Pub Date : 2021-04-13DOI: 10.1109/ISBI48211.2021.9434004
Herman Verinaz-Jadan, P. Song, Carmel L. Howe, Peter Quicke, Amanda J. Foust, P. Dragotti
Light Field Microscopy (LFM) is an imaging technique that captures 3D spatial information in a single 2D image. LFM is attractive because of its relatively simple implementation and fast acquisition rate. However, classic 3D reconstruction typically suffers from high computational cost, low lateral resolution, and reconstruction artifacts. In this work, we propose a new physics-based learning approach to improve the performance of the reconstruction under realistic conditions, these being lack of training data, background noise, and high data dimensionality. First, we propose a novel description of the system using a linear convolutional neural network. This description is complemented by a method that compacts the number of views of the acquired light field. Then, this model is used to solve the inverse problem under two scenarios. If labelled data is available, we train an end-to-end network that uses the Learned Iterative Shrinkage and Thresholding Algorithm (LISTA). If no labelled data is available, we propose an unsupervised technique that uses only unlabelled data to train LISTA by making use of Wasserstein Generative Adversarial Networks (WGANs). We experimentally show that our approach performs better than classic strategies in terms of artifact reduction and image quality.
{"title":"Deep Learning For Light Field Microscopy Using Physics-Based Models","authors":"Herman Verinaz-Jadan, P. Song, Carmel L. Howe, Peter Quicke, Amanda J. Foust, P. Dragotti","doi":"10.1109/ISBI48211.2021.9434004","DOIUrl":"https://doi.org/10.1109/ISBI48211.2021.9434004","url":null,"abstract":"Light Field Microscopy (LFM) is an imaging technique that captures 3D spatial information in a single 2D image. LFM is attractive because of its relatively simple implementation and fast acquisition rate. However, classic 3D reconstruction typically suffers from high computational cost, low lateral resolution, and reconstruction artifacts. In this work, we propose a new physics-based learning approach to improve the performance of the reconstruction under realistic conditions, these being lack of training data, background noise, and high data dimensionality. First, we propose a novel description of the system using a linear convolutional neural network. This description is complemented by a method that compacts the number of views of the acquired light field. Then, this model is used to solve the inverse problem under two scenarios. If labelled data is available, we train an end-to-end network that uses the Learned Iterative Shrinkage and Thresholding Algorithm (LISTA). If no labelled data is available, we propose an unsupervised technique that uses only unlabelled data to train LISTA by making use of Wasserstein Generative Adversarial Networks (WGANs). We experimentally show that our approach performs better than classic strategies in terms of artifact reduction and image quality.","PeriodicalId":372939,"journal":{"name":"2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121757157","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 : 2021-04-13DOI: 10.1109/ISBI48211.2021.9433946
Yutong Yan, Pierre-Henri Conze, G. Quellec, M. Lamard, B. Cochener, G. Coatrieux
Manually segmenting masses from native mammograms is a very time-consuming and error-prone task. Therefore, an integrated computer-aided diagnosis (CAD) system is required to assist radiologists for automatic and precise breast mass delineation. In this work, we present a two-stage multi-scale pipeline that provides accurate mass delineations from high-resolution full mammograms. First, we propose an extended deep detector integrating a multi-scale fusion strategy for automated mass localization. Second, a convolutional encoder-decoder network using nested and dense skip connections is used to fine-delineate candidate masses. Experiments on public DDSM-CBIS and INbreast datasets reveals strong robustness against the diversity of size, shape and appearance of masses, with an average Dice of 80.44% on INbreast. This shows promising accuracy as an automated full-image mass segmentation system, towards better interaction-free CAD.
{"title":"Two-Stage Multi-Scale Mass Segmentation From Full Mammograms","authors":"Yutong Yan, Pierre-Henri Conze, G. Quellec, M. Lamard, B. Cochener, G. Coatrieux","doi":"10.1109/ISBI48211.2021.9433946","DOIUrl":"https://doi.org/10.1109/ISBI48211.2021.9433946","url":null,"abstract":"Manually segmenting masses from native mammograms is a very time-consuming and error-prone task. Therefore, an integrated computer-aided diagnosis (CAD) system is required to assist radiologists for automatic and precise breast mass delineation. In this work, we present a two-stage multi-scale pipeline that provides accurate mass delineations from high-resolution full mammograms. First, we propose an extended deep detector integrating a multi-scale fusion strategy for automated mass localization. Second, a convolutional encoder-decoder network using nested and dense skip connections is used to fine-delineate candidate masses. Experiments on public DDSM-CBIS and INbreast datasets reveals strong robustness against the diversity of size, shape and appearance of masses, with an average Dice of 80.44% on INbreast. This shows promising accuracy as an automated full-image mass segmentation system, towards better interaction-free CAD.","PeriodicalId":372939,"journal":{"name":"2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122910848","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 : 2021-04-13DOI: 10.1109/ISBI48211.2021.9433765
Golara Javadi, S. Samadi, Sharareh Bayat, Samira Sojoudi, Antonio Hurtado, Silvia D. Chang, Peter C. Black, P. Mousavi, P. Abolmaesumi
Ultrasound imaging is a common tool used in prostate biopsy. The challenges associated with using a systematic and nontargeted approach are the high rate of false negatives and not being patient specific. Intraprostatic pathology information of individuals is not available during the biopsy procedure. Even after histopathology analysis of the biopsy cores, the report only represents a statistical distribution of cancer within the core. Labeling the data based on these noisy labels results in challenges for network training, where networks inevitably overfit to noisy data. To overcome this problem, we argue that it is critical to build a clean dataset. In this paper, we address the challenges associated with using statistical labels and alleviate this issue by taking advantage of confident learning to estimate uncertainty in the data label. Next, we find the label error, clean the labels, and evaluate the clean data by comparing it using a metric based on the involvement of cancer in core.
{"title":"Characterizing The Uncertainty Of Label Noise In Systematic Ultrasound-Guided Prostate Biopsy","authors":"Golara Javadi, S. Samadi, Sharareh Bayat, Samira Sojoudi, Antonio Hurtado, Silvia D. Chang, Peter C. Black, P. Mousavi, P. Abolmaesumi","doi":"10.1109/ISBI48211.2021.9433765","DOIUrl":"https://doi.org/10.1109/ISBI48211.2021.9433765","url":null,"abstract":"Ultrasound imaging is a common tool used in prostate biopsy. The challenges associated with using a systematic and nontargeted approach are the high rate of false negatives and not being patient specific. Intraprostatic pathology information of individuals is not available during the biopsy procedure. Even after histopathology analysis of the biopsy cores, the report only represents a statistical distribution of cancer within the core. Labeling the data based on these noisy labels results in challenges for network training, where networks inevitably overfit to noisy data. To overcome this problem, we argue that it is critical to build a clean dataset. In this paper, we address the challenges associated with using statistical labels and alleviate this issue by taking advantage of confident learning to estimate uncertainty in the data label. Next, we find the label error, clean the labels, and evaluate the clean data by comparing it using a metric based on the involvement of cancer in core.","PeriodicalId":372939,"journal":{"name":"2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129977968","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 : 2021-04-13DOI: 10.1109/ISBI48211.2021.9434162
Ranjeet Ranjan Jha, Hritik Gupta, S. Pathak, W. Schneider, B. V. R. Kumar, A. Bhavsar, A. Nigam
In addition to the more traditional diffusion tensor imaging (DTI), over time, reconstruction techniques like HARDI have been proposed, which have a comparatively higher scanning time due to increased measurements, but are significantly better in the estimation of fiber structures. In order to make HARDI-based analysis faster, we propose an approach to reconstruct more HARDI volumes in q-space. The proposed GAN-based architecture leverages several modules, including a multi-context module, feature inter-dependencies module along-with numerous losses such as L1, adversarial, and total variation loss, to learn the transformation. The method is backed by some encouraging quantitative and visual results.
{"title":"Enhancing HARDI Reconstruction from Undersampled Data Via Multi-Context and Feature Inter-Dependency GAN","authors":"Ranjeet Ranjan Jha, Hritik Gupta, S. Pathak, W. Schneider, B. V. R. Kumar, A. Bhavsar, A. Nigam","doi":"10.1109/ISBI48211.2021.9434162","DOIUrl":"https://doi.org/10.1109/ISBI48211.2021.9434162","url":null,"abstract":"In addition to the more traditional diffusion tensor imaging (DTI), over time, reconstruction techniques like HARDI have been proposed, which have a comparatively higher scanning time due to increased measurements, but are significantly better in the estimation of fiber structures. In order to make HARDI-based analysis faster, we propose an approach to reconstruct more HARDI volumes in q-space. The proposed GAN-based architecture leverages several modules, including a multi-context module, feature inter-dependencies module along-with numerous losses such as L1, adversarial, and total variation loss, to learn the transformation. The method is backed by some encouraging quantitative and visual results.","PeriodicalId":372939,"journal":{"name":"2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130095999","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 : 2021-04-13DOI: 10.1109/ISBI48211.2021.9433990
Gayathri Malamal, Mahesh Raveendranatha Panicker
In the case of typical beamforming in ultrasound imaging, apodization schemes assume a geometric delay driven diffuse reflection model and are not robust for specular reflections. Conversely, the beamforming schemes exclusive to emphasizing specularity suppress the diffuse reflections and speckles. This results in separate beamforming modes for normal tissue scanning and specular reflectors like needles. However, most tissue reflections compose of both diffuse and specular components and a synergistic approach is important. Towards this, a novel approach called reflection tuned apodization (RTA) using coherent plane-wave compounding is proposed, where the apodization window is aligned appropriately by analyzing the reflections from the transmitted plane wave angles for each pixel. A reflection similarity measure is estimated from the plane wave angles to differentiate and characterize the tissue reflections. The beamforming results with the proposed RTA on experimental data show a remarkable improvement in the visibility of specular regions without the suppression of diffuse reflections and speckles compared to the conventional apodization approach.
{"title":"Towards Diffuse Beamforming For Specular Reflectors: A Pixel-Level Reflection Tuned Apodization Scheme For Ultrasound Imaging","authors":"Gayathri Malamal, Mahesh Raveendranatha Panicker","doi":"10.1109/ISBI48211.2021.9433990","DOIUrl":"https://doi.org/10.1109/ISBI48211.2021.9433990","url":null,"abstract":"In the case of typical beamforming in ultrasound imaging, apodization schemes assume a geometric delay driven diffuse reflection model and are not robust for specular reflections. Conversely, the beamforming schemes exclusive to emphasizing specularity suppress the diffuse reflections and speckles. This results in separate beamforming modes for normal tissue scanning and specular reflectors like needles. However, most tissue reflections compose of both diffuse and specular components and a synergistic approach is important. Towards this, a novel approach called reflection tuned apodization (RTA) using coherent plane-wave compounding is proposed, where the apodization window is aligned appropriately by analyzing the reflections from the transmitted plane wave angles for each pixel. A reflection similarity measure is estimated from the plane wave angles to differentiate and characterize the tissue reflections. The beamforming results with the proposed RTA on experimental data show a remarkable improvement in the visibility of specular regions without the suppression of diffuse reflections and speckles compared to the conventional apodization approach.","PeriodicalId":372939,"journal":{"name":"2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127883028","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 : 2021-04-13DOI: 10.1109/ISBI48211.2021.9433804
Wanwen Chen, Kathan Nilesh Mehta, Bhumi Dinesh Bhanushali, J. Galeotti
We present a novel algorithm for needle tracking in ultrasound-guided needle insertion. Most previous research assumes that in ultrasound images the needle is a straight and bright line, but needles can bend due to the interaction with heterogeneous tissue. We utilize a novel weighted RANSAC curve fitting method combined with probabilistic Hough transform to track the curved needle robustly, and the algorithm can additionally utilize external tracking information, such as robotic kinematics, to further improve the tracking accuracy. We compared against classical tracking algorithms and a U-Net model, testing over different needle curvature and tissues. Our proposed algorithm achieves higher accuracy in tip location, shaft fitting, and tip angle. In-vivo porcine experiments with naturally bending short needles also show our method better tracked the tip location.
{"title":"Ultrasound-Based Tracking Of Partially In-Plane, Curved Needles","authors":"Wanwen Chen, Kathan Nilesh Mehta, Bhumi Dinesh Bhanushali, J. Galeotti","doi":"10.1109/ISBI48211.2021.9433804","DOIUrl":"https://doi.org/10.1109/ISBI48211.2021.9433804","url":null,"abstract":"We present a novel algorithm for needle tracking in ultrasound-guided needle insertion. Most previous research assumes that in ultrasound images the needle is a straight and bright line, but needles can bend due to the interaction with heterogeneous tissue. We utilize a novel weighted RANSAC curve fitting method combined with probabilistic Hough transform to track the curved needle robustly, and the algorithm can additionally utilize external tracking information, such as robotic kinematics, to further improve the tracking accuracy. We compared against classical tracking algorithms and a U-Net model, testing over different needle curvature and tissues. Our proposed algorithm achieves higher accuracy in tip location, shaft fitting, and tip angle. In-vivo porcine experiments with naturally bending short needles also show our method better tracked the tip location.","PeriodicalId":372939,"journal":{"name":"2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129152087","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 : 2021-04-13DOI: 10.1109/ISBI48211.2021.9433948
Ignacio Sarasua, Jonwong Lee, C. Wachinger
Geometric deep learning can find representations that are optimal for a given task and therefore improve the performance over pre-defined representations. While current work has mainly focused on point representations, meshes also contain connectivity information and are therefore a more comprehensive characterization of the underlying anatomical surface. In this work, we evaluate four recent geometric deep learning approaches that operate on mesh representations. These approaches can be grouped into template-free and template-based approaches, where the template-based methods need a more elaborate pre-processing step with the definition of a common reference template and correspondences. We compare the different networks for the prediction of Alzheimer’s disease based on the meshes of the hippocampus. Our results show advantages for template-based methods in terms of accuracy, number of learnable parameters, and training speed. While the template creation may be limiting for some applications, neuroimaging has a long history of building templates with automated tools readily available. Overall, working with meshes is more involved than working with simplistic point clouds, but they also offer new avenues for designing geometric deep learning architectures.
{"title":"Geometric Deep Learning on Anatomical Meshes for the Prediction of Alzheimer’s Disease","authors":"Ignacio Sarasua, Jonwong Lee, C. Wachinger","doi":"10.1109/ISBI48211.2021.9433948","DOIUrl":"https://doi.org/10.1109/ISBI48211.2021.9433948","url":null,"abstract":"Geometric deep learning can find representations that are optimal for a given task and therefore improve the performance over pre-defined representations. While current work has mainly focused on point representations, meshes also contain connectivity information and are therefore a more comprehensive characterization of the underlying anatomical surface. In this work, we evaluate four recent geometric deep learning approaches that operate on mesh representations. These approaches can be grouped into template-free and template-based approaches, where the template-based methods need a more elaborate pre-processing step with the definition of a common reference template and correspondences. We compare the different networks for the prediction of Alzheimer’s disease based on the meshes of the hippocampus. Our results show advantages for template-based methods in terms of accuracy, number of learnable parameters, and training speed. While the template creation may be limiting for some applications, neuroimaging has a long history of building templates with automated tools readily available. Overall, working with meshes is more involved than working with simplistic point clouds, but they also offer new avenues for designing geometric deep learning architectures.","PeriodicalId":372939,"journal":{"name":"2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127828841","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 : 2021-04-13DOI: 10.1109/ISBI48211.2021.9434145
Priyanka S. Rana, E. Meijering, A. Sowmya, Yang Song
In this paper, we present a multi-label classification pipeline and a novel feature descriptor for the protein subcellular localisation. The challenge here is the development of a computational model that can classify multi-site proteins on a highly imbalanced dataset with a long-tail distribution and multi-label images. To address this challenge, we design a Location-Sorted Random Projections feature descriptor to represent image intensity and gradient of the protein of interest in reference to the correlated cellular region. Multilabel Synthetic Minority Over-sampling Technique is optimised to generate synthetic features with labels to handle class imbalance. Our method achieves the state-of-the-art performance on a large-scale public dataset and demonstrates excellent performance for the minority classes.
{"title":"Multi-Label Classification Based On Subcellular Region-Guided Feature Description For Protein Localisation","authors":"Priyanka S. Rana, E. Meijering, A. Sowmya, Yang Song","doi":"10.1109/ISBI48211.2021.9434145","DOIUrl":"https://doi.org/10.1109/ISBI48211.2021.9434145","url":null,"abstract":"In this paper, we present a multi-label classification pipeline and a novel feature descriptor for the protein subcellular localisation. The challenge here is the development of a computational model that can classify multi-site proteins on a highly imbalanced dataset with a long-tail distribution and multi-label images. To address this challenge, we design a Location-Sorted Random Projections feature descriptor to represent image intensity and gradient of the protein of interest in reference to the correlated cellular region. Multilabel Synthetic Minority Over-sampling Technique is optimised to generate synthetic features with labels to handle class imbalance. Our method achieves the state-of-the-art performance on a large-scale public dataset and demonstrates excellent performance for the minority classes.","PeriodicalId":372939,"journal":{"name":"2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131273566","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 : 2021-04-13DOI: 10.1109/ISBI48211.2021.9434080
Angela P. Cuadros, Carlos M. Restrepo, P. Noel
This paper introduces a single-scan dual-energy coded aperture computed tomography system that enables material characterization at a reduced exposure level. Rapid kVp switching with a single-static block/unblock coded aperture relies on coded illumination with a plurality of X-ray spectra created by the kVp switching. Based on the tensor representation of the projection data, an algorithm to estimate the missing measurements in the tensor is proposed. This results in a full set of synthesized measurements that can be used with filtered back-projection or iterative reconstruction algorithms to accurately reconstruct the object in each energy channel. Simulation results validate the effectiveness of the proposed cost-effective solution to attain material characterization in low-dose dual-energy CT.
{"title":"Low-Dose Dual KVP Switching Using A Static Coded Aperture","authors":"Angela P. Cuadros, Carlos M. Restrepo, P. Noel","doi":"10.1109/ISBI48211.2021.9434080","DOIUrl":"https://doi.org/10.1109/ISBI48211.2021.9434080","url":null,"abstract":"This paper introduces a single-scan dual-energy coded aperture computed tomography system that enables material characterization at a reduced exposure level. Rapid kVp switching with a single-static block/unblock coded aperture relies on coded illumination with a plurality of X-ray spectra created by the kVp switching. Based on the tensor representation of the projection data, an algorithm to estimate the missing measurements in the tensor is proposed. This results in a full set of synthesized measurements that can be used with filtered back-projection or iterative reconstruction algorithms to accurately reconstruct the object in each energy channel. Simulation results validate the effectiveness of the proposed cost-effective solution to attain material characterization in low-dose dual-energy CT.","PeriodicalId":372939,"journal":{"name":"2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134016849","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 : 2021-04-13DOI: 10.1109/ISBI48211.2021.9434089
J. Beaumont, O. Acosta, P. Raniga, G. Gambarota, J. Fripp
Brain morphometry performed with magnetic resonance (MR) imaging is affected by partial volume (PV) effects when single voxels contain the signal from two different tissues. This paper proposes a generalization of the MP2 RAGE sequence PV estimation model which accounts for transmitted magnetic field $(B1^{+})$ inhomogeneities at 7T. Our simulation experiments demonstrated that the PV estimation error of the proposed model is significantly lower than the error obtained with the same model neglecting $B1^{+}$ inhomogeneities (p<0.0001). The accuracy and precision of the $B1^{+}$ model (acc=92.0%, prec=89.6%) was significantly increased compared to the non $B1^{+}$ model (acc=69.8%, prec=65.4%). This highlights the importance of accounting for $B1^{+}$ inhomogeneities when computing PV on MP2RAGE data, which would otherwise limit the accuracy of brain morphometry at 7T.
{"title":"Towards a generalization of the MP2RAGE partial volume estimation model to account for B1+ inhomogeneities at 7T","authors":"J. Beaumont, O. Acosta, P. Raniga, G. Gambarota, J. Fripp","doi":"10.1109/ISBI48211.2021.9434089","DOIUrl":"https://doi.org/10.1109/ISBI48211.2021.9434089","url":null,"abstract":"Brain morphometry performed with magnetic resonance (MR) imaging is affected by partial volume (PV) effects when single voxels contain the signal from two different tissues. This paper proposes a generalization of the MP2 RAGE sequence PV estimation model which accounts for transmitted magnetic field $(B1^{+})$ inhomogeneities at 7T. Our simulation experiments demonstrated that the PV estimation error of the proposed model is significantly lower than the error obtained with the same model neglecting $B1^{+}$ inhomogeneities (p<0.0001). The accuracy and precision of the $B1^{+}$ model (acc=92.0%, prec=89.6%) was significantly increased compared to the non $B1^{+}$ model (acc=69.8%, prec=65.4%). This highlights the importance of accounting for $B1^{+}$ inhomogeneities when computing PV on MP2RAGE data, which would otherwise limit the accuracy of brain morphometry at 7T.","PeriodicalId":372939,"journal":{"name":"2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133289248","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：