2023/09/28 by Fanwen Wang, Michael Tänzer, Wang, Fanwen +11
Medicine · #Advanced MRI Techniques and Applications #Cardiac Imaging and Diagnostics #FOS: Electrical engineering #Medical Imaging Techniques and Applications #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2309.16853
openalex publication_date 2023/09/28 · openalex created_date 2023/10/03 · openalex updated_date 2026/07/28
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.