Self-Supervised Deep Active Accelerated MRI

TL;DR

This paper introduces a self-supervised deep learning framework that actively learns to optimize MRI sampling patterns and reconstruction quality simultaneously, using Monte Carlo tree search to guide the sampling process.

Contribution

It presents a novel joint learning approach for MRI data acquisition and reconstruction, integrating active sampling with deep learning without requiring labeled sampling data.

Findings

Improved MRI reconstruction quality with limited samples

Effective joint training of sampling and reconstruction networks

Utilization of Monte Carlo tree search for optimal sampling patterns

Abstract

We propose to simultaneously learn to sample and reconstruct magnetic resonance images (MRI) to maximize the reconstruction quality given a limited sample budget, in a self-supervised setup. Unlike existing deep methods that focus only on reconstructing given data, thus being passive, we go beyond the current state of the art by considering both the data acquisition and the reconstruction process within a single deep-learning framework. As our network learns to acquire data, the network is active in nature. In order to do so, we simultaneously train two neural networks, one dedicated to reconstruction and the other to progressive sampling, each with an automatically generated supervision signal that links them together. The two supervision signals are created through Monte Carlo tree search (MCTS). MCTS returns a better sampling pattern than what the current sampling network can give and, thus, a better final reconstruction. The sampling network is trained to mimic the MCTS results using the previous sampling network, thus being enhanced. The reconstruction network is trained to give the highest reconstruction quality, given the MCTS sampling pattern. Through this framework, we are able to train the two networks without providing any direct supervision on sampling.