Computational diffusion MRI : MICCAI Workshop最新文献

FASSt : Filtering via Symmetric Autoencoder for Spherical Superficial White Matter Tractography. FASSt：通过对称自动编码器对球形表层白质分层图进行过滤。

Computational diffusion MRI : MICCAI Workshop Pub Date : 2023-10-01 Epub Date: 2024-02-07 DOI: 10.1007/978-3-031-47292-3_12

Yuan Li, Xinyu Nie, Yao Fu, Yonggang Shi

{"title":"FASSt : Filtering via Symmetric Autoencoder for Spherical Superficial White Matter Tractography.","authors":"Yuan Li, Xinyu Nie, Yao Fu, Yonggang Shi","doi":"10.1007/978-3-031-47292-3_12","DOIUrl":"10.1007/978-3-031-47292-3_12","url":null,"abstract":"Superficial white matter (SWM) plays an important role in functioning of the human brain, and it contains a large amount of cortico-cortical connections. However, the difficulties of generating complete and reliable U-fibers make SWM-related analysis lag behind relatively matured Deep white matter (DWM) analysis. With the aid of some newly proposed surface-based SWM tractography algorithms, we have developed a specialized SWM filtering method based on a symmetric variational autoencoder (VAE). In this work, we first demonstrate the advantage of the spherical representation and generate these spherical tracts using the triangular mesh and the registered spherical surface. We then introduce the Filtering via symmetric Autoencoder for Spherical Superficial White Matter tractography (FASSt) framework with a novel symmetric weights module to perform the filtering task in a latent space. We evaluate and compare our method with the state-of-the-art clustering-based method on diffusion MRI data from Human Connectome Project (HCP). The results show that our proposed method outperform these clustering methods and achieves excellent performance in groupwise consistency and topographic regularity.","PeriodicalId":72661,"journal":{"name":"Computational diffusion MRI : MICCAI Workshop","volume":"14328 ","pages":"129-139"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10948089/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140159635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Self Supervised Denoising Diffusion Probabilistic Models for Abdominal DW-MRI. 用于腹部 DW-MRI 的自监督去噪扩散概率模型

Computational diffusion MRI : MICCAI Workshop Pub Date : 2023-10-01 DOI: 10.1007/978-3-031-47292-3_8

Serge Vasylechko, Onur Afacan, Sila Kurugol

引用次数: 0

Automated Mapping of Residual Distortion Severity in Diffusion MRI. 自动绘制弥散核磁共振成像中的残余失真严重程度图

Computational diffusion MRI : MICCAI Workshop Pub Date : 2023-01-01 Epub Date: 2024-02-07 DOI: 10.1007/978-3-031-47292-3_6

Shuo Huang, Lujia Zhong, Yonggang Shi

{"title":"Automated Mapping of Residual Distortion Severity in Diffusion MRI.","authors":"Shuo Huang, Lujia Zhong, Yonggang Shi","doi":"10.1007/978-3-031-47292-3_6","DOIUrl":"10.1007/978-3-031-47292-3_6","url":null,"abstract":"Susceptibility-induced distortion is a common artifact in diffusion MRI (dMRI), which deforms the dMRI locally and poses significant challenges in connectivity analysis. While various methods were proposed to correct the distortion, residual distortions often persist at varying degrees across brain regions and subjects. Generating a voxel-level residual distortion severity map can thus be a valuable tool to better inform downstream connectivity analysis. To fill this current gap in dMRI analysis, we propose a supervised deep-learning network to predict a severity map of residual distortion. The training process is supervised using the structural similarity index measure (SSIM) of the fiber orientation distribution (FOD) in two opposite phase encoding (PE) directions. Only b0 images and related outputs from the distortion correction methods are needed as inputs in the testing process. The proposed method is applicable in large-scale datasets such as the UK Biobank, Adolescent Brain Cognitive Development (ABCD), and other emerging studies that only have complete dMRI data in one PE direction but acquires b0 images in both PEs. In our experiments, we trained the proposed model using the Lifespan Human Connectome Project Aging (HCP-Aging) dataset <math><mo>(</mo><mi>n</mi><mo>=</mo><mn>662</mn><mo>)</mo></math> and apply the trained model to data <math><mo>(</mo><mi>n</mi><mo>=</mo><mn>1330</mn><mo>)</mo></math> from UK Biobank. Our results show low training, validation, and test errors, and the severity map correlates excellently with an FOD integrity measure in both HCP-Aging and UK Biobank data. The proposed method is also highly efficient and can generate the severity map in around 1 second for each subject.","PeriodicalId":72661,"journal":{"name":"Computational diffusion MRI : MICCAI Workshop","volume":"14328 ","pages":"58-69"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10948104/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140159634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stepwise Stochastic Dictionary Adaptation Improves Microstructure Reconstruction with Orientation Distribution Function Fingerprinting. 逐步随机字典自适应改进取向分布函数指纹显微结构重建。

Computational diffusion MRI : MICCAI Workshop Pub Date : 2022-11-01 DOI: 10.1007/978-3-031-21206-2_8

Patryk Filipiak, Timothy Shepherd, Lee Basler, Anthony Zuccolotto, Dimitris G Placantonakis, Walter Schneider, Fernando E Boada, Steven H Baete

{"title":"Stepwise Stochastic Dictionary Adaptation Improves Microstructure Reconstruction with Orientation Distribution Function Fingerprinting.","authors":"Patryk Filipiak, Timothy Shepherd, Lee Basler, Anthony Zuccolotto, Dimitris G Placantonakis, Walter Schneider, Fernando E Boada, Steven H Baete","doi":"10.1007/978-3-031-21206-2_8","DOIUrl":"https://doi.org/10.1007/978-3-031-21206-2_8","url":null,"abstract":"Fitting of the multicompartment biophysical model of white matter is an ill-posed optimization problem. One approach to make it computationally tractable is through Orientation Distribution Function (ODF) Fingerprinting. However, the accuracy of this method relies solely on ODF dictionary generation mechanisms which either sample the microstructure parameters on a multidimensional grid or draw them randomly with a uniform distribution. In this paper, we propose a stepwise stochastic adaptation mechanism to generate ODF dictionaries tailored specifically to the diffusion-weighted images in hand. The results we obtained on a diffusion phantom and in vivo human brain images show that our reconstructed diffusivities are less noisy and the separation of a free water fraction is more pronounced than for the prior (uniform) distribution of ODF dictionaries.","PeriodicalId":72661,"journal":{"name":"Computational diffusion MRI : MICCAI Workshop","volume":"13722 ","pages":"89-100"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9870046/pdf/nihms-1863087.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9819885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Computational Diffusion MRI: 13th International Workshop, CDMRI 2022, Held in Conjunction with MICCAI 2022, Singapore, Singapore, September 22, 2022, Proceedings 计算扩散MRI:第十三届国际研讨会，CDMRI 2022，与MICCAI 2022一起举行，新加坡，新加坡，2022年9月22日，会议录

Computational diffusion MRI : MICCAI Workshop Pub Date : 2022-01-01 DOI: 10.1007/978-3-031-21206-2

引用次数: 1

Lesion Normalization and Supervised Learning in Post-traumatic Seizure Classification with Diffusion MRI. 病灶归一化与监督学习在创伤后癫痫扩散MRI分类中的应用。

Computational diffusion MRI : MICCAI Workshop Pub Date : 2021-10-01 DOI: 10.1007/978-3-030-87615-9_12

Md Navid Akbar, Sebastian Ruf, Marianna La Rocca, Rachael Garner, Giuseppe Barisano, Ruskin Cua, Paul Vespa, Deniz Erdoğmuş, Dominique Duncan

{"title":"Lesion Normalization and Supervised Learning in Post-traumatic Seizure Classification with Diffusion MRI.","authors":"Md Navid Akbar, Sebastian Ruf, Marianna La Rocca, Rachael Garner, Giuseppe Barisano, Ruskin Cua, Paul Vespa, Deniz Erdoğmuş, Dominique Duncan","doi":"10.1007/978-3-030-87615-9_12","DOIUrl":"https://doi.org/10.1007/978-3-030-87615-9_12","url":null,"abstract":"Traumatic brain injury (TBI) is a serious condition, potentially causing seizures and other lifelong disabilities. Patients who experience at least one seizure one week after TBI (late seizure) are at high risk for lifelong complications of TBI, such as post-traumatic epilepsy (PTE). Identifying which TBI patients are at risk of developing seizures remains a challenge. Although magnetic resonance imaging (MRI) methods that probe structural and functional alterations after TBI are promising for biomarker detection, physical deformations following moderate-severe TBI present problems for standard processing of neuroimaging data, complicating the search for biomarkers. In this work, we consider a prediction task to identify which TBI patients will develop late seizures, using fractional anisotropy (FA) features from white matter tracts in diffusion-weighted MRI (dMRI). To understand how best to account for brain lesions and deformations, four preprocessing strategies are applied to dMRI, including the novel application of a lesion normalization technique to dMRI. The pipeline involving the lesion normalization technique provides the best prediction performance, with a mean accuracy of 0.819 and a mean area under the curve of 0.785. Finally, following statistical analyses of selected features, we recommend the dMRI alterations of a certain white matter tract as a potential biomarker.","PeriodicalId":72661,"journal":{"name":"Computational diffusion MRI : MICCAI Workshop","volume":"13006 ","pages":"133-143"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10365258/pdf/nihms-1914721.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9870075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Lesion Normalization and Supervised Learning in Post-Traumatic Seizure Classification with Diffusion MRI 病灶归一化与监督学习在创伤后癫痫扩散MRI分类中的应用

Computational diffusion MRI : MICCAI Workshop Pub Date : 2021-08-10 DOI: 10.1101/2021.08.06.21261733

M. Akbar, S. Ruf, M. Rocca, R. Garner, G. Barisano, R. Cua, P. Vespa, Deniz Erdoğmuş, D. Duncan

{"title":"Lesion Normalization and Supervised Learning in Post-Traumatic Seizure Classification with Diffusion MRI","authors":"M. Akbar, S. Ruf, M. Rocca, R. Garner, G. Barisano, R. Cua, P. Vespa, Deniz Erdoğmuş, D. Duncan","doi":"10.1101/2021.08.06.21261733","DOIUrl":"https://doi.org/10.1101/2021.08.06.21261733","url":null,"abstract":"Traumatic brain injury (TBI) is a serious condition, potentially causing seizures and other lifelong disabilities. Patients who experience at least one seizure one week after TBI (late seizure) are at high risk for lifelong complications of TBI, such as post-traumatic epilepsy (PTE). Identifying which TBI patients are at risk of developing seizures remains a challenge. Although magnetic resonance imaging (MRI) methods that probe structural and functional alterations after TBI are promising for biomarker detection, physical deformations following moderate-severe TBI present problems for standard processing of neuroimaging data, complicating the search for biomarkers. In this work, we consider a prediction task to identify which TBI patients will develop late seizures, using fractional anisotropy (FA) features from white matter tracts in diffusion-weighted MRI (dMRI). To understand how best to account for brain lesions and deformations, four preprocessing strategies are applied to dMRI, including the novel application of a lesion normalization technique to dMRI. The pipeline involving the lesion normalization technique provides the best prediction performance, with a mean accuracy of 0.819 and a mean area under the curve of 0.785. Finally, following statistical analyses of selected features, we recommend the dMRI alterations of a certain white matter tract as a potential biomarker.","PeriodicalId":72661,"journal":{"name":"Computational diffusion MRI : MICCAI Workshop","volume":"448 1","pages":"133-143"},"PeriodicalIF":0.0,"publicationDate":"2021-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82907834","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}

引用次数: 1

Two Parallel Stages Deep Learning Network for Anterior Visual Pathway Segmentation 两并行阶段深度学习网络前视通路分割

Computational diffusion MRI : MICCAI Workshop Pub Date : 2021-01-01 DOI: 10.1007/978-3-030-73018-5_22

Liang Siqi, Zan Chen, Wenlong Guo, Qingrun Zeng, Yuanjing Feng

引用次数: 7

Beyond Lesion-Load: Tractometry-Based Metrics for Characterizing White Matter Lesions within Fibre Pathways 超越病变负荷:纤维通路中表征白质病变的基于束测法的度量

Computational diffusion MRI : MICCAI Workshop Pub Date : 2021-01-01 DOI: 10.1007/978-3-030-73018-5_18

Maxime Chamberland, M. Winter, Thomas A. W. Brice, Derek K. Jones, E. Tallantyre

引用次数: 5

Longitudinal Parcellation of the Infant Cortex Using Multi-modal Connectome Harmonics 利用多模态连接组谐波对婴儿皮层进行纵向分割

Computational diffusion MRI : MICCAI Workshop Pub Date : 2021-01-01 DOI: 10.1007/978-3-030-73018-5_20

H. Taylor, Sahar Ahmad, Ye Wu, Khoi Minh Huynh, Zhen Zhou, Zhengwang Wu, Weili Lin, Li Wang, Gang Li, Han Zhang, P. Yap

引用次数: 0