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…
EMORe: Motion-Robust 5D MRI Reconstruction via Expectation-Maximization-Guided Binning Correction and Outlier Rejection
Syed M. Arshad, Lee C. Potter, Yingmin Liu +3
We propose EMORe, an adaptive reconstruction method designed to enhance motion robustness in free-running, free-breathing self-gated 5D cardiac magnetic resonance imaging (MRI). Tr…
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…
Accelerated Real-time Cine and Flow under In-magnet Staged Exercise
Preethi Chandrasekaran, Chong Chen, Yingmin Liu +5
Background: Cardiovascular magnetic resonance imaging (CMR) is a well established imaging tool for diagnosing and managing cardiac conditions. The integration of exercise stress wi…