A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises
SK Zhou, H Greenspan, C Davatzikos… - Proceedings of the …, 2021 - ieeexplore.ieee.org
Since its renaissance, deep learning has been widely used in various medical imaging tasks
and has achieved remarkable success in many medical imaging applications, thereby …
and has achieved remarkable success in many medical imaging applications, thereby …
[HTML][HTML] The sensitivity of diffusion MRI to microstructural properties and experimental factors
Diffusion MRI is a non-invasive technique to study brain microstructure. Differences in the
microstructural properties of tissue, including size and anisotropy, can be represented in the …
microstructural properties of tissue, including size and anisotropy, can be represented in the …
Deep generative medical image harmonization for improving cross‐site generalization in deep learning predictors
Background In the medical imaging domain, deep learning‐based methods have yet to see
widespread clinical adoption, in part due to limited generalization performance across …
widespread clinical adoption, in part due to limited generalization performance across …
Distortion correction of diffusion weighted MRI without reverse phase-encoding scans or field-maps
Diffusion magnetic resonance images may suffer from geometric distortions due to
susceptibility induced off resonance fields, which cause geometric mismatch with anatomical …
susceptibility induced off resonance fields, which cause geometric mismatch with anatomical …
Fiber tractography bundle segmentation depends on scanner effects, vendor effects, acquisition resolution, diffusion sampling scheme, diffusion sensitization, and …
When investigating connectivity and microstructure of white matter pathways of the brain
using diffusion tractography bundle segmentation, it is important to understand potential …
using diffusion tractography bundle segmentation, it is important to understand potential …
Image-encoded biological and non-biological variables may be used as shortcuts in deep learning models trained on multisite neuroimaging data
Objective This work investigates if deep learning (DL) models can classify originating site
locations directly from magnetic resonance imaging (MRI) scans with and without correction …
locations directly from magnetic resonance imaging (MRI) scans with and without correction …
Diffusion mri with machine learning
D Karimi, SK Warfield - Imaging Neuroscience, 2024 - direct.mit.edu
Diffusion-weighted magnetic resonance imaging (dMRI) of the brain offers unique
capabilities including noninvasive probing of tissue microstructure and structural …
capabilities including noninvasive probing of tissue microstructure and structural …
Deep learning reveals untapped information for local white-matter fiber reconstruction in diffusion-weighted MRI
Purpose Diffusion-weighted magnetic resonance imaging (DW-MRI) is of critical importance
for characterizing in-vivo white matter. Models relating microarchitecture to observed DW …
for characterizing in-vivo white matter. Models relating microarchitecture to observed DW …
A Unified Learning Model for Estimating Fiber Orientation Distribution Functions on Heterogeneous Multi-shell Diffusion-Weighted MRI
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 …
in every voxel through its spectrum in q-space, typically acquired in one or more shells …
[HTML][HTML] Convolutional-recurrent neural networks approximate diffusion tractography from T1-weighted MRI and associated anatomical context
Diffusion MRI (dMRI) streamline tractography is the gold-standard for in vivo estimation of
white matter (WM) pathways in the brain. However, the high angular resolution dMRI …
white matter (WM) pathways in the brain. However, the high angular resolution dMRI …