Pub Date : 2017-04-18DOI: 10.1109/ISBI.2017.7950694
Tzu-Hsi Song, Victor Sanchez, Hesham EIDaly, N. Rajpoot
Automated cell detection is a critical step for a number of computer-assisted pathology related image analysis algorithm. However, automated cell detection is complicated due to the variable cytomorphological and histological factors associated with each cell. In order to efficiently resolve the challenge of automated cell detection, deep learning strategies are widely applied and have recently been shown to be successful in histopathological images. In this paper, we concentrate on bone marrow trephine biopsy images and propose a hybrid deep autoencoder (HDA) network with Curvature Gaussian model for efficient and precise bone marrow hematopoietic stem cell detection via related high-level feature correspondence. The accuracy of our proposed method is up to 94%, outperforming other supervised and unsupervised detection approaches.
{"title":"Hybrid deep autoencoder with Curvature Gaussian for detection of various types of cells in bone marrow trephine biopsy images","authors":"Tzu-Hsi Song, Victor Sanchez, Hesham EIDaly, N. Rajpoot","doi":"10.1109/ISBI.2017.7950694","DOIUrl":"https://doi.org/10.1109/ISBI.2017.7950694","url":null,"abstract":"Automated cell detection is a critical step for a number of computer-assisted pathology related image analysis algorithm. However, automated cell detection is complicated due to the variable cytomorphological and histological factors associated with each cell. In order to efficiently resolve the challenge of automated cell detection, deep learning strategies are widely applied and have recently been shown to be successful in histopathological images. In this paper, we concentrate on bone marrow trephine biopsy images and propose a hybrid deep autoencoder (HDA) network with Curvature Gaussian model for efficient and precise bone marrow hematopoietic stem cell detection via related high-level feature correspondence. The accuracy of our proposed method is up to 94%, outperforming other supervised and unsupervised detection approaches.","PeriodicalId":6547,"journal":{"name":"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)","volume":"1 1","pages":"1040-1043"},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83298042","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 : 2017-04-18DOI: 10.1109/ISBI.2017.7950587
Pengyue Zhang, Fusheng Wang, Yefeng Zheng
Difficulty on collecting annotated medical images leads to lack of enough supervision and makes discrimination tasks challenging. However, raw data, e.g., spatial context information from 3D CT images, even without annotation, may contain rich useful information. In this paper, we exploit spatial context information as a source of supervision to solve discrimination tasks for fine-grained body part recognition with conventional 3D CT and MR volumes. The proposed pipeline consists of two steps: 1) pre-train a convolutional network for an auxiliary task of 2D slices ordering in a self-supervised manner; 2) transfer and fine-tune the pre-trained network for fine-grained body part recognition. Without any use of human annotation in the first stage, the pre-trained network can still outperform CNN trained from scratch on CT as well as M-R data. Moreover, by comparing with pre-trained CNN from ImageNet, we discover that the distance between source and target tasks plays a crucial role in transfer learning. Our experiments demonstrate that our approach can achieve high accuracy with a slice location estimation error of only a few slices on CT and MR data. To the best of our knowledge, our work is the first attempt studying the problem of robust body part recognition at a continuous level.
{"title":"Self supervised deep representation learning for fine-grained body part recognition","authors":"Pengyue Zhang, Fusheng Wang, Yefeng Zheng","doi":"10.1109/ISBI.2017.7950587","DOIUrl":"https://doi.org/10.1109/ISBI.2017.7950587","url":null,"abstract":"Difficulty on collecting annotated medical images leads to lack of enough supervision and makes discrimination tasks challenging. However, raw data, e.g., spatial context information from 3D CT images, even without annotation, may contain rich useful information. In this paper, we exploit spatial context information as a source of supervision to solve discrimination tasks for fine-grained body part recognition with conventional 3D CT and MR volumes. The proposed pipeline consists of two steps: 1) pre-train a convolutional network for an auxiliary task of 2D slices ordering in a self-supervised manner; 2) transfer and fine-tune the pre-trained network for fine-grained body part recognition. Without any use of human annotation in the first stage, the pre-trained network can still outperform CNN trained from scratch on CT as well as M-R data. Moreover, by comparing with pre-trained CNN from ImageNet, we discover that the distance between source and target tasks plays a crucial role in transfer learning. Our experiments demonstrate that our approach can achieve high accuracy with a slice location estimation error of only a few slices on CT and MR data. To the best of our knowledge, our work is the first attempt studying the problem of robust body part recognition at a continuous level.","PeriodicalId":6547,"journal":{"name":"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)","volume":"1 1","pages":"578-582"},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88597039","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 : 2017-04-18DOI: 10.1109/ISBI.2017.7950516
Hui Tang, Mehdi Moradi, Prasanth Prasanna, Hongzhi Wang, T. Syeda-Mahmood
Detection of calcified plaques in coronary arteries is helpful in cardiovascular disease risk assessment. This is often performed by radiologists on computed tomography (CT) images. We work towards an automatic solution for calcium detection in CT images. Most of previous work in this area combines CT and CTA for this purpose to facilitate the localization of the coronary arteries. Given the cost and dose advantages of using only CT scan compared to using both CT and CTA, we propose a solution for automatic calcium assessment in CT. We model the whole chest including all heart chambers and main arteries. Instead of localizing calcium candidates with respect to the coronary artery alone, we assess their position with respect to eight other anatomies, segmented from CT images using joint atlas label fusion methodology. This comprehensive spatial information together with other types of features such as shape, size and texture of each calcium candidate is used with a random forest classifier trained on 104 patients to detect coronary calcification. The results show that our method has a precision of 95.1% and a recall of 89.0% in classifying calcium candidates found based on thresholding. In the patient level, using this method, all the test patients with true calcification were detected as positive, yielding a patient level sensitivity of 100%. Among the test patients without calcification, 44 out of 56 patients resulted in no calcium finding, yielding a patient level specificity of 78.6%. We quantified the whole heart Agatston score for the manual and the automatically detected calcium on the 22 diseased test cases, and found a Pearson correlation coefficient of 0.98. These results show that our proposed framework can reliably detect calcification using CT data.
{"title":"An algorithm for fully automatic detection of calcium in chest CT imaging","authors":"Hui Tang, Mehdi Moradi, Prasanth Prasanna, Hongzhi Wang, T. Syeda-Mahmood","doi":"10.1109/ISBI.2017.7950516","DOIUrl":"https://doi.org/10.1109/ISBI.2017.7950516","url":null,"abstract":"Detection of calcified plaques in coronary arteries is helpful in cardiovascular disease risk assessment. This is often performed by radiologists on computed tomography (CT) images. We work towards an automatic solution for calcium detection in CT images. Most of previous work in this area combines CT and CTA for this purpose to facilitate the localization of the coronary arteries. Given the cost and dose advantages of using only CT scan compared to using both CT and CTA, we propose a solution for automatic calcium assessment in CT. We model the whole chest including all heart chambers and main arteries. Instead of localizing calcium candidates with respect to the coronary artery alone, we assess their position with respect to eight other anatomies, segmented from CT images using joint atlas label fusion methodology. This comprehensive spatial information together with other types of features such as shape, size and texture of each calcium candidate is used with a random forest classifier trained on 104 patients to detect coronary calcification. The results show that our method has a precision of 95.1% and a recall of 89.0% in classifying calcium candidates found based on thresholding. In the patient level, using this method, all the test patients with true calcification were detected as positive, yielding a patient level sensitivity of 100%. Among the test patients without calcification, 44 out of 56 patients resulted in no calcium finding, yielding a patient level specificity of 78.6%. We quantified the whole heart Agatston score for the manual and the automatically detected calcium on the 22 diseased test cases, and found a Pearson correlation coefficient of 0.98. These results show that our proposed framework can reliably detect calcification using CT data.","PeriodicalId":6547,"journal":{"name":"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)","volume":"221 1","pages":"265-269"},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89343344","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 : 2017-04-18DOI: 10.1109/ISBI.2017.7950635
G. Arkesteijn, D. Poot, M. Niestijl, M. Vernooij, W. Niessen, L. Vliet, F. Vos
Purpose: To increase the sensitivity in longitudinal analysis of DW-MRI data with the ball-and-sticks model.
目的:提高球棒模型对DW-MRI数据纵向分析的敏感性。
{"title":"Longitudinal analysis of diffusion-weighted MRI with a ball-and-sticks model","authors":"G. Arkesteijn, D. Poot, M. Niestijl, M. Vernooij, W. Niessen, L. Vliet, F. Vos","doi":"10.1109/ISBI.2017.7950635","DOIUrl":"https://doi.org/10.1109/ISBI.2017.7950635","url":null,"abstract":"Purpose: To increase the sensitivity in longitudinal analysis of DW-MRI data with the ball-and-sticks model.","PeriodicalId":6547,"journal":{"name":"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)","volume":"161 1","pages":"783-786"},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86740565","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 : 2017-04-18DOI: 10.1109/ISBI.2017.7950500
Chi-Hieu Pham, Aurélien Ducournau, Ronan Fablet, F. Rousseau
Example-based single image super-resolution (SR) has recently shown outcomes with high reconstruction performance. Several methods based on neural networks have successfully introduced techniques into SR problem. In this paper, we propose a three-dimensional (3D) convolutional neural network to generate high-resolution (HR) brain image from its input low-resolution (LR) with the help of patches of other HR brain images. Our work demonstrates the need of fitting data and network parameters for 3D brain MRI.
{"title":"Brain MRI super-resolution using deep 3D convolutional networks","authors":"Chi-Hieu Pham, Aurélien Ducournau, Ronan Fablet, F. Rousseau","doi":"10.1109/ISBI.2017.7950500","DOIUrl":"https://doi.org/10.1109/ISBI.2017.7950500","url":null,"abstract":"Example-based single image super-resolution (SR) has recently shown outcomes with high reconstruction performance. Several methods based on neural networks have successfully introduced techniques into SR problem. In this paper, we propose a three-dimensional (3D) convolutional neural network to generate high-resolution (HR) brain image from its input low-resolution (LR) with the help of patches of other HR brain images. Our work demonstrates the need of fitting data and network parameters for 3D brain MRI.","PeriodicalId":6547,"journal":{"name":"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)","volume":"461 1","pages":"197-200"},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77138857","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 : 2017-04-18DOI: 10.1109/ISBI.2017.7950690
Kausik Das, S. Karri, Abhijit Guha Roy, J. Chatterjee, D. Sheet
Histopathology forms the gold standard for confirmed diagnosis of a suspicious hyperplasia being benign or malignant and for its sub-typing. While techniques like whole-slide imaging have enabled computer assisted analysis for exhaustive reporting of the tissue section, it has also given rise to the big-data deluge and the time complexity associated with processing GBs of image data acquired over multiple magnifications. Since preliminary screening of a slide into benign or malignant carried out on the fly during the digitization process can reduce a Pathologist's work load, to devote more time for detailed analysis, slide screening has to be performed on the fly with high sensitivity. We propose a deep convolutional neural network (CNN) based solution, where we analyse images from random number of regions of the tissue section at multiple magnifications without any necessity of view correspondence across magnifications. Further a majority voting based approach is used for slide level diagnosis, i.e., the class posteriori estimate of each views at a particular magnification is obtained from the magnification specific CNN, and subsequently posteriori estimate across random multi-views at multi-magnification are voting filtered to provide a slide level diagnosis. We have experimentally evaluated performance using a patient level 5-folded cross-validation with 58 malignant and 24 benign cases of breast tumors to obtain average accuracy of 94.67 ± 14.60%, sensitivity of 96.00 ± 8.94%, specificity of 92.00 ± 17.85% and F-score of 96.24 ± 5.29% while processing each view in ≈ 10 ms.
{"title":"Classifying histopathology whole-slides using fusion of decisions from deep convolutional network on a collection of random multi-views at multi-magnification","authors":"Kausik Das, S. Karri, Abhijit Guha Roy, J. Chatterjee, D. Sheet","doi":"10.1109/ISBI.2017.7950690","DOIUrl":"https://doi.org/10.1109/ISBI.2017.7950690","url":null,"abstract":"Histopathology forms the gold standard for confirmed diagnosis of a suspicious hyperplasia being benign or malignant and for its sub-typing. While techniques like whole-slide imaging have enabled computer assisted analysis for exhaustive reporting of the tissue section, it has also given rise to the big-data deluge and the time complexity associated with processing GBs of image data acquired over multiple magnifications. Since preliminary screening of a slide into benign or malignant carried out on the fly during the digitization process can reduce a Pathologist's work load, to devote more time for detailed analysis, slide screening has to be performed on the fly with high sensitivity. We propose a deep convolutional neural network (CNN) based solution, where we analyse images from random number of regions of the tissue section at multiple magnifications without any necessity of view correspondence across magnifications. Further a majority voting based approach is used for slide level diagnosis, i.e., the class posteriori estimate of each views at a particular magnification is obtained from the magnification specific CNN, and subsequently posteriori estimate across random multi-views at multi-magnification are voting filtered to provide a slide level diagnosis. We have experimentally evaluated performance using a patient level 5-folded cross-validation with 58 malignant and 24 benign cases of breast tumors to obtain average accuracy of 94.67 ± 14.60%, sensitivity of 96.00 ± 8.94%, specificity of 92.00 ± 17.85% and F-score of 96.24 ± 5.29% while processing each view in ≈ 10 ms.","PeriodicalId":6547,"journal":{"name":"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)","volume":"38 1","pages":"1024-1027"},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86859980","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 : 2017-04-18DOI: 10.1109/ISBI.2017.7950476
Dingna Duan, I. Rekik, Shun-ren Xia, Weili Lin, J. Gilmore, D. Shen, Gang Li
The dynamic development of brain cognition and motor functions during infancy are highly associated with the rapid changes of the convoluted cortical folding. However, little is known about how the cortical folding, which can be characterized on different scales, develops in the first two postnatal years. In this paper, we propose a curvature-based multi-scale method using spherical wavelets to map the complicated longitudinal changes of cortical folding during infancy. Specifically, we first decompose the cortical curvature map, which encodes the cortical folding information, into multiple spatial-frequency scales, and then measure the scale-specific wavelet power at 6 different scales as quantitative indices of cortical folding degree. We apply this method on 219 longitudinal MR images from 73 healthy infants at 0, 1, and 2 years of age. We reveal that the changing patterns of cortical folding are both scale-specific and region-specific. Particularly, at coarser spatial-frequency levels, the majority of the primary folds flatten out, while at finer spatial-frequency levels, the majority of the minor folds become more convoluted. This study provides valuable insights into the longitudinal changes of infant cortical folding.
{"title":"Longitudinal multi-scale mapping of infant cortical folding using spherical wavelets","authors":"Dingna Duan, I. Rekik, Shun-ren Xia, Weili Lin, J. Gilmore, D. Shen, Gang Li","doi":"10.1109/ISBI.2017.7950476","DOIUrl":"https://doi.org/10.1109/ISBI.2017.7950476","url":null,"abstract":"The dynamic development of brain cognition and motor functions during infancy are highly associated with the rapid changes of the convoluted cortical folding. However, little is known about how the cortical folding, which can be characterized on different scales, develops in the first two postnatal years. In this paper, we propose a curvature-based multi-scale method using spherical wavelets to map the complicated longitudinal changes of cortical folding during infancy. Specifically, we first decompose the cortical curvature map, which encodes the cortical folding information, into multiple spatial-frequency scales, and then measure the scale-specific wavelet power at 6 different scales as quantitative indices of cortical folding degree. We apply this method on 219 longitudinal MR images from 73 healthy infants at 0, 1, and 2 years of age. We reveal that the changing patterns of cortical folding are both scale-specific and region-specific. Particularly, at coarser spatial-frequency levels, the majority of the primary folds flatten out, while at finer spatial-frequency levels, the majority of the minor folds become more convoluted. This study provides valuable insights into the longitudinal changes of infant cortical folding.","PeriodicalId":6547,"journal":{"name":"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)","volume":"56 1","pages":"93-96"},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75782398","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 : 2017-04-18DOI: 10.1109/ISBI.2017.7950724
A. T. Zavareh, O. Barajas, S. Hoyos
In this paper, we present a real-time instantaneous phase estimation technique for the calibration of swept source optical coherence tomography (SS-OCT) systems. The proposed algorithm is able to accurately estimate both the amplitude and phase content of a swept source signal. The results are robust in the presence of substantial noise. Furthermore, the resulting phase profile is readily unwrapped, allowing for the generation of a k-linear sampling clock by means of a simple comparison step, which enables the SS-OCT system operator to determine the required number of points for accurate image reconstruction without the need for MZI path length modification. Our Simulations exhibit equivalent results with Hilbert transform based calibration techniques and low computational time rendering it suitable for real time applications.
{"title":"An efficient estimation algorithm for the calibration of low-cost SS-OCT systems","authors":"A. T. Zavareh, O. Barajas, S. Hoyos","doi":"10.1109/ISBI.2017.7950724","DOIUrl":"https://doi.org/10.1109/ISBI.2017.7950724","url":null,"abstract":"In this paper, we present a real-time instantaneous phase estimation technique for the calibration of swept source optical coherence tomography (SS-OCT) systems. The proposed algorithm is able to accurately estimate both the amplitude and phase content of a swept source signal. The results are robust in the presence of substantial noise. Furthermore, the resulting phase profile is readily unwrapped, allowing for the generation of a k-linear sampling clock by means of a simple comparison step, which enables the SS-OCT system operator to determine the required number of points for accurate image reconstruction without the need for MZI path length modification. Our Simulations exhibit equivalent results with Hilbert transform based calibration techniques and low computational time rendering it suitable for real time applications.","PeriodicalId":6547,"journal":{"name":"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)","volume":"44 1","pages":"1169-1172"},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79332835","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 : 2017-04-18DOI: 10.1109/ISBI.2017.7950640
R. Shishegar, Anand A. Joshi, M. Tolcos, D. Walker, L. Johnston
Segmentation of the developing cortical plate from MRI data of the post-mortem fetal brain is highly challenging due to partial volume effects, low contrast, and heterogeneous maturation caused by ongoing myelination processes. We present a new atlas-free method that segments the inner and outer boundaries of the cortical plate in fetal brains by exploiting diffusion-weighted imaging cues and using a cortical thickness constraint. The accuracy of the segmentation algorithm is demonstrated by application to fetal sheep brain MRI data, and is shown to produce results comparable to manual segmentation and more accurate than semi-automatic segmentation.
{"title":"Automatic segmentation of fetal brain using diffusion-weighted imaging cues","authors":"R. Shishegar, Anand A. Joshi, M. Tolcos, D. Walker, L. Johnston","doi":"10.1109/ISBI.2017.7950640","DOIUrl":"https://doi.org/10.1109/ISBI.2017.7950640","url":null,"abstract":"Segmentation of the developing cortical plate from MRI data of the post-mortem fetal brain is highly challenging due to partial volume effects, low contrast, and heterogeneous maturation caused by ongoing myelination processes. We present a new atlas-free method that segments the inner and outer boundaries of the cortical plate in fetal brains by exploiting diffusion-weighted imaging cues and using a cortical thickness constraint. The accuracy of the segmentation algorithm is demonstrated by application to fetal sheep brain MRI data, and is shown to produce results comparable to manual segmentation and more accurate than semi-automatic segmentation.","PeriodicalId":6547,"journal":{"name":"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)","volume":"16 1","pages":"804-807"},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84208482","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 : 2017-04-18DOI: 10.1109/ISBI.2017.7950466
O. Taubmann, M. Unberath, G. Lauritsch, S. Achenbach, A. Maier
Tomographic reconstruction of cardiovascular structures from rotational angiograms acquired with interventional C-arm devices is challenging due to cardiac motion. Gating strategies are widely used to reduce data inconsistency but come at the cost of angular undersampling. We employ a spatio-temporally regularized 4-D reconstruction model, which is solved using a proximal algorithm, to handle the substantial undersampling associated with a strict gating setup. In a numerical phantom study based on the CAVAREV framework, similarity to the ground truth is improved from 82.3% to 87.6%by this approach compared to a state-of-the-art motion compensation algorithm, whereas previous regularized methods evaluated on this phantom achieved results below 80%. We also show first image results for a clinical patient data set.
{"title":"Spatio-temporally regularized 4-D cardiovascular C-arm CT reconstruction using a proximal algorithm","authors":"O. Taubmann, M. Unberath, G. Lauritsch, S. Achenbach, A. Maier","doi":"10.1109/ISBI.2017.7950466","DOIUrl":"https://doi.org/10.1109/ISBI.2017.7950466","url":null,"abstract":"Tomographic reconstruction of cardiovascular structures from rotational angiograms acquired with interventional C-arm devices is challenging due to cardiac motion. Gating strategies are widely used to reduce data inconsistency but come at the cost of angular undersampling. We employ a spatio-temporally regularized 4-D reconstruction model, which is solved using a proximal algorithm, to handle the substantial undersampling associated with a strict gating setup. In a numerical phantom study based on the CAVAREV framework, similarity to the ground truth is improved from 82.3% to 87.6%by this approach compared to a state-of-the-art motion compensation algorithm, whereas previous regularized methods evaluated on this phantom achieved results below 80%. We also show first image results for a clinical patient data set.","PeriodicalId":6547,"journal":{"name":"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)","volume":"26 1","pages":"52-55"},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78309315","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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