T1/T2 Relaxation Temporal Modelling from Accelerated Acquisitions Using a Latent Transformer

Michael Tänzer, Fanwen Wang, Mengyun Qiao, Wenjia Bai, Daniel Rueckert, Guang Yang, Sonia Nielles-Vallespin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationStatistical Atlases and Computational Models of the Heart. Regular and CMRxRecon Challenge Papers - 14th International Workshop, STACOM 2023, Held in Conjunction with MICCAI 2023, Revised Selected Papers
EditorsOscar Camara, Esther Puyol-Antón, Avan Suinesiaputra, Alistair Young, Maxime Sermesant, Qian Tao, Chengyan Wang
PublisherSpringer Science and Business Media Deutschland GmbH
Pages293-302
Number of pages10
ISBN (Print)9783031524479
DOIs
StatePublished - 2024
Event14th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2023 held in Conjunction with MICCAI 2023 - Vancouver, Canada
Duration: 12 Oct 202312 Oct 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14507 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2023 held in Conjunction with MICCAI 2023
Country/TerritoryCanada
CityVancouver
Period12/10/2312/10/23

Keywords

  • Deep learning
  • MRI reconstruction
  • Quantitative MRI

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