Computational diffusion MRI : MICCAI Workshop最新文献

Progressive axonal degeneration in white matter pathways traversing peritumoral penumbra in frontotemporal glioma. 额颞叶胶质瘤中穿越瘤周半暗带的白质通路的进行性轴突变性。

Computational diffusion MRI : MICCAI Workshop Pub Date : 2026-01-01 Epub Date: 2026-01-02 DOI: 10.1007/978-3-032-12837-9_3

Patryk Filipiak, Timothy M Shepherd, Dimitris G Placantonakis, Jelle Veraart, Fernando E Boada, Steven H Baete

引用次数: 0

QID²: An Image-Conditioned Diffusion Model for Q-space Up-sampling of DWI Data. QID2: DWI数据q空间上采样的图像条件扩散模型。

Computational diffusion MRI : MICCAI Workshop Pub Date : 2025-01-01 Epub Date: 2025-04-18 DOI: 10.1007/978-3-031-86920-4_11

Zijian Chen, Jueqi Wang, Archana Venkataraman

{"title":"QID2: An Image-Conditioned Diffusion Model for Q-space Up-sampling of DWI Data.","authors":"Zijian Chen, Jueqi Wang, Archana Venkataraman","doi":"10.1007/978-3-031-86920-4_11","DOIUrl":"10.1007/978-3-031-86920-4_11","url":null,"abstract":"We propose an image-conditioned diffusion model to estimate high angular resolution diffusion weighted imaging (DWI) from a low angular resolution acquisition. Our model, which we call QID2, takes as input a set of low angular resolution DWI data and uses this information to estimate the DWI data associated with a target gradient direction. We leverage a U-Net architecture with cross-attention to preserve the positional information of the reference images, further guiding the target image generation. We train and evaluate QID2 on single-shell DWI samples curated from the Human Connectome Project (HCP) dataset. Specifically, we sub-sample the HCP gradient directions to produce low angular resolution DWI data and train QID2 to reconstruct the missing high angular resolution samples. We compare QID2 with two state-of-the-art GAN models. Our results demonstrate that QID2 not only achieves higher-quality generated images, but it consistently outperforms state-of-the-art baseline methods in downstream tensor estimation across multiple metrics and in generalizing to downsampling scenario during testing. Taken together, this study highlights the potential of diffusion models, and QID2 in particular, for q-space up-sampling, thus offering a promising toolkit for clinical and research applications.","PeriodicalId":72661,"journal":{"name":"Computational diffusion MRI : MICCAI Workshop","volume":"15171 ","pages":"119-131"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12122016/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144182272","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

Introducing QuantConn: Overcoming challenging diffusion acquisitions with harmonization. 介绍QuantConn：用协调克服具有挑战性的扩散获取。

Computational diffusion MRI : MICCAI Workshop Pub Date : 2025-01-01 Epub Date: 2025-04-18 DOI: 10.1007/978-3-031-86920-4_15

Nancy R Newlin, Kurt Schilling, Serge Koudoro, Bramsh Qamar Chandio, Praitayini Kanakaraj, Daniel Moyer, Claire E Kelly, Sila Genc, Joseph Yuan-Mou Yang, Ye Wu, Nagesh Adluru, Vishwesh Nath, Sudhir Pathak, Walter Schneider, Anurag Gade, William Consagra, Yogesh Rathi, Tom Hendriks, Anna Vilanova, Maxime Chamberland, Tomasz Pieciak, Dominika Ciupek, Antonio Tristán Vega, Santiago Aja-Fernández, Maciej Malawski, Gani Ouedraogo, Julia Machnio, Paul M Thompson, Neda Jahanshad, Eleftherios Garyfallidis, Bennett Landman

{"title":"Introducing QuantConn: Overcoming challenging diffusion acquisitions with harmonization.","authors":"Nancy R Newlin, Kurt Schilling, Serge Koudoro, Bramsh Qamar Chandio, Praitayini Kanakaraj, Daniel Moyer, Claire E Kelly, Sila Genc, Joseph Yuan-Mou Yang, Ye Wu, Nagesh Adluru, Vishwesh Nath, Sudhir Pathak, Walter Schneider, Anurag Gade, William Consagra, Yogesh Rathi, Tom Hendriks, Anna Vilanova, Maxime Chamberland, Tomasz Pieciak, Dominika Ciupek, Antonio Tristán Vega, Santiago Aja-Fernández, Maciej Malawski, Gani Ouedraogo, Julia Machnio, Paul M Thompson, Neda Jahanshad, Eleftherios Garyfallidis, Bennett Landman","doi":"10.1007/978-3-031-86920-4_15","DOIUrl":"10.1007/978-3-031-86920-4_15","url":null,"abstract":"White matter alterations are increasingly implicated in neurological diseases and their progression. Diffusion-weighted magnetic resonance imaging (DW-MRI) has been included in many international-scale studies to identify alterations in white matter microstructure and connectivity. Yet, quantitative investigation of DW-MRI data is hindered by a lack of consistency due to variations in acquisition protocols, sites, and scanners. Specifically, there is a need to harmonize the preprocessing of DW-MRI datasets to ensure that compatible and reproducible quantitative metrics are derived from each site, including (1) bundle-wise microstructure measures, (2) features of white matter fiber bundles, and (3) connectomics measures. In the MICCAI CDMRI 2023 QuantConn challenge, participants are provided raw data from the same individuals taken with two different acquisition protocols on a single 4 tesla scanner in the same scanning session and asked to preprocess the data in order to minimize acquisition differences while retaining biological variation. Here, we outline the testing framework, provide baseline pre-harmonized results, and discuss the learning implications of this challenge.","PeriodicalId":72661,"journal":{"name":"Computational diffusion MRI : MICCAI Workshop","volume":"15171 ","pages":"164-174"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12734758/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145835403","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

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

Diffusion phantom study of fiber crossings at varied angles reconstructed with ODF-Fingerprinting. 用odf指纹法重建不同角度纤维交叉点的扩散模体研究。

Computational diffusion MRI : MICCAI Workshop Pub Date : 2023-01-01 Epub Date: 2024-02-07 DOI: 10.1007/978-3-031-47292-3_3

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

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

A Unified Learning Model for Estimating Fiber Orientation Distribution Functions on Heterogeneous Multi-shell Diffusion-Weighted MRI. 非均质多壳扩散加权MRI上纤维取向分布函数估计的统一学习模型。

Computational diffusion MRI : MICCAI Workshop Pub Date : 2023-01-01 Epub Date: 2024-02-07 DOI: 10.1007/978-3-031-47292-3_2

Tianyuan Yao, Nancy Newlin, Praitayini Kanakaraj, Vishwesh Nath, Leon Y Cai, Karthik Ramadass, Kurt Schilling, Bennett A Landman, Yuankai Huo

{"title":"A Unified Learning Model for Estimating Fiber Orientation Distribution Functions on Heterogeneous Multi-shell Diffusion-Weighted MRI.","authors":"Tianyuan Yao, Nancy Newlin, Praitayini Kanakaraj, Vishwesh Nath, Leon Y Cai, Karthik Ramadass, Kurt Schilling, Bennett A Landman, Yuankai Huo","doi":"10.1007/978-3-031-47292-3_2","DOIUrl":"10.1007/978-3-031-47292-3_2","url":null,"abstract":"Diffusion-weighted (DW) MRI measures the direction and scale of the local diffusion process in every voxel through its spectrum in q-space, typically acquired in one or more shells. Recent developments in micro-structure imaging and multi-tissue decomposition have sparked renewed attention to the radial b-value dependence of the signal. Applications in tissue classification and micro-architecture estimation, therefore, require a signal representation that extends over the radial as well as angular domain. Multiple approaches have been proposed that can model the non-linear relationship between the DW-MRI signal and biological microstructure. In the past few years, many deep learning-based methods have been developed towards faster inference speed and higher inter-scan consistency compared with traditional model-based methods (e.g., multi-shell multi-tissue constrained spherical deconvolution). However, a multi-stage learning strategy is typically required since the learning process relies on various middle representations, such as simple harmonic oscillator reconstruction (SHORE) representation. In this work, we present a unified dynamic network with a single-stage spherical convolutional neural network, which allows efficient fiber orientation distribution function (fODF) estimation through heterogeneous multi-shell diffusion MRI sequences. We study the Human Connectome Project (HCP) young adults with test-retest scans. From the experimental results, the proposed single-stage method outperforms prior multi-stage approaches in repeated fODF estimation with shell dropoff and single-shell DW-MRI sequences.","PeriodicalId":72661,"journal":{"name":"Computational diffusion MRI : MICCAI Workshop","volume":"14328 ","pages":"13-22"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12662721/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145650211","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