Physics-Informed Self-supervised Deep Learning Reconstruction for Accelerated First-Pass Perfusion Cardiac MRI

Elena Martín-González*, Ebraham Alskaf, Amedeo Chiribiri, Pablo Casaseca-de-la-Higuera, Carlos Alberola-López, Rita G. Nunes, Teresa Correia

*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

First-pass perfusion cardiac magnetic resonance (FPP-CMR) is becoming an essential non-invasive imaging method for detecting deficits of myocardial blood flow, allowing the assessment of coronary heart disease. Nevertheless, acquisitions suffer from relatively low spatial resolution and limited heart coverage. Compressed sensing (CS) methods have been proposed to accelerate FPP-CMR and achieve higher spatial resolution. However, the long reconstruction times have limited the widespread clinical use of CS in FPP-CMR. Deep learning techniques based on supervised learning have emerged as alternatives for speeding up reconstructions. However, these approaches require fully sampled data for training, which is not possible to obtain, particularly high-resolution FPP-CMR images. Here, we propose a physics-informed self-supervised deep learning FPP-CMR reconstruction approach for accelerating FPP-CMR scans and hence facilitate high spatial resolution imaging. The proposed method provides high-quality FPP-CMR images from 10x undersampled data without using fully sampled reference data.

Original languageEnglish
Title of host publicationMachine Learning for Medical Image Reconstruction - 4th International Workshop, MLMIR 2021, Held in Conjunction with MICCAI 2021, Proceedings
EditorsNandinee Haq, Patricia Johnson, Andreas Maier, Tobias Würfl, Jaejun Yoo
PublisherSpringer Science and Business Media Deutschland GmbH
Pages86-95
Number of pages10
ISBN (Print)9783030885519
DOIs
Publication statusPublished - 2021
Event4th International Workshop on Machine Learning for Medical Image Reconstruction, MLMIR 2021 held in Conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 - Virtual, Online
Duration: 1 Oct 20211 Oct 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12964 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference4th International Workshop on Machine Learning for Medical Image Reconstruction, MLMIR 2021 held in Conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
CityVirtual, Online
Period1/10/20211/10/2021

Keywords

  • Deep learning reconstruction
  • Model-based reconstruction
  • Quantitative perfusion cardiac MRI

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