Faster Diffusion Cardiac MRI with Deep Learning-Based Breath Hold Reduction

Michael Tänzer; Pedro Ferreira; Andrew Scott; Zohya Khalique; Maria Dwornik; Dudley Pennell; Guang Yang; Daniel Rueckert; Sonia Nielles-Vallespin

doi:10.1007/978-3-031-12053-4_8

Faster Diffusion Cardiac MRI with Deep Learning-Based Breath Hold Reduction

Michael Tänzer
, Pedro Ferreira
, Andrew Scott
, Zohya Khalique
, Maria Dwornik
, Dudley Pennell
, Guang Yang
, Daniel Rueckert
, Sonia Nielles-Vallespin

Informatics 31 - Chair of Artificial Intelligence in Healthcare and Medicine

Imperial College London
and Royal Brompton and Harefield NHS Trust

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

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.

Original language	English
Title of host publication	Medical Image Understanding and Analysis - 26th Annual Conference, MIUA 2022, Proceedings
Editors	Guang Yang, Angelica Aviles-Rivero, Michael Roberts, Carola-Bibiane Schönlieb
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	101-115
Number of pages	15
ISBN (Print)	9783031120527
DOIs	https://doi.org/10.1007/978-3-031-12053-4_8
State	Published - 2022
Event	26th Annual Conference on Medical Image Understanding and Analysis, MIUA 2022 - Cambridge, United Kingdom Duration: 27 Jul 2022 → 29 Jul 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13413 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	26th Annual Conference on Medical Image Understanding and Analysis, MIUA 2022
Country/Territory	United Kingdom
City	Cambridge
Period	27/07/22 → 29/07/22

Keywords

Cardiac
Deep learning
Diffusion
MRI

Access to Document

10.1007/978-3-031-12053-4_8

Cite this

Tänzer, M., Ferreira, P., Scott, A., Khalique, Z., Dwornik, M., Pennell, D., Yang, G., Rueckert, D., & Nielles-Vallespin, S. (2022). Faster Diffusion Cardiac MRI with Deep Learning-Based Breath Hold Reduction. In G. Yang, A. Aviles-Rivero, M. Roberts, & C.-B. Schönlieb (Eds.), Medical Image Understanding and Analysis - 26th Annual Conference, MIUA 2022, Proceedings (pp. 101-115). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13413 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-12053-4_8

Tänzer, Michael ; Ferreira, Pedro ; Scott, Andrew et al. / Faster Diffusion Cardiac MRI with Deep Learning-Based Breath Hold Reduction. Medical Image Understanding and Analysis - 26th Annual Conference, MIUA 2022, Proceedings. editor / Guang Yang ; Angelica Aviles-Rivero ; Michael Roberts ; Carola-Bibiane Schönlieb. Springer Science and Business Media Deutschland GmbH, 2022. pp. 101-115 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

keywords = "Cardiac, Deep learning, Diffusion, MRI",

author = "Michael T{\"a}nzer and Pedro Ferreira and Andrew Scott and Zohya Khalique and Maria Dwornik and Dudley Pennell and Guang Yang and Daniel Rueckert and Sonia Nielles-Vallespin",

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Tänzer, M, Ferreira, P, Scott, A, Khalique, Z, Dwornik, M, Pennell, D, Yang, G, Rueckert, D & Nielles-Vallespin, S 2022, Faster Diffusion Cardiac MRI with Deep Learning-Based Breath Hold Reduction. in G Yang, A Aviles-Rivero, M Roberts & C-B Schönlieb (eds), Medical Image Understanding and Analysis - 26th Annual Conference, MIUA 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13413 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 101-115, 26th Annual Conference on Medical Image Understanding and Analysis, MIUA 2022, Cambridge, United Kingdom, 27/07/22. https://doi.org/10.1007/978-3-031-12053-4_8

Faster Diffusion Cardiac MRI with Deep Learning-Based Breath Hold Reduction. / Tänzer, Michael; Ferreira, Pedro; Scott, Andrew et al.
Medical Image Understanding and Analysis - 26th Annual Conference, MIUA 2022, Proceedings. ed. / Guang Yang; Angelica Aviles-Rivero; Michael Roberts; Carola-Bibiane Schönlieb. Springer Science and Business Media Deutschland GmbH, 2022. p. 101-115 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13413 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Faster Diffusion Cardiac MRI with Deep Learning-Based Breath Hold Reduction

AU - Tänzer, Michael

AU - Ferreira, Pedro

AU - Scott, Andrew

AU - Khalique, Zohya

AU - Dwornik, Maria

AU - Pennell, Dudley

AU - Yang, Guang

AU - Rueckert, Daniel

AU - Nielles-Vallespin, Sonia

PY - 2022

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N2 - 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.

AB - 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.

KW - Cardiac

KW - Deep learning

KW - Diffusion

KW - MRI

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DO - 10.1007/978-3-031-12053-4_8

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AN - SCOPUS:85135942532

SN - 9783031120527

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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EP - 115

BT - Medical Image Understanding and Analysis - 26th Annual Conference, MIUA 2022, Proceedings

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A2 - Aviles-Rivero, Angelica

A2 - Roberts, Michael

A2 - Schönlieb, Carola-Bibiane

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Tänzer M, Ferreira P, Scott A, Khalique Z, Dwornik M, Pennell D et al. Faster Diffusion Cardiac MRI with Deep Learning-Based Breath Hold Reduction. In Yang G, Aviles-Rivero A, Roberts M, Schönlieb CB, editors, Medical Image Understanding and Analysis - 26th Annual Conference, MIUA 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 101-115. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-12053-4_8