TEAM PILOT -- Learned Feasible Extendable Set of Dynamic MRI Acquisition Trajectories

TL;DR

This paper introduces a deep learning method for dynamic MRI that uses 3D window attention to create flexible, extendable sampling trajectories, significantly improving reconstruction speed and adaptability over existing techniques.

Contribution

It proposes a novel deep compressed sensing approach with 3D window attention for flexible, extendable MRI sampling trajectories, reducing training and inference times.

Findings

Outperforms current state-of-the-art methods in tests with real data.

Reduces training and inference times compared to existing approaches.

Adapts to different temporal dimensions during inference without retraining.

Abstract

Dynamic Magnetic Resonance Imaging (MRI) is a crucial non-invasive method used to capture the movement of internal organs and tissues, making it a key tool for medical diagnosis. However, dynamic MRI faces a major challenge: long acquisition times needed to achieve high spatial and temporal resolution. This leads to higher costs, patient discomfort, motion artifacts, and lower image quality. Compressed Sensing (CS) addresses this problem by acquiring a reduced amount of MR data in the Fourier domain, based on a chosen sampling pattern, and reconstructing the full image from this partial data. While various deep learning methods have been developed to optimize these sampling patterns and improve reconstruction, they often struggle with slow optimization and inference times or are limited to specific temporal dimensions used during training. In this work, we introduce a novel deep-compressed sensing approach that uses 3D window attention and flexible, temporally extendable acquisition trajectories. Our method significantly reduces both training and inference times compared to existing approaches, while also adapting to different temporal dimensions during inference without requiring additional training. Tests with real data show that our approach outperforms current state-of-theart techniques. The code for reproducing all experiments will be made available upon acceptance of the paper.