Diffusion MRI (dMRI) provides invaluable information for the study of tissue microstructure
and brain connectivity, but suffers from a range of imaging artifacts that greatly challenge the …

The advent of deep learning has brought a revolutionary transformation to image denoising
techniques. However, the persistent challenge of acquiring noise-clean pairs for supervised …

Diffusion-weighted magnetic resonance imaging (dMRI) is the primary method for
noninvasively studying the organization of white matter in the human brain. Here we …

Patient scans from MRI often suffer from noise, which hampers the diagnostic capability of
such images. As a method to mitigate such artifacts, denoising is largely studied both within …

Background Accessible tools to efficiently detect and segment diffusion abnormalities in
acute strokes are highly anticipated by the clinical and research communities. Methods We …

Diffusion tensor magnetic resonance imaging (DTI) is a widely adopted neuroimaging
method for the in vivo mapping of brain tissue microstructure and white matter tracts …

Single image super-resolution (SISR) has achieved significant performance improvements
due to the deep convolutional neural networks (CNN). However, the deep learning-based …

Deformable image registration is one of the fundamental tasks in medical imaging. Classical
registration algorithms usually require a high computational cost for iterative optimizations …

Diffusion-weighted magnetic resonance imaging (dMRI) measurements and models provide
information about brain connectivity and are sensitive to the physical properties of tissue …

Purpose To assess the robustness and repeatability of intravoxel incoherent motion model
(IVIM) parameter estimation for the diffusion‐weighted MRI in the abdominal organs under …