T1/T2 relaxation temporal modelling from accelerated acquisitions using a Latent Transformer

TL;DR

This paper introduces a deep learning approach using a Latent Transformer to model temporal dependencies in accelerated T1/T2 MRI mapping, resulting in higher fidelity tissue characterization from undersampled data.

Contribution

It presents a novel multi-resolution sequence-to-sequence transformer module integrated into an encoder-decoder for improved temporal modeling in MRI reconstruction.

Findings

Higher fidelity T1/T2 maps from undersampled data

Explicit temporal modeling improves artifact removal

Demonstrates importance of temporal dynamics in MRI reconstruction

Abstract

Quantitative cardiac magnetic resonance T1 and T2 mapping enable myocardial tissue characterisation but the lengthy scan times restrict their widespread clinical application. We propose a deep learning method that incorporates a time dependency Latent Transformer module to model relationships between parameterised time frames for improved reconstruction from undersampled data. The module, implemented as a multi-resolution sequence-to-sequence transformer, is integrated into an encoder-decoder architecture to leverage the inherent temporal correlations in relaxation processes. The presented results for accelerated T1 and T2 mapping show the model recovers maps with higher fidelity by explicit incorporation of time dynamics. This work demonstrates the importance of temporal modelling for artifact-free reconstruction in quantitative MRI.