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Diffusion tractography is routinely used to study white matter architecture and brain connectivity in vivo. A key step for successful tractography of neuronal tracts is the correct identification of tract directions in each voxel. Here we…

White matter structures composed of myelinated axons in the living human brain are primarily studied by diffusion-weighted MRI (dMRI). These long-range projections are typically characterized in a two-step process: dMRI is used to estimate…

Neurons and Cognition · Quantitative Biology 2017-10-09 Matthew Cieslak , Tegan Brennan , Wendy Meiring , Lukas J. Volz , Clint Greene , Alexander Asturias , Subhash Suri , Scott T. Grafton

Diffusion tensor imaging (DTI) plays a key role in analyzing the physical structures of biological tissues, particularly in reconstructing fiber tracts of the human brain in vivo. On the one hand, eigenvalues of diffusion tensors (DTs)…

Applications · Statistics 2013-04-18 Tao Yu , Chunming Zhang , Andrew L. Alexander , Richard J. Davidson

Diffusion models exhibit excellent sample quality, but existing guidance methods often require additional model training or are limited to specific tasks. We revisit guidance in diffusion models from the perspective of variational inference…

Machine Learning · Computer Science 2025-05-27 Kushagra Pandey , Farrin Marouf Sofian , Felix Draxler , Theofanis Karaletsos , Stephan Mandt

Diffusion-weighted magnetic resonance imaging (DW-MRI) is a non-invasive way of imaging white matter tracts in the human brain. DW-MRIs are usually acquired using echo-planar imaging (EPI) with high gradient fields, which could introduce…

Image and Video Processing · Electrical Eng. & Systems 2023-04-04 Zhangxing Bian , Muhan Shao , Aaron Carass , Jerry L. Prince

White matter tractography is an advanced neuroimaging technique that reconstructs the 3D white matter pathways of the brain from diffusion MRI data. It can be framed as a pathfinding problem aiming to infer neural fiber trajectories from…

Computer Vision and Pattern Recognition · Computer Science 2025-09-23 Itzik Waizman , Yakov Gusakov , Itay Benou , Tammy Riklin Raviv

We enable the estimation of the per-axon axial diffusivity from single encoding, strongly diffusion-weighted, pulsed gradient spin echo data. Additionally, we improve the estimation of the per-axon radial diffusivity compared to estimates…

Medical Physics · Physics 2022-07-07 Marco Pizzolato , Erick Jorge Canales-Rodríguez , Mariam Andersson , Tim B. Dyrby

Diffusion magnetic resonance imaging (dMRI) is currently the only tool for noninvasively imaging the brain's white matter tracts. The fiber orientation (FO) is a key feature computed from dMRI for fiber tract reconstruction. Because the…

Computer Vision and Pattern Recognition · Computer Science 2017-05-22 Chuyang Ye , Jerry L. Prince

Multiple sclerosis (MS) is an inflammatory and neurodegenerative disease characterized by diffuse and focal areas of tissue loss. Conventional MRI techniques such as T1-weighted and T2-weighted scans are generally used in the diagnosis and…

Object tracking is a fundamental task in computer vision, requiring the localization of objects of interest across video frames. Diffusion models have shown remarkable capabilities in visual generation, making them well-suited for…

Computer Vision and Pattern Recognition · Computer Science 2024-10-15 Pha Nguyen , Ngan Le , Jackson Cothren , Alper Yilmaz , Khoa Luu

We propose a novel approach to denoising diffusion magnetic resonance images (dMRI) using convolutional neural networks, that exploits the benefits of data acquired at multiple b-values to offset the need for many redundant observations.…

Image and Video Processing · Electrical Eng. & Systems 2024-10-23 Jakub Jurek , Andrzej Materka , Kamil Ludwisiak , Agata Majos , Filip Szczepankiewicz

Diffusion-weighted MR imaging (DWI) is the only method we currently have to measure connections between different parts of the human brain in vivo. To elucidate the structure of these connections, algorithms for tracking bundles of axonal…

Machine Learning · Statistics 2014-12-05 Charles Zheng , Franco Pestilli , Ariel Rokem

Streamline classification is essential to identify anatomically meaningful white matter tracts from diffusion MRI (dMRI) tractography. However, current streamline classification methods rely primarily on the geometric features of the…

Computer Vision and Pattern Recognition · Computer Science 2025-11-25 Haotian Yan , Bocheng Guo , Jianzhong He , Nir A. Sochen , Ofer Pasternak , Lauren J O'Donnell , Fan Zhang

Diffusion MRI (dMRI) provides the ability to reconstruct neuronal fibers in the brain, $\textit{in vivo}$, by measuring water diffusion along angular gradient directions in q-space. High angular resolution diffusion imaging (HARDI) can…

Machine Learning · Statistics 2018-05-30 Evan Schwab , René Vidal , Nicolas Charon

Cross-term spatiotemporal encoding (xSPEN) is a recently introduced imaging approach delivering single-scan 2D NMR images with unprecedented resilience to field inhomogeneities. The method relies on performing a pre-acquisition encoding and…

Medical Physics · Physics 2017-08-25 Eddy Solomon , Gilad Liberman , Zhiyong Zhang , Lucio Frydman

Anisotropic Diffusion is widely used for noise reduction with simultaneous preservation of vascular structures in maximum intensity projected (MIP) angiograms. However, extension to minimum intensity projected (mIP) venograms in…

Computer Vision and Pattern Recognition · Computer Science 2015-01-15 P. K. Akshara , J. S. Paul

Confocal histology provides an opportunity to establish intra-voxel fiber orientation distributions that can be used to quantitatively assess the biological relevance of diffusion weighted MRI models, e.g., constrained spherical…

Water diffusion gives rise to micron-scale sensitivity of diffusion MRI (dMRI) to cellular-level tissue structure. Precision medicine and quantitative imaging depend on uncovering the information content of dMRI and establishing its…

Medical Physics · Physics 2026-02-03 Santiago Coelho , Jenny Chen , Filip Szczepankiewicz , Els Fieremans , Dmitry S. Novikov

Due to recent technological advances, large brain imaging data sets can now be collected. Such data are highly complex so extraction of meaningful information from them remains challenging. Thus, there is an urgent need for statistical…

Applications · Statistics 2021-06-30 Seungyong Hwang , Thomas C. M. Lee , Debashis Paul , Jie Peng