Accelerated partial separable model using dimension-reduced optimization technique for ultra-fast cardiac MRI

TL;DR

This paper introduces a dimension-reduced optimization technique for the partial separable model in cardiac MRI, significantly accelerating image reconstruction while maintaining high image quality, enabling real-time applications.

Contribution

It proposes a novel dimension reduction approach in the PS model optimization, leading to 20-fold faster reconstruction without quality loss.

Findings

Achieves 20-fold faster reconstruction than existing methods.

Maintains superior image quality with robustness to parameter variations.

Enables real-time cardiac MRI imaging.

Abstract

Objective. Imaging dynamic object with high temporal resolution is challenging in magnetic resonance imaging (MRI). Partial separable (PS) model was proposed to improve the imaging quality by reducing the degrees of freedom of the inverse problem. However, PS model still suffers from long acquisition time and even longer reconstruction time. The main objective of this study is to accelerate the PS model, shorten the time required for acquisition and reconstruction, and maintain good image quality simultaneously. Approach. We proposed to fully exploit the dimension reduction property of the PS model, which means implementing the optimization algorithm in subspace. We optimized the data consistency term, and used a Tikhonov regularization term based on the Frobenius norm of temporal difference. The proposed dimension-reduced optimization technique was validated in free-running cardiac MRI. We have performed both retrospective experiments on public dataset and prospective experiments on in-vivo data. The proposed method was compared with four competing algorithms based on PS model, and two non-PS model methods. Main results. The proposed method has robust performance against shortened acquisition time or suboptimal hyper-parameter settings, and achieves superior image quality over all other competing algorithms. The proposed method is 20-fold faster than the widely accepted PS+Sparse method, enabling image reconstruction to be finished in just a few seconds. Significance. Accelerated PS model has the potential to save much time for clinical dynamic MRI examination, and is promising for real-time MRI applications.