Sampling Pattern Optimization for Joint Acceleration of Multi-contrast MRI using Deep Learning

Abstract

Usage of multiple-acquisition MRI is one field of study that proved its effectiveness and practicality since routine MR scan protocol typically acquires multiple information for the same anatomical structures. In this study, we propose simultaneous optimization of sampling pattern and reconstruction for joint acceleration of multi-contrast MRI. The simultaneous optimization of sampling pattern and reconstruction provided superior performance over single contrast imaging and over single sampling pattern for multi-contrast MRI. The proposed technique can be adopted in routine clinical scan without forcing extra scans during acquisition.