OUTCOMES: Rapid Under-sampling Optimization achieves up to 50% improvements in reconstruction accuracy for multi-contrast MRI sequences

TL;DR

OUTCOMES is a rapid, data-driven undersampling optimization method for multi-contrast MRI that significantly improves reconstruction accuracy within seconds, enabling real-time clinical application.

Contribution

The paper introduces OUTCOMES, a fast, GPU-accelerated undersampling trajectory optimization technique that outperforms existing methods in speed and accuracy.

Findings

Achieves 30%-50% improvement in reconstruction accuracy.

Optimizes trajectories within 5-10 seconds.

Enables real-time application in clinical MRI scans.

Abstract

Multi-contrast Magnetic Resonance Imaging (MRI) acquisitions from a single scan have tremendous potential to streamline exams and reduce imaging time. However, maintaining clinically feasible scan time necessitates significant undersampling, pushing the limits on compressed sensing and other low-dimensional techniques. During MRI scanning, one of the possible solutions is by using undersampling designs which can effectively improve the acquisition and achieve higher reconstruction accuracy. However, existing undersampling optimization methods are time-consuming and the limited performance prevents their clinical applications. In this paper, we proposed an improved undersampling trajectory optimization scheme to generate an optimized trajectory within seconds and apply it to subsequent multi-contrast MRI datasets on a per-subject basis, where we named it OUTCOMES. By using a data-driven method combined with improved algorithm design, GPU acceleration, and more efficient computation, the proposed method can optimize a trajectory within 5-10 seconds and achieve 30%-50% reconstruction improvement with the same acquisition cost, which makes real-time under-sampling optimization possible for clinical applications.