Reconstruction techniques for accelerating dynamic cardiovascular magnetic resonance imaging

Andrew Phair; René Botnar; Claudia Prieto

doi:10.1016/j.jocmr.2025.101873

Reconstruction techniques for accelerating dynamic cardiovascular magnetic resonance imaging

Andrew Phair, René Botnar, Claudia Prieto

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Abstract

Achieving sufficient spatial and temporal resolution for dynamic applications in cardiovascular magnetic resonance (CMR) imaging is a challenging task due to the inherently slow nature of CMR. In order to accelerate scans and allow improved resolution, much research over the past three decades has been aimed at developing innovative reconstruction methods that can yield high-quality images from reduced amounts of k-space data. In this review, we describe the evolution of these reconstruction techniques, with a particular focus on those advances that have shifted the dynamic reconstruction paradigm as it relates to CMR. This review discusses and explains the fundamental ideas behind the success of modern reconstruction algorithms, including parallel imaging, spatio-temporal redundancies, compressed sensing, low-rank methods and machine learning.

Original language	English
Article number	101873
Pages (from-to)	101873
Journal	Journal of Cardiovascular Magnetic Resonance
Volume	27
Issue number	1
DOIs	https://doi.org/10.1016/j.jocmr.2025.101873
Publication status	E-pub ahead of print - 6 Mar 2025

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10.1016/j.jocmr.2025.101873

1-s2.0-S1097664725000353-mainAccepted author manuscript, 30.3 MBLicence: Unspecified

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abstract = "Achieving sufficient spatial and temporal resolution for dynamic applications in cardiovascular magnetic resonance (CMR) imaging is a challenging task due to the inherently slow nature of CMR. In order to accelerate scans and allow improved resolution, much research over the past three decades has been aimed at developing innovative reconstruction methods that can yield high-quality images from reduced amounts of k-space data. In this review, we describe the evolution of these reconstruction techniques, with a particular focus on those advances that have shifted the dynamic reconstruction paradigm as it relates to CMR. This review discusses and explains the fundamental ideas behind the success of modern reconstruction algorithms, including parallel imaging, spatio-temporal redundancies, compressed sensing, low-rank methods and machine learning.",

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AB - Achieving sufficient spatial and temporal resolution for dynamic applications in cardiovascular magnetic resonance (CMR) imaging is a challenging task due to the inherently slow nature of CMR. In order to accelerate scans and allow improved resolution, much research over the past three decades has been aimed at developing innovative reconstruction methods that can yield high-quality images from reduced amounts of k-space data. In this review, we describe the evolution of these reconstruction techniques, with a particular focus on those advances that have shifted the dynamic reconstruction paradigm as it relates to CMR. This review discusses and explains the fundamental ideas behind the success of modern reconstruction algorithms, including parallel imaging, spatio-temporal redundancies, compressed sensing, low-rank methods and machine learning.

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Reconstruction techniques for accelerating dynamic cardiovascular magnetic resonance imaging

Abstract

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