A Novel Streamline-based diffusion MRI Tractography Registration Method with Probabilistic Keypoint Detection
University of Electronic Science and Technology of China（电子科技大学）；Nanjing University of Science and Technology（南京理工大学）；Harvard Medical School（哈佛医学院）；Brigham and Women’s Hospital（布莱根和妇女医院）
Junyi Wang, Mubai Du, Ye Wu, Yijie Li, William M. Wells, Lauren J. O'Donnell, Fan Zhang
Registration of diffusion MRI tractography is an essential step for analyzing group similarities and variations in the brain's white matter (WM). Streamline-based registration approaches can leverage the 3D geometric information of fiber pathways to enable spatial alignment after registration. Existing methods usually rely on the optimization of the spatial distances to identify the optimal transformation. However, such methods overlook point connectivity patterns within the streamline itself, limiting their ability to identify anatomical correspondences across tractography datasets. In this work, we propose a novel unsupervised approach using deep learning to perform streamline-based dMRI tractography registration. The overall idea is to identify corresponding keypoint pairs across subjects for spatial alignment of tractography datasets. We model tractography as point clouds to leverage the graph connectivity along streamlines. We propose a novel keypoint detection method for streamlines, framed as a probabilistic classification task to identify anatomically consistent correspondences across unstructured streamline sets. In the experiments, we compare several existing methods and show highly effective and efficient tractography registration performance.
DDTracking: A Deep Generative Framework for Diffusion MRI Tractography with Streamline Local-Global Spatiotemporal Modeling
University of Electronic Science and Technology of China（电子科技大学）；Nanjing University of Science and Technology（南京理工大学）；Brigham and Women’s Hospital（布里奇沃特医院）；Harvard Medical School（哈佛医学院）
Hawkes Institute and Department of Computer Science, University College London, UK（霍克斯研究所和计算机科学系，伦敦大学学院，英国）；Department of Maths and Computer Science, University of Catania, Italy（数学和计算机科学系，卡塔尼亚大学，意大利）；AI Centre and Department of Computer Science, University College London, UK（人工智能中心和计算机科学系，伦敦大学学院，英国）
A Novel Dual-Stream Framework for dMRI Tractography Streamline Classification with Joint dMRI and fMRI Data
University of Electronic Science and Technology of China（电子科技大学）；Zhejiang University of Technology（浙江工业大学）；University of Tel Aviv（特拉维夫大学）；Harvard Medical School（哈佛医学院）
TractCloud-FOV: Deep Learning-based Robust Tractography Parcellation in Diffusion MRI with Incomplete Field of View
Harvard Medical School（哈佛医学院）；Harvard University（哈佛大学）；The University of Sydney（悉尼大学）；Massachusetts General Hospital（麻省总医院）；University of Electronic Science and Technology of China（电子科技大学）；Harvard-MIT Health Sciences and Technology（哈佛-麻省理工健康科学与技术部）
Université de Sherbrooke（舍布鲁克大学）；Université Bordeaux（波尔多大学）；CNRS（法国国家科学研究中心）；CEA（法国原子能和替代能源委员会）；Imeka Solutions inc.（Imeka解决方案公司）
Current tractography methods use the local orientation information to propagate streamlines from seed locations. Many such seeds provide streamlines that stop prematurely or fail to map the true white matter pathways because some bundles are "harder-to-track" than others. This results in tractography reconstructions with poor white and gray matter spatial coverage. In this work, we propose a generative, autoencoder-based method, named GESTA (Generative Sampling in Bundle Tractography using Autoencoders), that produces streamlines achieving better spatial coverage. Compared to other deep learni…
School of Computer Science, University of Sydney（悉尼大学计算机科学学院）；School of Computer Science and Engineering, University of New South Wales（新南威尔士大学计算机科学与工程学院）；Center for Morphometric Analysis, Massachusetts General Hospital（麻省总医院形态测量分析中心）
White matter parcellation classifies tractography streamlines into clusters or anatomically meaningful tracts to enable quantification and visualization. Most parcellation methods focus on the deep white matter (DWM), while fewer methods address the superficial white matter (SWM) due to its complexity. We propose a deep-learning-based framework, Superficial White Matter Analysis (SupWMA), that performs an efficient and consistent parcellation of 198 SWM clusters from whole-brain tractography. A point-cloud-based network is modified for our SWM parcellation task, and supervised contrastive lear…
We present DeepTract, a deep-learning framework for estimating white matter fibers orientation and streamline tractography. We adopt a data-driven approach for fiber reconstruction from diffusion weighted images (DWI), which does not assume a specific diffusion model. We use a recurrent neural network for mapping sequences of DWI values into probabilistic fiber orientation distributions. Based on these estimations, our model facilitates both deterministic and probabilistic streamline tractography. We quantitatively evaluate our method using the Tractometer tool, demonstrating competitive perfo…
University of Electronic Science and Technology of China（电子科技大学）；Harvard Medical School（哈佛医学院）；Nuclear Industry 215 Hospital of Shaanxi Province（陕西省核工业二一五医院）；The First Affiliated Hospital, Sun Yat-sen University（中山大学附属第一医院）；Zhejiang University of Technology（浙江工业大学）；University of Sydney（悉尼大学）
The retinogeniculate pathway (RGVP) is responsible for carrying visual information from the retina to the lateral geniculate nucleus. Identification and visualization of the RGVP are important in studying the anatomy of the visual system and can inform treatment of related brain diseases. Diffusion MRI (dMRI) tractography is an advanced imaging method that uniquely enables in vivo mapping of the 3D trajectory of the RGVP. Currently, identification of the RGVP from tractography data relies on expert (manual) selection of tractography streamlines, which is time-consuming, has high clinical and e…
Université Paris-Saclay, CEA, Neurospin（巴黎-萨克雷大学，法国原子能委员会，神经自旋）；Harvard Medical School（哈佛医学院）；University of Sydney（悉尼大学）；University College London (UCL)（伦敦大学学院）
Superficial white matter (SWM) has been less studied than long-range connections despite being of interest to clinical research, andfew tractography parcellation methods have been adapted to SWM. Here, we propose an efficient geometry-based parcellation method (GeoLab) that allows high-performance segmentation of hundreds of short white matter bundles from a subject. This method has been designed for the SWM atlas of EBRAINS European infrastructure, which is composed of 657 bundles. The atlas projection relies on the precomputed statistics of six bundle-specific geometrical properties of atlas…
Bruno Kessler Foundation（布鲁诺·凯斯勒基金会）；University of Trento（特伦托大学）
Diffusion magnetic resonance imaging (dMRI) and tractography provide means to study the anatomical structures within the white matter of the brain. When studying tractography data across subjects, it is usually necessary to align, i.e. to register, tractographies together. This registration step is most often performed by applying the transformation resulting from the registration of other volumetric images (T1, FA). In contrast with registration methods that "transform" tractographies, in this work, we try to find which streamline in one tractography correspond to which streamline in the othe…
The state-of-the-art method for automatically segmenting white matter bundles in diffusion-weighted MRI is tractography in conjunction with streamline cluster selection. This process involves long chains of processing steps which are not only computationally expensive but also complex to setup and tedious with respect to quality control. Direct bundle segmentation methods treat the task as a traditional image segmentation problem. While they so far did not deliver competitive results, they can potentially mitigate many of the mentioned issues. We present a novel supervised approach for direct …
Université de Sherbrooke（舍布鲁克大学）；Univ. Bordeaux（波尔多大学）；CNRS（法国国家科学研究中心）；CEA（法国原子能和替代能源委员会）
Current brain white matter fiber tracking techniques show a number of problems, including: generating large proportions of streamlines that do not accurately describe the underlying anatomy; extracting streamlines that are not supported by the underlying diffusion signal; and under-representing some fiber populations, among others. In this paper, we describe a novel autoencoder-based learning method to filter streamlines from diffusion MRI tractography, and hence, to obtain more reliable tractograms. Our method, dubbed FINTA (Filtering in Tractography using Autoencoders) uses raw, unlabeled tr…
Bundle-specific Tractogram Distribution Estimation Using Higher-order Streamline Differential Equation
College of Information Engineering, Zhejiang University of Technology（浙江工业大学信息工程学院）；Zhejiang Provincial Collaborative Innovation Center for High-end Digital Intelligence Diagnosis and Treatment Equipment（浙江省高端数字智能诊疗装备协同创新中心）；Zhejiang Provincial United Key Laboratory of Embedded Systems（浙江省嵌入式系统联合重点实验室）；Department of Biomedical Engineering, Tsinghua University（清华大学生物医学工程系）
Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）；University of Sydney（悉尼大学）；University of New South Wales（新南威尔士大学）；Massachusetts General Hospital（麻省总医院）
Diffusion MRI tractography is an advanced imaging technique that enables in vivo mapping of the brain's white matter connections. White matter parcellation classifies tractography streamlines into clusters or anatomically meaningful tracts. It enables quantification and visualization of whole-brain tractography. Currently, most parcellation methods focus on the deep white matter (DWM), whereas fewer methods address the superficial white matter (SWM) due to its complexity. We propose a novel two-stage deep-learning-based framework, Superficial White Matter Analysis (SupWMA), that performs an ef…
We present a geometric framework for aligning white matter fiber tracts. By registering fiber tracts between brains, one expects to see overlap of anatomical structures that often provide meaningful comparisons across subjects. However, the geometry of white matter tracts is highly heterogeneous, and finding direct tract-correspondence across multiple individuals remains a challenging problem. We present a novel deformation metric between tracts that allows one to compare tracts while simultaneously obtaining a registration. To accomplish this, fiber tracts are represented by an intrinsic mean…
University of Massachusetts Boston（马萨诸塞大学波士顿分校）；Harvard Medical School（哈佛医学院）；Isomics, Inc.（Isomics公司）
Fiber tracking produces large tractography datasets that are tens of gigabytes in size consisting of millions of streamlines. Such vast amounts of data require formats that allow for efficient storage, transfer, and visualization. We present TRAKO, a new data format based on the Graphics Layer Transmission Format (glTF) that enables immediate graphical and hardware-accelerated processing. We integrate a state-of-the-art compression technique for vertices, streamlines, and attached scalar and property data. We then compare TRAKO to existing tractography storage methods and provide a detailed ev…
Deep Learning-Based Diffusion MRI Tractography: Integrating Spatial and Anatomical Information
School of Biomedical Engineering, Southern Medical University（南方医科大学生物医学工程学院）；School of Computer Science and Technology, Nanjing University of Science and Technology（南京理工大学计算机科学与技术学院）；Shunde Hospital, Southern Medical University（南方医科大学顺德医院）
Reconstructing the somatotopic organization of the corticospinal tract remains a challenge for modern tractography methods
Institution of Information Processing and Automation, Zhejiang University of Technology（浙江工业大学信息处理与自动化研究所）；Brigham and Women’s Hospital, Harvard Medical School（哈佛医学院布莱根妇女医院）；Massachusetts General Hospital, Harvard Medical School（哈佛医学院麻省总医院）；Boston University School of Medicine（波士顿大学医学院）
Fondazione Bruno Kessler（布鲁诺·凯斯勒基金会）；Università di Trento（特伦托大学）
Diffusion magnetic resonance imaging (dMRI) data allow to reconstruct the 3D pathways of axons within the white matter of the brain as a tractography. The analysis of tractographies has drawn attention from the machine learning and pattern recognition communities providing novel challenges such as finding an appropriate representation space for the data. Many of the current learning algorithms require the input to be from a vectorial space. This requirement contrasts with the intrinsic nature of the tractography because its basic elements, called streamlines or tracks, have different lengths a…
DeepMultiConnectome: Deep Multi-Task Prediction of Structural Connectomes Directly from Diffusion MRI Tractography
Eindhoven University of Technology（埃因霍温理工大学）；Brigham and Women’s Hospital（布莱尔妇女医院）；Harvard Medical School（哈佛医学院）；The University of Sydney（悉尼大学）；University of Electronic Science and Technology of China（电子科技大学）；Harvard-MIT Health Sciences and Technology（哈佛-麻省理工健康科学与技术）
FIESTA: Autoencoders for accurate fiber segmentation in tractography
Université de Sherbrooke, Canada（舍布鲁克大学，加拿大）；Sherbrooke Connectivity Imaging Lab (SCIL)（舍布鲁克连接成像实验室（SCIL））；Videos & Images Theory and Analytics Lab (VITAL)（视频与图像理论及分析实验室（VITAL））；Department of Computer Science（计算机科学系）；Department of Radiology（放射科）；Brigham and Women’s Hospital（布莱根妇女医院）；Mass General Brigham/Harvard Medical School, USA（麻省总医院布莱根联合/哈佛医学院，美国）；Centre de Calcul Scientifique（科学计算中心）；Medical Imaging and Neuroinformatic (MINi) Lab（医学成像与神经信息学（MINi）实验室）；Groupe d’Imagerie Neurofonctionnelle (GIN)（神经功能成像小组（GIN））；CNRS, CEA, IMN, GIN, UMR 5293, F-33000 Bordeaux（法国国家科学研究中心、法国原子能委员会、神经影像研究所、神经功能成像小组、5293号联合研究单位，波尔多）；Université de Bordeaux, France（波尔多大学，法国）；Pharma Research and Early Development（制药研究与早期开发）；Neuroscience and Rare Diseases（神经科学与罕见病）；Roche Innovation Center Basel（巴塞尔罗氏创新中心）；F. Hoffmann-La Roche Ltd., Basel, Switzerland（F.霍夫曼-罗氏有限公司，巴塞尔，瑞士）
Automated Mapping the Pathways of Cranial Nerve II, III, V, and VII/VIII: A Multi-Parametric Multi-Stage Diffusion Tractography Atlas
Advanced Interdisciplinary Science and Technology, Zhejiang University of Technology（浙江工业大学先进交叉学科技术学院）；College of Information Engineering, Zhejiang University of Technology（浙江工业大学信息工程学院）；Department of Radiology, Xiangya Hospital, Central South University（中南大学湘雅医院放射科）；Department of Neurosurgery, Capital Medical University Xuanwu Hospital（首都医科大学北京协和医院神经外科）；Department of Neurosurgery, Nuclear Industry 215 Hospital of Shaanxi Province（陕西核工业215医院神经外科）
Global Context Is All You Need for Parallel Efficient Tractography Parcellation
University of Bonn（波恩大学）；Center for X-ray and Nano Science CXNS, DESY（欧洲同步辐射中心X射线与纳米科学中心）；University Hospital Bonn（波恩大学医院）；German Center for Neurodegenerative Diseases (DZNE)（德国神经退行性疾病中心）；Lamarr Institute for Machine Learning and Artificial Intelligence（拉马尔机器学习与人工智能研究院）
Assessing Streamline Plausibility Through Randomized Iterative Spherical-Deconvolution Informed Tractogram Filtering
Saarland University（萨尔大学）；Krembil Research Institute（克伦比尔研究所）；Toronto Western Hospital（多伦多西部医院）；University Health Network（大学健康网络）；KTH Royal Institute of Technology（KTH皇家理工学院）
École de technologie supérieure（高等技术学院）；McGill University（麦吉尔大学）
Diffusion magnetic resonance imaging, a non-invasive tool to infer white matter fiber connections, produces a large number of streamlines containing a wealth of information on structural connectivity. The size of these tractography outputs makes further analyses complex, creating a need for methods to group streamlines into meaningful bundles. In this work, we address this by proposing a set of kernel dictionary learning and sparsity priors based methods. Proposed frameworks include L-0 norm, group sparsity, as well as manifold regularization prior. The proposed methods allow streamlines to be…
Anatomy-guided fiber trajectory distribution estimation for cranial nerves tractography
College of Information Engineering, Zhejiang University of Technology（浙江工业大学信息工程学院）；The Hong Kong University of Science and Technology（香港科技大学）；Nuclear Industry 215 Hospital of Shaanxi Province（陕西省核工业二一五医院）；Capital Medical University Xuanwu Hospital（首都医科大学宣武医院）
ConSEAL: Connectivity-Informed Streamline Endpoint Alignment for Diffeomorphic Cortical Registration
Department of Statistics and Operations Research, The University of North Carolina at Chapel Hill（统计与运筹学系，北卡罗来纳大学教堂山分校）；Department of Psychiatry, University of Rochester（精神病学系，罗切斯特大学）
