Faster Diffusion Cardiac MRI with Deep Learning-Based Breath Hold Reduction
Tanzer M, Ferreira P, Scott A, Khalique Z, Dwornik M, Pennell D, Yang G, Rueckert D, Nielles-Vallespin S (2022)
Publication Type: Conference contribution
Publication year: 2022
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 13413 LNCS
Pages Range: 101-115
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Cambridge, GBR
ISBN: 9783031120527
DOI: 10.1007/978-3-031-12053-4_8
Abstract
Diffusion Tensor Cardiac Magnetic Resonance (DT-CMR) enables us to probe the microstructural arrangement of cardiomyocytes within the myocardium in vivo and non-invasively, which no other imaging modality allows. This innovative technology could revolutionise the ability to perform cardiac clinical diagnosis, risk stratification, prognosis and therapy follow-up. However, DT-CMR is currently inefficient with over six minutes needed to acquire a single 2D static image. Therefore, DT-CMR is currently confined to research but not used clinically. We propose to reduce the number of repetitions needed to produce DT-CMR datasets and subsequently de-noise them, decreasing the acquisition time by a linear factor while maintaining acceptable image quality. Our proposed approach, based on Generative Adversarial Networks, Vision Transformers, and Ensemble Learning, performs significantly and considerably better than previous proposed approaches, bringing single breath-hold DT-CMR closer to reality.
Involved external institutions
United Kingdom (GB)How to cite
APA:
Tanzer, M., Ferreira, P., Scott, A., Khalique, Z., Dwornik, M., Pennell, D.,... Nielles-Vallespin, S. (2022). Faster Diffusion Cardiac MRI with Deep Learning-Based Breath Hold Reduction. In Guang Yang, Angelica Aviles-Rivero, Michael Roberts, Carola-Bibiane Schönlieb (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 101-115). Cambridge, GBR: Springer Science and Business Media Deutschland GmbH.
MLA:
Tanzer, Michael, et al. "Faster Diffusion Cardiac MRI with Deep Learning-Based Breath Hold Reduction." Proceedings of the 26th Annual Conference on Medical Image Understanding and Analysis, MIUA 2022, Cambridge, GBR Ed. Guang Yang, Angelica Aviles-Rivero, Michael Roberts, Carola-Bibiane Schönlieb, Springer Science and Business Media Deutschland GmbH, 2022. 101-115.
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