Non-Learning based Deep Parallel MRI Reconstruction (NLDpMRI)

TL;DR

This paper introduces NLDpMRI, a non-learning based deep parallel MRI reconstruction method that reconstructs images from undersampled k-space data without training on large datasets, improving generalizability across different configurations.

Contribution

The proposed NLDpMRI method eliminates the need for training on large datasets, enabling generalized MRI reconstruction across various undersampling patterns and coil configurations.

Findings

Outperforms GRAPPA in reconstruction quality

Handles different undersampling patterns effectively

Requires only single undersampled data for reconstruction

Abstract

Fast data acquisition in Magnetic Resonance Imaging (MRI) is vastly in demand and scan time directly depends on the number of acquired k-space samples. Recently, the deep learning-based MRI reconstruction techniques were suggested to accelerate MR image acquisition. The most common issues in any deep learning-based MRI reconstruction approaches are generalizability and transferability. For different MRI scanner configurations using these approaches, the network must be trained from scratch every time with new training dataset, acquired under new configurations, to be able to provide good reconstruction performance. Here, we propose a new generalized parallel imaging method based on deep neural networks called NLDpMRI to reduce any structured aliasing ambiguities related to the different k-space undersampling patterns for accelerated data acquisition. Two loss functions including non-regularized and regularized are proposed for parallel MRI reconstruction using deep network optimization and we reconstruct MR images by optimizing the proposed loss functions over the network parameters. Unlike any deep learning-based MRI reconstruction approaches, our method doesn't include any training step that the network learns from a large number of training samples and it only needs the single undersampled multi-coil k-space data for reconstruction. Also, the proposed method can handle k-space data with different undersampling patterns, and the different number of coils. Experimental results show that the proposed method outperforms the current state-of-the-art GRAPPA method and the deep learning-based variational network method.