2 citations · 4 across the 5 of their papers we have counts for
5 papers
MOSAIC: A Self-supervised Dynamic Multi-encoding Reconstruction Framework for 3D Late Gadolinium Enhancement MRI
Muhammad A. Sultan, Yingmin Liu, Katherine Binzel +3
Purpose: To develop and evaluate a self-supervised dynamic reconstruction framework for highly undersampled dual-echo three-dimensional late gadolinium enhancement (3D LGE) MRI. Me…
A multi-dynamic low-rank deep image prior (ML-DIP) for 3D real-time cardiovascular MRI
Chong Chen, Marc Vornehm, Zhenyu Bu +6
Purpose: To develop a reconstruction framework for 3D real-time cine cardiovascular magnetic resonance (CMR) from highly undersampled data without requiring fully sampled training…
An unsupervised method for MRI recovery: Deep image prior with structured sparsity
Muhammad Ahmad Sultan, Chong Chen, Yingmin Liu +3
Objective: To propose and validate an unsupervised MRI reconstruction method that does not require fully sampled k-space data. Materials and Methods: The proposed method, deep imag…
Multi-dynamic deep image prior for cardiac MRI
Marc Vornehm, Chong Chen, Muhammad Ahmad Sultan +4
Cardiovascular magnetic resonance imaging is a powerful diagnostic tool for assessing cardiac structure and function. However, traditional breath-held imaging protocols pose challe…
Deep Image prior with StruCtUred Sparsity (DISCUS) for dynamic MRI reconstruction
Muhammad A. Sultan, Chong Chen, Yingmin Liu +2
High-quality training data are not always available in dynamic MRI. To address this, we propose a self-supervised deep learning method called deep image prior with structured spars…