Pub Date : 2014-07-31DOI: 10.1109/ISBI.2014.6867895
Qian Zhao, Nabile M. Safdar, Glenna Yu, Emmarie Myers, A. Koroulakis, C. Duan, A. Sandler, M. Linguraru
Pectus excavatum (PE) is the most common major congenital deformity that involves the lower sternum and cartilages. Noncontrast CT is useful to assess the deformity of the bones and guide minimally invasive surgery. However, it has very poor visibility of cartilages even for the experienced clinicians who need to assess the 3D geometry of cartilages. In this study, we propose a novel method to estimate cartilages in noncontrast CT scans. The ribs and sternum are first segmented using region growing. The skeleton of the ribs is extracted and modeled by cosine series expansion. Then a statistical shape model is built with the cosine coefficients to estimate the cartilages as curves that connect the ribs and sternum. The results are refined by the cartilage surface that is approximated by contracting the skin surface to the bones. Leave-one-out validation was performed on 12 CT scans from healthy and PE subjects. The average distance between the estimated cartilages and ground truth is 1.53 mm. The promising results indicate that our method could estimate the costal cartilages in noncontrast CT effectively and assist to develop an image-based surgical planning system for PE correction.
{"title":"Cartilage estimation in noncontrast thoracic CT","authors":"Qian Zhao, Nabile M. Safdar, Glenna Yu, Emmarie Myers, A. Koroulakis, C. Duan, A. Sandler, M. Linguraru","doi":"10.1109/ISBI.2014.6867895","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867895","url":null,"abstract":"Pectus excavatum (PE) is the most common major congenital deformity that involves the lower sternum and cartilages. Noncontrast CT is useful to assess the deformity of the bones and guide minimally invasive surgery. However, it has very poor visibility of cartilages even for the experienced clinicians who need to assess the 3D geometry of cartilages. In this study, we propose a novel method to estimate cartilages in noncontrast CT scans. The ribs and sternum are first segmented using region growing. The skeleton of the ribs is extracted and modeled by cosine series expansion. Then a statistical shape model is built with the cosine coefficients to estimate the cartilages as curves that connect the ribs and sternum. The results are refined by the cartilage surface that is approximated by contracting the skin surface to the bones. Leave-one-out validation was performed on 12 CT scans from healthy and PE subjects. The average distance between the estimated cartilages and ground truth is 1.53 mm. The promising results indicate that our method could estimate the costal cartilages in noncontrast CT effectively and assist to develop an image-based surgical planning system for PE correction.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126164927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-07-31DOI: 10.1109/ISBI.2014.6867900
Damien Grosgeorge, C. Petitjean, S. Ruan
Segmenting the left ventricle (LV) and the right ventricle (RV) in magnetic resonance (MR) images is required for cardiac function assessment. In particular, the segmentation of the RV is a difficult task due to low contrast with surrounding tissues and high shape variability. To overcome these problems, we introduce a fully automatic segmentation method based on multi-label graph cuts, that makes use of a probabilistic shape model. The shape model is obtained by merging several atlases after their non-rigid registration on the unseen image. This prior is then incorporated into the multi-label graph cut framework in order to guide the segmentation. Our automatic segmentation method has been applied on 754 MR images. We show that encouraging results can be obtained for this challenging application.
{"title":"Joint segmentation of right and left cardiac ventricles using multi-label graph cut","authors":"Damien Grosgeorge, C. Petitjean, S. Ruan","doi":"10.1109/ISBI.2014.6867900","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867900","url":null,"abstract":"Segmenting the left ventricle (LV) and the right ventricle (RV) in magnetic resonance (MR) images is required for cardiac function assessment. In particular, the segmentation of the RV is a difficult task due to low contrast with surrounding tissues and high shape variability. To overcome these problems, we introduce a fully automatic segmentation method based on multi-label graph cuts, that makes use of a probabilistic shape model. The shape model is obtained by merging several atlases after their non-rigid registration on the unseen image. This prior is then incorporated into the multi-label graph cut framework in order to guide the segmentation. Our automatic segmentation method has been applied on 754 MR images. We show that encouraging results can be obtained for this challenging application.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126887324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-07-31DOI: 10.1109/ISBI.2014.6868085
B. D. Senneville, Y. Regard, C. Moonen, M. Ries
Real-time motion estimation has a growing interest for the guidance of interventional procedures in mobile organs. For this purpose, combined magnetic resonance (MR) imaging and ultrasound (US) echography systems can now provide both MR- and US- images, which can be exploited simultaneously for improved target tracking. For this purpose, two tracking strategies can be investigated: While indirect tracking methods rely on a calibration obtained prior to the intervention, direct tracking methods perform the target localization directly on the continuously acquired position. The current paper describes real-time methodological developments designed for the guidance of non-invasive interventional procedures, using a combined MR/US imaging system: A GPU (Graphics Processing Unit) optimized processing pipeline is proposed for both direct and indirect approaches, in conjunction with simultaneous high-frame-rate MR and echography. Experiments on a moving ex-vivo target were performed with MR-guided HIFU (High Intensity Focused Ultrasound) during continuous ultrasound echography. Real-time US echography-based tracking during MR-guided HIFU heating was achieved with heated area dimensions similar to those obtained for a static target.
{"title":"Combined ultrasound echography and magnetic resonance imaging guidance for direct and indirect target tracking","authors":"B. D. Senneville, Y. Regard, C. Moonen, M. Ries","doi":"10.1109/ISBI.2014.6868085","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6868085","url":null,"abstract":"Real-time motion estimation has a growing interest for the guidance of interventional procedures in mobile organs. For this purpose, combined magnetic resonance (MR) imaging and ultrasound (US) echography systems can now provide both MR- and US- images, which can be exploited simultaneously for improved target tracking. For this purpose, two tracking strategies can be investigated: While indirect tracking methods rely on a calibration obtained prior to the intervention, direct tracking methods perform the target localization directly on the continuously acquired position. The current paper describes real-time methodological developments designed for the guidance of non-invasive interventional procedures, using a combined MR/US imaging system: A GPU (Graphics Processing Unit) optimized processing pipeline is proposed for both direct and indirect approaches, in conjunction with simultaneous high-frame-rate MR and echography. Experiments on a moving ex-vivo target were performed with MR-guided HIFU (High Intensity Focused Ultrasound) during continuous ultrasound echography. Real-time US echography-based tracking during MR-guided HIFU heating was achieved with heated area dimensions similar to those obtained for a static target.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"182 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126954274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-07-31DOI: 10.1109/ISBI.2014.6867985
J. Kybic, Jiri Borovec
We describe an automatic method for fast registration of images with very different appearances. The images are jointly segmented into a small number of classes, the segmented images are registered, and the process is repeated. The segmentation calculates feature vectors on superpixels and then it finds a softmax classifier maximizing mutual information between class labels in the two images. For speed, the registration considers a sparse set of rectangular neighborhoods on the interfaces between classes. A triangulation is created with spatial regularization handled by pairwise spring-like terms on the edges. The optimal transformation is found globally using loopy belief propagation. Multiresolution helps to improve speed and robustness. Our main application is registering stained histological slices, which are large and differ both in the local and global appearance. We show that our method has comparable accuracy to standard pixel-based registration, while being faster and more general.
{"title":"Automatic simultaneous segmentation and fast registration of histological images","authors":"J. Kybic, Jiri Borovec","doi":"10.1109/ISBI.2014.6867985","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867985","url":null,"abstract":"We describe an automatic method for fast registration of images with very different appearances. The images are jointly segmented into a small number of classes, the segmented images are registered, and the process is repeated. The segmentation calculates feature vectors on superpixels and then it finds a softmax classifier maximizing mutual information between class labels in the two images. For speed, the registration considers a sparse set of rectangular neighborhoods on the interfaces between classes. A triangulation is created with spatial regularization handled by pairwise spring-like terms on the edges. The optimal transformation is found globally using loopy belief propagation. Multiresolution helps to improve speed and robustness. Our main application is registering stained histological slices, which are large and differ both in the local and global appearance. We show that our method has comparable accuracy to standard pixel-based registration, while being faster and more general.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"159 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127668102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-07-31DOI: 10.1109/ISBI.2014.6868035
Venkata R. Yelleswarapu, Fenglin Liu, W. Cong, Ge Wang
We previously discussed “omni-tomography”, but intrinsic conflicts between the magnetic fields of the MRI and the x-ray tube within the CT are inherent. We propose that by using low-field MRI with a negligible fringe field at the site of the CT source, it is possible to create a CT-MRI system with minimal interference. Low field MRI is particularly useful for lung imaging, where hyperpolarized gas can enhance the signal. Three major designs were considered and simulated, with modifications in coil design and axis allowing for further variation. The first uses Halbach arrays to minimize magnetic fields outside, the second uses solenoids pairs with active shielding, and the third uses a rotating compact MRI-CT. Each system is low field, which may allow the implementation of a standard rotating CT. Both structural and functional information can be acquired simultaneously for a true hybrid image with matching temporal and spatial image acquisition.
{"title":"TOP-level designs of a hybrid low field MRI-CT system for pulmonary imaging","authors":"Venkata R. Yelleswarapu, Fenglin Liu, W. Cong, Ge Wang","doi":"10.1109/ISBI.2014.6868035","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6868035","url":null,"abstract":"We previously discussed “omni-tomography”, but intrinsic conflicts between the magnetic fields of the MRI and the x-ray tube within the CT are inherent. We propose that by using low-field MRI with a negligible fringe field at the site of the CT source, it is possible to create a CT-MRI system with minimal interference. Low field MRI is particularly useful for lung imaging, where hyperpolarized gas can enhance the signal. Three major designs were considered and simulated, with modifications in coil design and axis allowing for further variation. The first uses Halbach arrays to minimize magnetic fields outside, the second uses solenoids pairs with active shielding, and the third uses a rotating compact MRI-CT. Each system is low field, which may allow the implementation of a standard rotating CT. Both structural and functional information can be acquired simultaneously for a true hybrid image with matching temporal and spatial image acquisition.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127250715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-07-31DOI: 10.1109/ISBI.2014.6868056
Wei Li, S. Sonntag, M. Becker, N. Marx, U. Steinseifer, D. Merhof
2D color Doppler imaging (CDI) is currently the clinical standard to assess the mitral regurgitation (MR) severity. However, due to technical and operational limitations, commonly used diagnostic approaches suffer from known shortcomings: inadequate reliability, poor reproducibility and heavy user-dependency. Aiming at improving the quality of medical assessment, an efficient numerical reconstruction of color Doppler images is presented. With help of a graphical user interface (GUI), virtual CDI of different system configurations and imaging parameters was conveniently generated in a reasonable time span. The numerical reconstruction was based on experimental results and computational fluid dynamics (CFD) simulation of a flow chamber with different orifices simulating variations of mitral insufficiency. This platform can be used to validate, evaluate and further develop existing diagnostic approaches of MR.
{"title":"Efficient numerical reconstruction of color Doppler images of mitral regurgitation in vitro","authors":"Wei Li, S. Sonntag, M. Becker, N. Marx, U. Steinseifer, D. Merhof","doi":"10.1109/ISBI.2014.6868056","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6868056","url":null,"abstract":"2D color Doppler imaging (CDI) is currently the clinical standard to assess the mitral regurgitation (MR) severity. However, due to technical and operational limitations, commonly used diagnostic approaches suffer from known shortcomings: inadequate reliability, poor reproducibility and heavy user-dependency. Aiming at improving the quality of medical assessment, an efficient numerical reconstruction of color Doppler images is presented. With help of a graphical user interface (GUI), virtual CDI of different system configurations and imaging parameters was conveniently generated in a reasonable time span. The numerical reconstruction was based on experimental results and computational fluid dynamics (CFD) simulation of a flow chamber with different orifices simulating variations of mitral insufficiency. This platform can be used to validate, evaluate and further develop existing diagnostic approaches of MR.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114384222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-07-31DOI: 10.1109/ISBI.2014.6867797
Xi Jiang, Jinglei Lv, Dajiang Zhu, Tuo Zhang, Xiang Li, Xintao Hu, Lei Guo, Tianming Liu
It is widely believed that working memory process involves large-scale functional interactions among multiple brain networks. However, network-level functional interactions across large-scale brain networks in working memory have been rarely explored yet in the literature. In this paper, we propose a novel framework for modeling network-level functional interactions in working memory based on our publicly released 358 DICCCOL landmarks. First, 14 DICCCOLs are detected as group-wise activated ROIs via GLM and compose the `basic network' of working memory. Second, the time-frequency functional interaction patterns of each pair of activated DICCCOL and other DICCCOLs are calculated using cross-wavelet transform. Third, the common functional interaction patterns and corresponding brain networks are learned via effective online dictionary learning and sparse coding methods. Experimental results showed that multiple brain networks are involved in working memory processes. More importantly, each brain network interacts with the `basic network' via a specific functionally meaningful time-frequency interaction pattern.
{"title":"Discovering network-level functional interactions from working memory fMRI data","authors":"Xi Jiang, Jinglei Lv, Dajiang Zhu, Tuo Zhang, Xiang Li, Xintao Hu, Lei Guo, Tianming Liu","doi":"10.1109/ISBI.2014.6867797","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867797","url":null,"abstract":"It is widely believed that working memory process involves large-scale functional interactions among multiple brain networks. However, network-level functional interactions across large-scale brain networks in working memory have been rarely explored yet in the literature. In this paper, we propose a novel framework for modeling network-level functional interactions in working memory based on our publicly released 358 DICCCOL landmarks. First, 14 DICCCOLs are detected as group-wise activated ROIs via GLM and compose the `basic network' of working memory. Second, the time-frequency functional interaction patterns of each pair of activated DICCCOL and other DICCCOLs are calculated using cross-wavelet transform. Third, the common functional interaction patterns and corresponding brain networks are learned via effective online dictionary learning and sparse coding methods. Experimental results showed that multiple brain networks are involved in working memory processes. More importantly, each brain network interacts with the `basic network' via a specific functionally meaningful time-frequency interaction pattern.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114382824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-07-31DOI: 10.1109/ISBI.2014.6867993
V. Uhlmann, R. Delgado-Gonzalo, M. Unser
We propose a novel active contour for the analysis of filament-like structures and boundaries - features that traditional snakes based on closed curves have difficulties to delineate. Our method relies on a parametric representation of an open curve involving Hermite-spline basis functions. This allows us to impose constraints both on the contour and on its derivatives. The proposed parameterization enables tangential controls and facilitates the design of an energy term that considers oriented features. In this way, our technique can be used to detect edges as well as ridges. The use of the Hermite-spline basis is well suited to a semi-interactive implementation. We developed an ImageJ plugin, and present experimental results on real biological data.
{"title":"Snakes with tangent-based control and energies for bioimage analysis","authors":"V. Uhlmann, R. Delgado-Gonzalo, M. Unser","doi":"10.1109/ISBI.2014.6867993","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867993","url":null,"abstract":"We propose a novel active contour for the analysis of filament-like structures and boundaries - features that traditional snakes based on closed curves have difficulties to delineate. Our method relies on a parametric representation of an open curve involving Hermite-spline basis functions. This allows us to impose constraints both on the contour and on its derivatives. The proposed parameterization enables tangential controls and facilitates the design of an energy term that considers oriented features. In this way, our technique can be used to detect edges as well as ridges. The use of the Hermite-spline basis is well suited to a semi-interactive implementation. We developed an ImageJ plugin, and present experimental results on real biological data.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"358 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122811385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-07-31DOI: 10.1109/ISBI.2014.6868109
Chao Jin, Dehui Xiang, Xinjian Chen
Automatic localization is one of important steps in medical image segmentation. In this paper, a model-based method for three-dimensional image localization is developed. Our method is based on a combination of 3D Generalized Hough Transform and 3D Active Appearance Models. It consists of two main parts: training and localization. The proposed method was tested on a clinical abdomen CT data set, including 27 contrast-enhanced volume data, in which 15 were chose as training data while the other 12 as testing data. The experimental results show that: (1) an overall cortex localization average distance is 12.58±3.26 voxels. (2) The proposed method is highly efficient, the running time is about only 35.70±3.62 seconds for each volume data.
{"title":"Renal cortex localization by combining 3D Generalized Hough Transform and 3D Active Appearance Models","authors":"Chao Jin, Dehui Xiang, Xinjian Chen","doi":"10.1109/ISBI.2014.6868109","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6868109","url":null,"abstract":"Automatic localization is one of important steps in medical image segmentation. In this paper, a model-based method for three-dimensional image localization is developed. Our method is based on a combination of 3D Generalized Hough Transform and 3D Active Appearance Models. It consists of two main parts: training and localization. The proposed method was tested on a clinical abdomen CT data set, including 27 contrast-enhanced volume data, in which 15 were chose as training data while the other 12 as testing data. The experimental results show that: (1) an overall cortex localization average distance is 12.58±3.26 voxels. (2) The proposed method is highly efficient, the running time is about only 35.70±3.62 seconds for each volume data.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130151460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-07-31DOI: 10.1109/ISBI.2014.6867942
Zhichao Lian, Xiang Li, Jianchuan Xing, Jinglei Lv, Xi Jiang, Dajiang Zhu, Shu Zhang, Jiansong Xu, M. Potenza, Tianming Liu, Jing Zhang
Multiple recent neuroimaging studies have demonstrated that the human brain's function undergoes remarkable temporal dynamics. However, quantitative characterization and modeling of such functional dynamics have been rarely explored. To fill this gap, we presents a novel Bayesian connectivity change point model (BCCPM), to analyze the joint probabilities among the nodes of brain networks between different time periods and statistically determine the boundaries of temporal blocks to estimate the change points. Intuitively, the determined change points represent the transitions of functional interaction patterns within the brain networks and can be used to investigate temporal functional brain dynamics. The BCCPM has been evaluated and validated by synthesized data. Also, the BCCPM has been applied to a real block-design task-based fMRI dataset and interesting results were obtained.
{"title":"Exploring functional brain dynamics via a Bayesian connectivity change point model","authors":"Zhichao Lian, Xiang Li, Jianchuan Xing, Jinglei Lv, Xi Jiang, Dajiang Zhu, Shu Zhang, Jiansong Xu, M. Potenza, Tianming Liu, Jing Zhang","doi":"10.1109/ISBI.2014.6867942","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867942","url":null,"abstract":"Multiple recent neuroimaging studies have demonstrated that the human brain's function undergoes remarkable temporal dynamics. However, quantitative characterization and modeling of such functional dynamics have been rarely explored. To fill this gap, we presents a novel Bayesian connectivity change point model (BCCPM), to analyze the joint probabilities among the nodes of brain networks between different time periods and statistically determine the boundaries of temporal blocks to estimate the change points. Intuitively, the determined change points represent the transitions of functional interaction patterns within the brain networks and can be used to investigate temporal functional brain dynamics. The BCCPM has been evaluated and validated by synthesized data. Also, the BCCPM has been applied to a real block-design task-based fMRI dataset and interesting results were obtained.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"36 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124516110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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