Projection onto Epigraph Sets for Rapid Self-Tuning Compressed Sensing MRI

TL;DR

This paper introduces a fast self-tuning compressed sensing MRI reconstruction method that uses projections onto epigraph sets to efficiently select regularization parameters, significantly reducing computation time while maintaining high image quality.

Contribution

The paper presents a novel self-tuning method for CS MRI that employs projections onto epigraph sets for rapid parameter selection, outperforming traditional line search techniques in speed.

Findings

Achieves nearly tenfold reduction in reconstruction time.

Maintains near-optimal image quality with automatic parameter tuning.

Demonstrates effectiveness across various MRI imaging modalities.

Abstract

The compressed sensing (CS) framework leverages the sparsity of MR images to reconstruct from undersampled acquisitions. CS reconstructions involve one or more regularization parameters that weigh sparsity in transform domains against fidelity to acquired data. While parameter selection is critical for reconstruction quality, the optimal parameters are subject and dataset specific. Thus, commonly practiced heuristic parameter selection generalizes poorly to independent datasets. Recent studies have proposed to tune parameters by estimating the risk of removing significant image coefficients. Line searches are performed across the parameter space to identify the parameter value that minimizes this risk. Although effective, these line searches yield prolonged reconstruction times. Here, we propose a new self-tuning CS method for multi-coil multi-acquisition reconstructions. The proposed method uses computationally efficient projections onto epigraph sets of the $l_1$ and total-variation norms to simultaneously achieve parameter selection and regularization. In vivo demonstrations are provided for balanced steady-state free precession, time-of-flight, and T1-weighted imaging. The proposed method achieves nearly an order of magnitude improvement in computational efficiency over line-search methods while maintaining near-optimal parameter selection.