Over the past two decades, medical imaging and diagnostic techniques have gained
immense attraction due to the rapid development in computing, internet, data storage and …

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 …

We introduce and evaluate a post-processing technique for fast denoising of diffusion-
weighted MR images. By exploiting the intrinsic redundancy in diffusion MRI using universal …

Purpose To test if the proposed deep learning based denoising method denoising
convolutional neural networks (DnCNN) with residual learning and multi-channel strategy …

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 …

Abstract Magnetic Resonance Images (MRI) are often contaminated by rician noise at the
acquisition time. This type of noise typically deteriorates the performance of disease …

This work is the result of more than 10 years of research in the area of MRI from a signal and
noise perspective. Our interest has always been to properly model the noise that affects our …

The low-rank matrix approximation (LRMA) is an efficient image denoising method to reduce
additive Gaussian noise. However, the existing low-rank matrix approximation does not …

The worldwide spread of the Coronavirus pandemic and its huge impact challenged medical
and research communities to explore novel approaches for medical diagnosis from medical …

Purpose Multi‐exponential relaxometry is a powerful tool for characterizing tissue, but
generally requires high image signal‐to‐noise ratio (SNR). This work evaluates the use of …