Synergistic multi-contrast cardiac magnetic resonance image reconstruction

Haikun Qi; Gastao Cruz; René Botnar; Claudia Prieto

doi:10.1098/rsta.2020.0197

Synergistic multi-contrast cardiac magnetic resonance image reconstruction

Haikun Qi, Gastao Cruz, René Botnar, Claudia Prieto

Biomedical Engineering Department

Research output: Contribution to journal › Review article › peer-review

5 Citations (Scopus)

Abstract

Cardiac magnetic resonance imaging (CMR) is an important tool for the non-invasive diagnosis of a variety of cardiovascular diseases. Parametric mapping with multi-contrast CMR is able to quantify tissue alterations in myocardial disease and promises to improve patient care. However, magnetic resonance imaging is an inherently slow imaging modality, resulting in long acquisition times for parametric mapping which acquires a series of cardiac images with different contrasts for signal fitting or dictionary matching. Furthermore, extra efforts to deal with respiratory and cardiac motion by triggering and gating further increase the scan time. Several techniques have been developed to speed up CMR acquisitions, which usually acquire less data than that required by the Nyquist-Shannon sampling theorem, followed by regularized reconstruction to mitigate undersampling artefacts. Recent advances in CMR parametric mapping speed up CMR by synergistically exploiting spatial-temporal and contrast redundancies. In this article, we will review the recent developments in multi-contrast CMR image reconstruction for parametric mapping with special focus on low-rank and model-based reconstructions. Deep learning-based multi-contrast reconstruction has recently been proposed in other magnetic resonance applications. These developments will be covered to introduce the general methodology. Current technical limitations and potential future directions are discussed. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 1'.

Original language	English
Article number	20200197
Pages (from-to)	20200197
Number of pages	1
Journal	Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Volume	379
Issue number	2200
DOIs	https://doi.org/10.1098/rsta.2020.0197
Publication status	Published - 28 Jun 2021

Keywords

accelerated imaging
cardiac magnetic resonance imaging
multi-contrast imaging
parametric mapping
undersampled reconstruction

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1098/rsta.2020.0197

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title = "Synergistic multi-contrast cardiac magnetic resonance image reconstruction",

abstract = "Cardiac magnetic resonance imaging (CMR) is an important tool for the non-invasive diagnosis of a variety of cardiovascular diseases. Parametric mapping with multi-contrast CMR is able to quantify tissue alterations in myocardial disease and promises to improve patient care. However, magnetic resonance imaging is an inherently slow imaging modality, resulting in long acquisition times for parametric mapping which acquires a series of cardiac images with different contrasts for signal fitting or dictionary matching. Furthermore, extra efforts to deal with respiratory and cardiac motion by triggering and gating further increase the scan time. Several techniques have been developed to speed up CMR acquisitions, which usually acquire less data than that required by the Nyquist-Shannon sampling theorem, followed by regularized reconstruction to mitigate undersampling artefacts. Recent advances in CMR parametric mapping speed up CMR by synergistically exploiting spatial-temporal and contrast redundancies. In this article, we will review the recent developments in multi-contrast CMR image reconstruction for parametric mapping with special focus on low-rank and model-based reconstructions. Deep learning-based multi-contrast reconstruction has recently been proposed in other magnetic resonance applications. These developments will be covered to introduce the general methodology. Current technical limitations and potential future directions are discussed. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 1'.",

keywords = "accelerated imaging, cardiac magnetic resonance imaging, multi-contrast imaging, parametric mapping, undersampled reconstruction",

author = "Haikun Qi and Gastao Cruz and Ren{\'e} Botnar and Claudia Prieto",

note = "Funding Information: Data accessibility. This article does not contain any additional data. Authors{\textquoteright} contributions. H.Q., G.C., R.B. and C.P. devised and wrote the manuscript. Competing interests. We declare we have no competing interests. Funding. The authors acknowledge financial support from EPSRC EP/P001009/1, EP/P032311/1, EPSRC EP/P007619, Wellcome EPSRC Centre for Medical Engineering (grant no. NS/A000049/1) and the Department of health via the National Institute for Health Research (NIHR) comprehensive Biomedical Research Centre award to Guy{\textquoteright}s and St. Thomas{\textquoteright} NHS Foundation Trust. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health. Publisher Copyright: {\textcopyright} 2021 The Author(s). Copyright: Copyright 2021 Elsevier B.V., All rights reserved.",

year = "2021",

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doi = "10.1098/rsta.2020.0197",

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AU - Qi, Haikun

AU - Cruz, Gastao

AU - Botnar, René

AU - Prieto, Claudia

N1 - Funding Information: Data accessibility. This article does not contain any additional data. Authors’ contributions. H.Q., G.C., R.B. and C.P. devised and wrote the manuscript. Competing interests. We declare we have no competing interests. Funding. The authors acknowledge financial support from EPSRC EP/P001009/1, EP/P032311/1, EPSRC EP/P007619, Wellcome EPSRC Centre for Medical Engineering (grant no. NS/A000049/1) and the Department of health via the National Institute for Health Research (NIHR) comprehensive Biomedical Research Centre award to Guy’s and St. Thomas’ NHS Foundation Trust. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health. Publisher Copyright: © 2021 The Author(s). Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021/6/28

Y1 - 2021/6/28

N2 - Cardiac magnetic resonance imaging (CMR) is an important tool for the non-invasive diagnosis of a variety of cardiovascular diseases. Parametric mapping with multi-contrast CMR is able to quantify tissue alterations in myocardial disease and promises to improve patient care. However, magnetic resonance imaging is an inherently slow imaging modality, resulting in long acquisition times for parametric mapping which acquires a series of cardiac images with different contrasts for signal fitting or dictionary matching. Furthermore, extra efforts to deal with respiratory and cardiac motion by triggering and gating further increase the scan time. Several techniques have been developed to speed up CMR acquisitions, which usually acquire less data than that required by the Nyquist-Shannon sampling theorem, followed by regularized reconstruction to mitigate undersampling artefacts. Recent advances in CMR parametric mapping speed up CMR by synergistically exploiting spatial-temporal and contrast redundancies. In this article, we will review the recent developments in multi-contrast CMR image reconstruction for parametric mapping with special focus on low-rank and model-based reconstructions. Deep learning-based multi-contrast reconstruction has recently been proposed in other magnetic resonance applications. These developments will be covered to introduce the general methodology. Current technical limitations and potential future directions are discussed. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 1'.

AB - Cardiac magnetic resonance imaging (CMR) is an important tool for the non-invasive diagnosis of a variety of cardiovascular diseases. Parametric mapping with multi-contrast CMR is able to quantify tissue alterations in myocardial disease and promises to improve patient care. However, magnetic resonance imaging is an inherently slow imaging modality, resulting in long acquisition times for parametric mapping which acquires a series of cardiac images with different contrasts for signal fitting or dictionary matching. Furthermore, extra efforts to deal with respiratory and cardiac motion by triggering and gating further increase the scan time. Several techniques have been developed to speed up CMR acquisitions, which usually acquire less data than that required by the Nyquist-Shannon sampling theorem, followed by regularized reconstruction to mitigate undersampling artefacts. Recent advances in CMR parametric mapping speed up CMR by synergistically exploiting spatial-temporal and contrast redundancies. In this article, we will review the recent developments in multi-contrast CMR image reconstruction for parametric mapping with special focus on low-rank and model-based reconstructions. Deep learning-based multi-contrast reconstruction has recently been proposed in other magnetic resonance applications. These developments will be covered to introduce the general methodology. Current technical limitations and potential future directions are discussed. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 1'.

KW - accelerated imaging

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KW - multi-contrast imaging

KW - parametric mapping

KW - undersampled reconstruction

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JO - Philosophical transactions. Series A, Mathematical, physical, and engineering sciences

JF - Philosophical transactions. Series A, Mathematical, physical, and engineering sciences

IS - 2200

M1 - 20200197

ER -

Synergistic multi-contrast cardiac magnetic resonance image reconstruction

Abstract

Keywords

UN SDGs

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