# Joint learning of cartesian undersampling and reconstruction for accelerated MRI

## TL;DR

This paper introduces a joint learning approach for designing MRI acquisition trajectories and reconstruction algorithms simultaneously, leading to improved image quality at accelerated scan speeds.

## Contribution

It presents a novel end-to-end differentiable training method for combined acquisition and reconstruction in MRI, inspired by recent optical imaging advances.

## Key findings

- Learned Cartesian trajectories improve reconstruction quality.
- Joint optimization enhances MRI acceleration capabilities.
- Demonstrates effectiveness at various speed-up rates.

## Abstract

Magnetic Resonance Imaging (MRI) is considered today the golden-standard modality for soft tissues. The long acquisition times, however, make it more prone to motion artifacts as well as contribute to the relatively high costs of this examination. Over the years, multiple studies concentrated on designing reduced measurement schemes and image reconstruction schemes for MRI, however, these problems have been so far addressed separately. On the other hand, recent works in optical computational imaging have demonstrated growing success of the simultaneous learning-based design of the acquisition and reconstruction schemes manifesting significant improvement in the reconstruction quality with a constrained time budget. Inspired by these successes, in this work, we propose to learn accelerated MR acquisition schemes (in the form of Cartesian trajectories) jointly with the image reconstruction operator. To this end, we propose an algorithm for training the combined acquisition-reconstruction pipeline end-to-end in a differentiable way. We demonstrate the significance of using the learned Cartesian trajectories at different speed up rates.