Pub Date : 2014-07-31DOI: 10.1109/ISBI.2014.6868083
S. Saporito, Ingeborg H. F. Herold, P. Houthuizen, H. Korsten, H. C. Assen, M. Mischi
Several cardiovascular parameters of clinical interest can be assessed by indicator dilution techniques. Ultrasound contrast agents have been proposed as non-invasive indicator, showing promising results for blood volume estimation. However, the definition of an optimal region of interest for quantification of the indicator remains a critical step in the procedure, usually performed manually. In this work we present an automatic method to extract indicator dilution curves. Dimensionality reduction is achieved by principal component analysis followed by clustering to identify the different regions of interest. The method is evaluated on in vitro and in vivo datasets, compared to manually defined regions. The average difference was -3.47% ± 3.58% for in vitro volume estimates and the error was 1.29% ± 2.54% for trans-pulmonary mean transit time estimation. The new method allows kinetic parameter estimates in close agreement with those obtained manually; therefore it is a promising alternative for indicator dilution curve extraction.
{"title":"Automatic blood pool identification in contrast ultrasound using principal component analysis","authors":"S. Saporito, Ingeborg H. F. Herold, P. Houthuizen, H. Korsten, H. C. Assen, M. Mischi","doi":"10.1109/ISBI.2014.6868083","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6868083","url":null,"abstract":"Several cardiovascular parameters of clinical interest can be assessed by indicator dilution techniques. Ultrasound contrast agents have been proposed as non-invasive indicator, showing promising results for blood volume estimation. However, the definition of an optimal region of interest for quantification of the indicator remains a critical step in the procedure, usually performed manually. In this work we present an automatic method to extract indicator dilution curves. Dimensionality reduction is achieved by principal component analysis followed by clustering to identify the different regions of interest. The method is evaluated on in vitro and in vivo datasets, compared to manually defined regions. The average difference was -3.47% ± 3.58% for in vitro volume estimates and the error was 1.29% ± 2.54% for trans-pulmonary mean transit time estimation. The new method allows kinetic parameter estimates in close agreement with those obtained manually; therefore it is a promising alternative for indicator dilution curve extraction.","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":"121858901","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.2014.6867840
Thiago de C. Martins, A. Fernandes, M. Tsuzuki
Electrical Impedance Tomography (EIT) image reconstruction can be approached as an optimization problem, intending to minimize the Euclidean distance between the potential values measured in the cross section of the body and the calculated values, for every pattern of current applied, through modelling the problem by the Finite Elements Method (FEM). This formulation is known to be ill-posed, which increases dependence of the EIT on the reconstruction algorithm, which must have a regularization technique to improve the conditioning of the problem. Therefore, this project proposes the use of a Multi-Objective Optimization algorithm in order to find the set of optimal solutions to the problem, aiming to minimize both the Euclidean distance and a regularization parameter.
{"title":"Image reconstruction by electrical impedance tomography using multi-objective simulated annealing","authors":"Thiago de C. Martins, A. Fernandes, M. Tsuzuki","doi":"10.1109/ISBI.2014.6867840","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867840","url":null,"abstract":"Electrical Impedance Tomography (EIT) image reconstruction can be approached as an optimization problem, intending to minimize the Euclidean distance between the potential values measured in the cross section of the body and the calculated values, for every pattern of current applied, through modelling the problem by the Finite Elements Method (FEM). This formulation is known to be ill-posed, which increases dependence of the EIT on the reconstruction algorithm, which must have a regularization technique to improve the conditioning of the problem. Therefore, this project proposes the use of a Multi-Objective Optimization algorithm in order to find the set of optimal solutions to the problem, aiming to minimize both the Euclidean distance and a regularization parameter.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"18 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":"121694097","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.2014.6868090
D. Laptev, A. Veznevets, J. Buhmann
In biological imaging the data is often represented by a sequence of anisotropic frames - the resolution in one dimension is significantly lower than in the other dimensions. E.g. in electron microscopy it arises from the thickness of a scanned section. This leads to blurred images and raises problems in tasks like neuronal image segmentation. We present an approach called SuperSlicing to decompose the observed frame into a sequence of plausible hidden sub-frames. Based on sub-frame decomposition by SuperSlicing we propose a novel automated method to perform neuronal structure segmentation. We test our approach on a popular benchmark, where SuperSlicing preserves topological structures significantly better than other algorithms.
{"title":"Superslicing frame restoration for anisotropic sstem","authors":"D. Laptev, A. Veznevets, J. Buhmann","doi":"10.1109/ISBI.2014.6868090","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6868090","url":null,"abstract":"In biological imaging the data is often represented by a sequence of anisotropic frames - the resolution in one dimension is significantly lower than in the other dimensions. E.g. in electron microscopy it arises from the thickness of a scanned section. This leads to blurred images and raises problems in tasks like neuronal image segmentation. We present an approach called SuperSlicing to decompose the observed frame into a sequence of plausible hidden sub-frames. Based on sub-frame decomposition by SuperSlicing we propose a novel automated method to perform neuronal structure segmentation. We test our approach on a popular benchmark, where SuperSlicing preserves topological structures significantly better than other algorithms.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"10 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":"125247321","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.2014.6868134
Jue Wu, M. Ashtari, L. Betancourt, N. Brodsky, J. Giannetta, J. Gee, H. Hurt, B. Avants
In the absence of a neonatal template with cortical subregion labels, it can be extremely difficult to obtain cortical parcellation of new neonatal brain images automatically. This paper addresses this problem by utilizing adult templates with rich cortical annotation and a neonatal template with simple tissue labels. Theoretical feasibility is assured because of the preservation of brain putative cytoarchitectonic boundaries from birth to adulthood. We use large deformation registration to propagate neuroanatomical labels from adult to neonatal brain and perform multi-atlas labeling based on accurate prior-based tissue segmentation. We evaluate the repeatability of the labeling by cross-validation with training and testing data. Preliminary results show interesting relationship between the volumes of labels and independent measures of neonatal development and maternal characteristics.
{"title":"Cortical parcellation for neonatal brains","authors":"Jue Wu, M. Ashtari, L. Betancourt, N. Brodsky, J. Giannetta, J. Gee, H. Hurt, B. Avants","doi":"10.1109/ISBI.2014.6868134","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6868134","url":null,"abstract":"In the absence of a neonatal template with cortical subregion labels, it can be extremely difficult to obtain cortical parcellation of new neonatal brain images automatically. This paper addresses this problem by utilizing adult templates with rich cortical annotation and a neonatal template with simple tissue labels. Theoretical feasibility is assured because of the preservation of brain putative cytoarchitectonic boundaries from birth to adulthood. We use large deformation registration to propagate neuroanatomical labels from adult to neonatal brain and perform multi-atlas labeling based on accurate prior-based tissue segmentation. We evaluate the repeatability of the labeling by cross-validation with training and testing data. Preliminary results show interesting relationship between the volumes of labels and independent measures of neonatal development and maternal characteristics.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"7 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":"125521552","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.2014.6867820
N. El-Zehiry, A. Wimmer
Rib Unfolding refers to the flattening of the rib cage into a two dimensional image that enables the radiologist to quickly examine all ribs for metastases and fractures without having to scroll through every single image in the CT scans. The rib centerline extraction is the core component of the rib unfolding technology. If the computed centerlines deviate from the true centerlines of the ribs then the unfolding shows the bone cortex or even surrounding tissues, making it impossible to spot lesions inside the ribs. Therefore, this paper presents an interactive system for data driven editing of rib centerlines. The user simply has to place a click point at the center of a rib. From there, the centerline is automatically corrected in both directions until the distance to the old one is below a threshold. We formulate the interactive data driven editing as an energy minimization problem where the new centerline point is calculated as the center of mass of the segmentation mask in the plane orthogonal to the rib centerline, this segmentation mask represent the rib cross section. An automatic tracing scheme is calculated based on the refined centerline point and along the tangent vector to the refined centerlines to get the next orthogonal plane to be segmented. The novelty of this paper is three fold: 1. It defines an intuitive minimal user interaction workflow for the correction of rib centerlines. 2. It uses the data in the neighborhood of the centerline point to formulate the energy minimization problem and obtain a more accurate segmentation. 3. We present a novel re-initialization component to prevent the rib tracing from deviating from the correct solution. The initial point to be corrected is re-initialized based on the neighboring ribs. Quantitative assessment of our method shows that in 85% percent of the cases the rib correction can be perfomed using one or two points.
{"title":"Data driven editing of RIB centerlines","authors":"N. El-Zehiry, A. Wimmer","doi":"10.1109/ISBI.2014.6867820","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867820","url":null,"abstract":"Rib Unfolding refers to the flattening of the rib cage into a two dimensional image that enables the radiologist to quickly examine all ribs for metastases and fractures without having to scroll through every single image in the CT scans. The rib centerline extraction is the core component of the rib unfolding technology. If the computed centerlines deviate from the true centerlines of the ribs then the unfolding shows the bone cortex or even surrounding tissues, making it impossible to spot lesions inside the ribs. Therefore, this paper presents an interactive system for data driven editing of rib centerlines. The user simply has to place a click point at the center of a rib. From there, the centerline is automatically corrected in both directions until the distance to the old one is below a threshold. We formulate the interactive data driven editing as an energy minimization problem where the new centerline point is calculated as the center of mass of the segmentation mask in the plane orthogonal to the rib centerline, this segmentation mask represent the rib cross section. An automatic tracing scheme is calculated based on the refined centerline point and along the tangent vector to the refined centerlines to get the next orthogonal plane to be segmented. The novelty of this paper is three fold: 1. It defines an intuitive minimal user interaction workflow for the correction of rib centerlines. 2. It uses the data in the neighborhood of the centerline point to formulate the energy minimization problem and obtain a more accurate segmentation. 3. We present a novel re-initialization component to prevent the rib tracing from deviating from the correct solution. The initial point to be corrected is re-initialized based on the neighboring ribs. Quantitative assessment of our method shows that in 85% percent of the cases the rib correction can be perfomed using one or two points.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"8 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":"128336048","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.2014.6867826
Lei Wang, M. Harz, T. Böhler, B. Platel, A. Homeyer, H. Hahn
Diagnosis of breast nonmass lesions, most notably ductal carcinoma in situ, is challenging. Recent studies show that dynamic contrast enhanced MRI achieves high sensitivity in diagnosis of nonmass lesions. Unlike successfully applied to diagnose mass lesions, particularly kinetic features are reported to be less effective in discriminating nonmass lesions. It is even difficult for human observers to differentiate nonmass lesions against the enhancing parenchymal or benign lesions due to their sometimes similar morphology and contrast kinetics. Towards an automated computer-aided diagnosis system of nonmass lesions, we implemented an extendable and completely automated framework that is efficient and modularized, aiming to discriminate detected suspicious regions into malignant and benign. The entire framework consists of five sequentially executed modules: motion correction, segmentation of breast regions, detection of suspicious regions, feature extraction, and knowledge-based analysis of suspicious regions. A preliminary test was performed on a data set collecting 162 nonmass lesions extracted from 67 patients, which achieved an area under ROC curve value of 0.74 for malignant lesions.
{"title":"A robust and extendable framework towards fully automated diagnosis of nonmass lesions in breast DCE-MRI","authors":"Lei Wang, M. Harz, T. Böhler, B. Platel, A. Homeyer, H. Hahn","doi":"10.1109/ISBI.2014.6867826","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867826","url":null,"abstract":"Diagnosis of breast nonmass lesions, most notably ductal carcinoma in situ, is challenging. Recent studies show that dynamic contrast enhanced MRI achieves high sensitivity in diagnosis of nonmass lesions. Unlike successfully applied to diagnose mass lesions, particularly kinetic features are reported to be less effective in discriminating nonmass lesions. It is even difficult for human observers to differentiate nonmass lesions against the enhancing parenchymal or benign lesions due to their sometimes similar morphology and contrast kinetics. Towards an automated computer-aided diagnosis system of nonmass lesions, we implemented an extendable and completely automated framework that is efficient and modularized, aiming to discriminate detected suspicious regions into malignant and benign. The entire framework consists of five sequentially executed modules: motion correction, segmentation of breast regions, detection of suspicious regions, feature extraction, and knowledge-based analysis of suspicious regions. A preliminary test was performed on a data set collecting 162 nonmass lesions extracted from 67 patients, which achieved an area under ROC curve value of 0.74 for malignant lesions.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"183 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":"124629550","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.2014.6867922
Xinran Liu, Ping Zhou, Tongjing Zhu
The three-dimensional (3D) imaging technique based on phase measuring profilometry has numerous advantages in comparison with other structured light techniques. Measurement accuracy is often used as the primary performance index to evaluate three-dimensional imaging systems. To obtain satisfactory accuracy, the error sources are analyzed and the following three methods related to three aspects of the error source are proposed in this paper. Gamma pre-encoding process is used to eliminate gamma distortion of fringe patterns, and multi-frequency heterodyne principle is applied to reduce the influence of skin color and non-uniform reflectivity of the body surface. In addition, a subpixel contour detection based method is proposed, which accurately extracts the edge of elliptical pattern in calibration images. The experimental results show that the root mean square (RMS) error of the measurement system reaches 0.01 mm, and smoother body surface is obtained.
{"title":"Calibration image pre-processing in 3D body surface measurement system","authors":"Xinran Liu, Ping Zhou, Tongjing Zhu","doi":"10.1109/ISBI.2014.6867922","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867922","url":null,"abstract":"The three-dimensional (3D) imaging technique based on phase measuring profilometry has numerous advantages in comparison with other structured light techniques. Measurement accuracy is often used as the primary performance index to evaluate three-dimensional imaging systems. To obtain satisfactory accuracy, the error sources are analyzed and the following three methods related to three aspects of the error source are proposed in this paper. Gamma pre-encoding process is used to eliminate gamma distortion of fringe patterns, and multi-frequency heterodyne principle is applied to reduce the influence of skin color and non-uniform reflectivity of the body surface. In addition, a subpixel contour detection based method is proposed, which accurately extracts the edge of elliptical pattern in calibration images. The experimental results show that the root mean square (RMS) error of the measurement system reaches 0.01 mm, and smoother body surface is obtained.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"1 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":"127073963","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.2014.6868063
Jing Tang, Yanhua Wang, R. Yao, L. Ying
Incorporating anatomical information obtained by magnetic resonance (MR) imaging has shown its promises to improve the positron emission tomography (PET) imaging quality. In this paper, we propose a novel maximum a posteriori (MAP) PET image reconstruction technique using a sparse prior whose dictionary is learned from the corresponding MR images. Specifically, a PET image is divided into three-dimensional overlapping patches which are expected to be sparsely represented over a redundant dictionary. With the assumption that the PET and MR images of a patient can be sparsified under a common dictionary, the dictionary is learned from the MR image to involve anatomical measurement in PET image reconstruction. The PET image and its sparse representation are updated alternately in the iterative reconstruction process. We evaluated the performance of the proposed method quantitatively, using a realistic simulation with the BrainWeb database phantoms. Noticeable improvement on the noise versus bias tradeoff has been demonstrated in images reconstructed from the proposed method, compared to that from the conventional smoothness MAP method.
{"title":"Sparsity-based PET image reconstruction using MRI learned dictionaries","authors":"Jing Tang, Yanhua Wang, R. Yao, L. Ying","doi":"10.1109/ISBI.2014.6868063","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6868063","url":null,"abstract":"Incorporating anatomical information obtained by magnetic resonance (MR) imaging has shown its promises to improve the positron emission tomography (PET) imaging quality. In this paper, we propose a novel maximum a posteriori (MAP) PET image reconstruction technique using a sparse prior whose dictionary is learned from the corresponding MR images. Specifically, a PET image is divided into three-dimensional overlapping patches which are expected to be sparsely represented over a redundant dictionary. With the assumption that the PET and MR images of a patient can be sparsified under a common dictionary, the dictionary is learned from the MR image to involve anatomical measurement in PET image reconstruction. The PET image and its sparse representation are updated alternately in the iterative reconstruction process. We evaluated the performance of the proposed method quantitatively, using a realistic simulation with the BrainWeb database phantoms. Noticeable improvement on the noise versus bias tradeoff has been demonstrated in images reconstructed from the proposed method, compared to that from the conventional smoothness MAP method.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"1 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":"131045986","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.2014.6867959
Xi Jiang, Jinglei Lv, Dajiang Zhu, Tuo Zhang, Xintao Hu, Lei Guo, Tianming Liu
Studying functional brain activities based on analyzing BOLD signals derived from fMRI data has received significant interest in the neuroimaging field. However, there exists considerable variability of BOLD signals for the corresponding brain region of interest (ROI) across different subjects. To extract more reliable and representative information from BOLD signals, in this paper, we propose a novel stochastic group-wise task BOLD information assessment framework. First, each BOLD signal is characterized as a point process. Second, a state-space generalized linear model is built to integrate group-wise point processes of the corresponding ROI across subjects. Third, a dynamics rate function is proposed to assess the stochastic group-wise BOLD information. Our experimental results based on working memory task fMRI data demonstrate that the resulting stochastic group-wise BOLD information is more accurate and informative than the original BOLD signals in each individual subject in terms of more robust response to the task stimulus.
{"title":"Integrating group-wise functional brain activities via point processes","authors":"Xi Jiang, Jinglei Lv, Dajiang Zhu, Tuo Zhang, Xintao Hu, Lei Guo, Tianming Liu","doi":"10.1109/ISBI.2014.6867959","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867959","url":null,"abstract":"Studying functional brain activities based on analyzing BOLD signals derived from fMRI data has received significant interest in the neuroimaging field. However, there exists considerable variability of BOLD signals for the corresponding brain region of interest (ROI) across different subjects. To extract more reliable and representative information from BOLD signals, in this paper, we propose a novel stochastic group-wise task BOLD information assessment framework. First, each BOLD signal is characterized as a point process. Second, a state-space generalized linear model is built to integrate group-wise point processes of the corresponding ROI across subjects. Third, a dynamics rate function is proposed to assess the stochastic group-wise BOLD information. Our experimental results based on working memory task fMRI data demonstrate that the resulting stochastic group-wise BOLD information is more accurate and informative than the original BOLD signals in each individual subject in terms of more robust response to the task stimulus.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"27 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":"134186092","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.2014.6867994
Ivan Figueroa-Garcia, J. Peyrat, G. Hamarneh, R. Abugharbieh
Robot-assisted partial nephrectomy (RAPN) is a minimally invasive surgery for the treatment of renal cell carcinoma that consists of removing the portion of the kidney that contains the tumor. To plan the resection, surgeons rely on preoperative scans of the patient. However, at surgery time, the shape of abdominal organs differ from these images due to factors such as patient position, insufflation and manipulation with surgical instruments. In this work, we focus on the simulation of kidney deformation due to an external pressure load, e.g. during insufflation, to provide a better estimation of the tumor mass position that is particularly important to plan resection with proper margins. The CT scans of ex vivo lamb kidneys with artificial tumors and fiducials are acquired in absence of external pressure load. From these images, 3D tetra-hedral meshes of kidney parenchyma and tumor, as well as a triangular mesh of the capsule, are extracted and then used along with a soft tissue biomechanical model to simulate deformations under additional external pressure load. A second CT scan of the same kidneys under real pressure load are acquired as a reference to evaluate the advantage of simulating deformations over using the first CT scan without external pressure load. Results show that the biomechanical simulation improves by 29% the tumor localization.
{"title":"Biomechanical kidney model for predicting tumor displacement in the presence of external pressure load","authors":"Ivan Figueroa-Garcia, J. Peyrat, G. Hamarneh, R. Abugharbieh","doi":"10.1109/ISBI.2014.6867994","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867994","url":null,"abstract":"Robot-assisted partial nephrectomy (RAPN) is a minimally invasive surgery for the treatment of renal cell carcinoma that consists of removing the portion of the kidney that contains the tumor. To plan the resection, surgeons rely on preoperative scans of the patient. However, at surgery time, the shape of abdominal organs differ from these images due to factors such as patient position, insufflation and manipulation with surgical instruments. In this work, we focus on the simulation of kidney deformation due to an external pressure load, e.g. during insufflation, to provide a better estimation of the tumor mass position that is particularly important to plan resection with proper margins. The CT scans of ex vivo lamb kidneys with artificial tumors and fiducials are acquired in absence of external pressure load. From these images, 3D tetra-hedral meshes of kidney parenchyma and tumor, as well as a triangular mesh of the capsule, are extracted and then used along with a soft tissue biomechanical model to simulate deformations under additional external pressure load. A second CT scan of the same kidneys under real pressure load are acquired as a reference to evaluate the advantage of simulating deformations over using the first CT scan without external pressure load. Results show that the biomechanical simulation improves by 29% the tumor localization.","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":"133297937","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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