Alternating Learning Approach for Variational Networks and Undersampling Pattern in Parallel MRI Applications

TL;DR

This paper introduces an alternating learning method that optimizes sampling patterns and variational network parameters to enhance accelerated parallel MRI image reconstruction, demonstrating significant RMSE improvements and stable convergence.

Contribution

It presents a novel alternating learning approach that jointly optimizes sampling patterns and variational network parameters for improved MRI reconstruction.

Findings

RMSE improvements of 14.9% to 51.2% over other methods

Stable convergence with similar SPs under different initial conditions

Enhanced image quality in accelerated MRI applications

Abstract

Purpose: To propose an alternating learning approach to learn the sampling pattern (SP) and the parameters of variational networks (VN) in accelerated parallel magnetic resonance imaging (MRI). Methods: The approach alternates between improving the SP, using bias-accelerated subset selection, and improving parameters of the VN, using ADAM with monotonicity verification. The algorithm learns an effective pair: an SP that captures fewer k-space samples generating undersampling artifacts that are removed by the VN reconstruction. The proposed approach was tested for stability and convergence, considering different initial SPs. The quality of the VNs and SPs was compared against other approaches, including joint learning methods and VN learning with fixed variable density Poisson-disc SPs, using two different datasets and different acceleration factors (AF). Results: The root mean squared error (RMSE) improvements ranged from 14.9% to 51.2% considering AF from 2 to 20 in the tested brain and knee joint datasets when compared to the other approaches. The proposed approach has shown stable convergence, obtaining similar SPs with the same RMSE under different initial conditions. Conclusion: The proposed approach was stable and learned effective SPs with the corresponding VN parameters that produce images with better quality than other approaches, improving accelerated parallel MRI applications.