most citedMotion correction in MRI using deep learning and a novel hybrid loss function

3 citations · 3 across the 2 of their papers we have counts for

collaborators

2 papers

eess.IV2022★ 3 cited

Motion correction in MRI using deep learning and a novel hybrid loss function

Lei Zhang, Xiaoke Wang, Michael Rawson +6

Purpose To develop and evaluate a deep learning-based method (MC-Net) to suppress motion artifacts in brain magnetic resonance imaging (MRI). Methods MC-Net was derived from a UNet…

eess.IV2020

A Learning-from-noise Dilated Wide Activation Network for denoising Arterial Spin Labeling (ASL) Perfusion Images

Danfeng Xie, Yiran Li, Hanlu Yang +3

Arterial spin labeling (ASL) perfusion MRI provides a non-invasive way to quantify cerebral blood flow (CBF) but it still suffers from a low signal-to-noise-ratio (SNR). Using deep…