{"title":"Reconstruction Techniques for Accelerating Dynamic Cardiac MRI.","authors":"Andrew Phair, René Botnar, Claudia Prieto","doi":"10.1016/j.jocmr.2025.101873","DOIUrl":null,"url":null,"abstract":"<p><p>Achieving sufficient spatial and temporal resolution for dynamic applications in cardiac MRI is a challenging task due to the inherently slow nature of MR imaging. 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 cardiac MRI. 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.</p>","PeriodicalId":15221,"journal":{"name":"Journal of Cardiovascular Magnetic Resonance","volume":" ","pages":"101873"},"PeriodicalIF":4.2000,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Cardiovascular Magnetic Resonance","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.jocmr.2025.101873","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CARDIAC & CARDIOVASCULAR SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Achieving sufficient spatial and temporal resolution for dynamic applications in cardiac MRI is a challenging task due to the inherently slow nature of MR imaging. 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 cardiac MRI. 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.
期刊介绍:
Journal of Cardiovascular Magnetic Resonance (JCMR) publishes high-quality articles on all aspects of basic, translational and clinical research on the design, development, manufacture, and evaluation of cardiovascular magnetic resonance (CMR) methods applied to the cardiovascular system. Topical areas include, but are not limited to:
New applications of magnetic resonance to improve the diagnostic strategies, risk stratification, characterization and management of diseases affecting the cardiovascular system.
New methods to enhance or accelerate image acquisition and data analysis.
Results of multicenter, or larger single-center studies that provide insight into the utility of CMR.
Basic biological perceptions derived by CMR methods.