Ogier Augustin C, Bustin Aurelien, Cochet Hubert, Schwitter Juerg, van Heeswijk Ruud B
Department of Diagnostic and Interventional Radiology, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland.
IHU LIRYC, Electrophysiology and Heart Modeling Institute, Université de Bordeaux, INSERM, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, Bordeaux, France.
Front Cardiovasc Med. 2022 May 6;9:876475. doi: 10.3389/fcvm.2022.876475. eCollection 2022.
Parametric mapping of the heart has become an essential part of many cardiovascular magnetic resonance imaging exams, and is used for tissue characterization and diagnosis in a broad range of cardiovascular diseases. These pulse sequences are used to quantify the myocardial T, T, , and T relaxation times, which are unique surrogate indices of fibrosis, edema and iron deposition that can be used to monitor a disease over time or to compare patients to one another. Parametric mapping is now well-accepted in the clinical setting, but its wider dissemination is hindered by limited inter-center reproducibility and relatively long acquisition times. Recently, several new parametric mapping techniques have appeared that address both of these problems, but substantial hurdles remain for widespread clinical adoption. This review serves both as a primer for newcomers to the field of parametric mapping and as a technical update for those already well at home in it. It aims to establish what is currently needed to improve the reproducibility of parametric mapping of the heart. To this end, we first give an overview of the metrics by which a mapping technique can be assessed, such as bias and variability, as well as the basic physics behind the relaxation times themselves and what their relevance is in the prospect of myocardial tissue characterization. This is followed by a summary of routine mapping techniques and their variations. The problems in reproducibility and the sources of bias and variability of these techniques are reviewed. Subsequently, novel fast, whole-heart, and multi-parametric techniques and their merits are treated in the light of their reproducibility. This includes state of the art segmentation techniques applied to parametric maps, and how artificial intelligence is being harnessed to solve this long-standing conundrum. We finish up by sketching an outlook on the road toward inter-center reproducibility, and what to expect in the future.
心脏参数映射已成为许多心血管磁共振成像检查的重要组成部分,并用于多种心血管疾病的组织特征分析和诊断。这些脉冲序列用于量化心肌的T1、T2、横向弛豫时间(T2*)和T1ρ弛豫时间,它们是纤维化、水肿和铁沉积的独特替代指标,可用于长期监测疾病或在患者之间进行比较。参数映射目前在临床环境中已被广泛接受,但其更广泛的传播受到中心间再现性有限和采集时间相对较长的阻碍。最近,出现了几种新的参数映射技术,解决了这两个问题,但要在临床上广泛应用仍存在重大障碍。这篇综述既为参数映射领域的新手提供入门指导,也为该领域的行家提供技术更新。其目的是确定目前提高心脏参数映射再现性所需的条件。为此,我们首先概述可用于评估映射技术的指标,如偏差和变异性,以及弛豫时间本身背后的基本物理学原理及其在心肌组织特征分析中的相关性。接下来总结常规映射技术及其变体。回顾这些技术在再现性方面存在的问题以及偏差和变异性的来源。随后,根据其再现性探讨新颖的快速、全心和多参数技术及其优点。这包括应用于参数图的最新分割技术,以及如何利用人工智能来解决这个长期存在的难题。我们最后勾勒出实现中心间再现性的道路展望以及未来的预期。