification with support vector machine (SVM). We use a nested cross validation to yield an unbiased assessment of the classification method and prevent overestimation. The accuracy (88.04%), sensitivity (88.64%) and specificity (87.50%) were achieved in our method as peak performance of the SVM classifier was achieved using the axial diffusion (AD) metric, demonstrating the potential of a joint TBSS and SVM pipeline for fast, objective classification of healthy and TS children. These results support that our methods may be useful for the early identification of subjects with TS, and hold promise for predicting prognosis and treatment outcome for individuals with TS.
Tourette syndrome (TS) is a childhood-onset neurobehavioral disorder characterized by the presence of multiple motor and vocal tics. Tic generation has been linked to disturbed networks of brain areas involved in planning, controlling and execution of action. The aim of our work is to select topological characteristics of structural network which were most efficient for estimating the classification models to identify early TS children. Here we employed the diffusion tensor imaging (DTI) and deterministic tractography to construct the structural networks of 44 TS children and 48 age and gender matched healthy children. We calculated four different connection matrices (fiber number, mean FA, averaged fiber length weighted and binary matrices) and then applied graph theoretical methods to extract the regional nodal characteristics of structural network. For each weighted or binary network, nodal degree, nodal efficiency and nodal betweenness were selected as features. Support Vector Machine Recursive Feature Extraction (SVM-RFE) algorithm was used to estimate the best feature subset for classification. The accuracy of 88.26% evaluated by a nested cross validation was achieved on combing best feature subset of each network characteristic. The identified discriminative brain nodes mostly located in the basal ganglia and frontal cortico-cortical networks involved in TS children which was associated with tic severity. Our study holds promise for early identification and predicting prognosis of TS children.
Amblyopia is a common yet hard-to-cure disease in children and results in poor or blurred vision. Some efforts such as voxel-based analysis, cortical thickness analysis have been tried to reveal the pathogenesis of amblyopia. However, few studies focused on alterations of the functional connectivity (FC) in amblyopia. In this study, we analyzed the abnormalities of amblyopia patients by both the seed-based FC with the left/right primary visual cortex and the network constructed throughout the whole brain. Experiments showed the following results: (1)As for the seed-based FC analysis, FC between superior occipital gyrus and the primary visual cortex was found to significantly decrease in both sides. The abnormalities were also found in lingual gyrus. The results may reflect functional deficits both in dorsal stream and ventral stream. (2)Two increased functional connectivities and 64 decreased functional connectivities were found in the whole brain network analysis. The decreased functional connectivities most concentrate in the temporal cortex. The results suggest that amblyopia may be caused by the deficits in the visual information transmission.
Shape regression analysis is a powerful tool to study local shape changes as a function of an independent regressor
variable. In this paper, we introduce spherical harmonic(SPHARM) representation to surface manifold learning and shape regression. Here, we use root mean square distance(RMSD) to measure the deformation degree of the surface, and find out that the hippocampus’ deformation degree is increased over age. We also investigate the particular changing area, and discover that the hippocampus have significant changes in the frontal area and tail area, especially in CA1 subfield.
Temporal lobe epilepsy (TLE) is one of the most common epilepsy syndromes with focal seizures generated in the left or
right temporal lobes. With the magnetic resonance imaging (MRI), many evidences have demonstrated that the
abnormalities in hippocampal volume and the distributed atrophies in cortical cortex. However, few studies have
investigated if TLE patients have the alternation in the structural networks. In the present study, we used the cortical
thickness to establish the morphological connectivity networks, and investigated the network properties using the graph
theoretical methods. We found that all the morphological networks exhibited the small-world efficiency in left TLE,
right TLE and normal groups. And the betweenness centrality analysis revealed that there were statistical inter-group
differences in the right uncus region. Since the right uncus located at the right temporal lobe, these preliminary evidences
may suggest that there are topological alternations of the cortical anatomical networks in TLE, especially for the right
TLE.
Hippocampal sclerosis (HS) is the most common damage seen in the patients with temporal lobe epilepsy (TLE). In the
present study, the hippocampal-cortical connectivity was defined as the correlation between the hippocampal volume and
cortical thickness at each vertex throughout the whole brain. We aimed to investigate the differences of ipsilateral
hippocampal-cortical connectivity between the unilateral TLE-HS patients and the normal controls. In our study, the
bilateral hippocampal volumes were first measured in each subject, and we found that the ipsilateral hippocampal
volume significantly decreased in the left TLE-HS patients. Then, group analysis showed significant thinner average
cortical thickness of the whole brain in the left TLE-HS patients compared with the normal controls. We found
significantly increased ipsilateral hippocampal-cortical connectivity in the bilateral superior temporal gyrus, the right
cingulate gyrus and the left parahippocampal gyrus of the left TLE-HS patients, which indicated structural vulnerability
related to the hippocampus atrophy in the patient group. However, for the right TLE-HS patients, no significant
differences were found between the patients and the normal controls, regardless of the ipsilateral hippocampal volume,
the average cortical thickness or the patterns of hippocampal-cortical connectivity, which might be related to less
atrophies observed in the MRI scans. Our study provided more evidence for the structural abnormalities in the unilateral
TLE-HS patients.
Resting-state functional magnetic resonance imaging (fMRI) is a technique that measures the intrinsic function of brain
and has some advantages over task-induced fMRI. Regional homogeneity (ReHo) assesses the similarity of the time
series of a given voxel with its nearest neighbors on a voxel-by-voxel basis, which reflects the temporal homogeneity of
the regional BOLD signal. In the present study, we used the resting state fMRI data to investigate the ReHo changes of
the whole brain in the prelingually deafened patients relative to normal controls. 18 deaf patients and 22 healthy subjects
were scanned. Kendall's coefficient of concordance (KCC) was calculated to measure the degree of regional coherence
of fMRI time courses. We found that regional coherence significantly decreased in the left frontal lobe, bilateral
temporal lobes and right thalamus, and increased in the postcentral gyrus, cingulate gyrus, left temporal lobe, left
thalamus and cerebellum in deaf patients compared with controls. These results show that the prelingually deafened
patients have higher degree of regional coherence in the paleocortex, and lower degree in neocortex. Since neocortex
plays an important role in the development of auditory, these evidences may suggest that the deaf persons reorganize the
paleocortex to offset the loss of auditory.
Extraction of the luminal contours from the intravascular ultrasound (IVUS) images is very important to analysis and diagnosis of coronary heart disease. Manual processing of large IVUS data is quite tedious and time consuming. This paper presented an algorithm for automatic detection of the luminal contours in intravascular ultrasound images, based on fuzzy clustering and snakes. To solve the difficulty of automatic contour initialization, this paper used fuzzy clustering and spline interpolation to obtain the initial contour. First, fuzzy clustering was used to detect the luminal
contours on the multiple longitudinal images. Then, luminal contour points were transformed into the individual transversal images. Those luminal contour points were spline-interpolated on these transversal images. The spline-interpolated contour was used as the initial contour of snakes. We evaluated automatically detection method based on the average contours obtained from expert manual segmentation as the ground truth, and the results had demonstrated that our method was accurate and efficient.
Evidence from several previous studies indicated that apparent diffusion coefficient (ADC) map was likely to reveal brain
regions belonging to the ischemic penumbra, that is, areas that may be at risk of infarction in a few hours following stroke
onset. Trace map overcomes the anisotropic diffusions of ADC map, so it is superior for evaluation of an infarct involving
white matter. Mean shift (MS) approach has been successfully used for image segmentation, particularly in brain MR
images. The aim of the study was to develop a tool for rapid and reliable segmentation of infarct in human acute ischemic
stroke based on the ADC and trace maps using the MS approach. In addition, a novel method of 3-dimensional visualization
was presented to provide useful insights into volume datasets for clinical diagnosis. We applied the presented method to
clinical data. The results showed that it was consistent, fast (about 8-10 minutes per subject) and indistinguishable from an
expert using manual segmentation when used our tool.
Tensor-based morphometry (TBM) is an automated technique for detecting the anatomical differences between populations by examining the gradients of the deformation fields used to nonlinearly warp MR images. The purpose of this study was to investigate the whole-brain volume changes between the patients with unilateral temporal lobe epilepsy (TLE) and the controls using TBM with DARTEL, which could achieve more accurate inter-subject registration of brain images. T1-weighted images were acquired from 21 left-TLE patients, 21 right-TLE patients and 21 healthy controls,
which were matched in age and gender. The determinants of the gradient of deformation fields at voxel level were obtained to quantify the expansion or contraction for individual images relative to the template, and then logarithmical transformation was applied on it. A whole brain analysis was performed using general lineal model (GLM), and the multiple comparison was corrected by false discovery rate (FDR) with p<0.05. For left-TLE patients, significant volume reductions were found in hippocampus, cingulate gyrus, precentral gyrus, right temporal lobe and cerebellum. These
results potentially support the utility of TBM with DARTEL to study the structural changes between groups.
Conventional film-screen mammography is the most effective tool for the early detection of breast cancer currently available. However, conventional mammography has relatively low sensitivity to detect small breast cancers (under several millimeters) owing to an overlap in the appearances of benign and malignant lesions, and surrounding structure. The limitations accompanying conventional mammography is to be addressed by incorporating a cone beam CT imaging technique with a recently developed flat panel detector. Computer simulation and preliminary studies have been performed to prove the feasibility of developing a flat panel detector-based cone beam CT breast imaging (FPD-CBCTBI) technique. A preliminary system characterization study of flat panel detector-based cone beam CT for breast imaging was performed to confirm the findings in the computer simulation and previous phantom studies using the current prototype cone beam CT scanner. The results indicate that the CBCTBI technique effectively removes structure overlap and significantly improves the detectability of small breast tumors. More importantly, the results also demonstrate CBCTBI offers good image quality with the radiation dose level less than or equal to that of conventional mammography. The results from this study suggest that FPD-CBCTBI is a potentially powerful breast-imaging tool.
Virtual endoscopy is meaningful for medical diagnosis and surgery. In this paper, a system framework for virtual endoscopy is proposed including automatic centerline extraction and view-dependent level-of-detail rendering techniques. Combining Hessian Matrix with distance mapping, our path planning method can generate accurate skeleton for virtual navigation. Furthermore real tim rendering can be achieved with our new view-dependent subdivision algorithm. The experimental results show the efficiency of our methods.
In this paper a practical surface reconstruction algorithm is proposed to efficiently process very large medical dataset in general PC. By considering the conflict between memory consumption and traversal speed, we restrict the traditional surface tracking in single layer and thus get a better trade-off between them. We also use a compression scheme to store the generated mesh, which decrease the memory requirement considerably. For efficient rendering, we employ a triangle strips generation algorithm to decode directly the com-pressed mesh into triangle strip. The experimental results tested on visible man fresh CT dataset show that the proposed algorithm is very efficient in both extracting and rendering phase.
KEYWORDS: Image segmentation, Medical imaging, 3D modeling, 3D image processing, Image processing, Image analysis, 3D image reconstruction, Volume rendering, Data modeling, Surgery
An integrated 3D medical image processing and analysis system we developed can provide powerful functions such as image preprocessing, virtual cutting, surface rendering, volume rendering, and manipulation. The system description, the method adopted and the application examples are presented. The system can be widely applied to processing and analysis of CT and MR images.
In the field of medical imaging, researchers often need visualize lots of 3D datasets to get the informaiton contained in these datasets. But the huge data genreated by modern medical imaging device challenge the real time processing and rendering algorithms at all the time. Spurring by the great achievement of Points Based Rendering (PBR) in the fields of computer graphics to render very large meshes, we propose a new algorithm to use the points as basic primitive of surface reconstruction and rendering to interactively reconstruct and render very large volume dataset. By utilizing the special characteristics of medical image datasets, we obtain a fast and efficient points-based reconstruction and rendering algorithm in common PC. The experimental results show taht this algorithm is feasible and efficient.
KEYWORDS: Endoscopy, 3D modeling, Reconstruction algorithms, Medical imaging, 3D image processing, Data modeling, Algorithm development, Visibility, 3D image reconstruction, Phase modulation
With the increasing of medical image datasets, the 3D model obtained by reconstruction often incorporates millions of triangles that make real time rendering very difficult. Progressive Mesh (PM) had been developed to address the above problem of view-dependent level-of-detail control, but its speed can’t meet the requirement of virtual endoscopy. In this study, we developed a new view-dependent continuous level-of-detail (CLOD) algorithm for triangle meshes with subdivision connectivity. First, the mesh was simplified in hierarchy to get the simplest mesh (called as base domain), then each hierarchy of the simplified mesh was parameterized to map to the base domain, and finally the view-dependent subdivision was used to resample the mesh to get a multi-resolution model. We constructed an index to record the changes of view parameters by the adaptive octree so as to make full use of the reusability of the adjacent frame and reduce the dynamic changes of the selected levels of detail. We tested our algorithm in several different datasets. The experiments showed that our method is efficient and easy to implement, and the model can be rendered in real time to meet the requirement of virtual endoscopy.
To study the application of diffusion weighted imaging and image post processing in the diagnosis of stroke, especially in acute stroke, 205 patients were examined by 1.5 T or 1.0 T MRI scanner and the images such as T1, T2 and diffusion weighted images were obtained. Image post processing was done with "3D Med System" developed by our lab to analyze data and acquire the apparent diffusion coefficient (ADC) map. In acute and subacute stage of stroke, the signal in cerebral infarction areas changed to hyperintensity in T2- and diffusion-weighted images, normal or hypointensity in T1-weighted images. In hyperacute stage, however, the signal was hyperintense just in the diffusion weighted imaes; others were normal. In the chronic stage, the signal in T1- and diffusion-weighted imaging showed hypointensity and hyperintensity in T2 weighted imaging. Because ADC declined obviously in acute and subacute stage of stroke, the lesion area was hypointensity in ADC map. With the development of the disease, ADC gradually recovered and then changed to hyperintensity in ADC map in chronic stage. Using diffusion weighted imaging and ADC mapping can make a diagnosis of stroke, especially in the hyperacute stage of stroke, and can differentiate acute and chronic stroke.
In this paper, an algorithm for the semiautomatic segmentation of medical image series is proposed by combining the live wire algorithm and the active contour model. First, we use the robust anisotropic diffusion filtering to smooth the images while keeping the edges. Then we modify the traditional live wire algorithm by combining it with the watershed method. Using the improved live wire method, the accurate segmentation of one or more medical images could be obtained firstly. Based on the segmentation of previous slices, the computer will segment the nearby slices using the modified active contour model automatically. To make full use of the correlative information between contiguous slices, a gray-scale model is applied to the model to record the local region characters of the desired object, and a new functional definition of the external energy is designed. Furthermore, in order to be adaptable with the topological change of the nearby slices, affine cell image decomposition is applied to the active contour model. The experiment results show that this algorithm can recover the boundary of the desired object from a series of medical images quickly and reliably with only little user intervention.
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