Objectives This study sought to develop a fully automated framework for cardiac function analysis from cardiac magnetic resonance (CMR), including comprehensive quality control (QC) algorithms to detect erroneous output. Background Analysis of cine CMR imaging using deep learning (DL) algorithms could automate ventricular function assessment. However, variable image quality, variability in phenotypes of disease, and unavoidable weaknesses in training of DL algorithms currently prevent their use in clinical practice. Methods The framework consists of a pre-analysis DL image QC, followed by a DL algorithm for biventricular segmentation in long-axis and short-axis views, myocardial feature-tracking (FT), and a post-analysis QC to detect erroneous results. The study validated the framework in healthy subjects and cardiac patients by comparison against manual analysis (n = 100) and evaluation of the QC steps’ ability to detect erroneous results (n = 700). Next, this method was used to obtain reference values for cardiac function metrics from the UK Biobank. Results Automated analysis correlated highly with manual analysis for left and right ventricular volumes (all r > 0.95), strain (circumferential r = 0.89, longitudinal r > 0.89), and filling and ejection rates (all r ≥ 0.93). There was no significant bias for cardiac volumes and filling and ejection rates, except for right ventricular end-systolic volume (bias +1.80 ml; p = 0.01). The bias for FT strain was <1.3%. The sensitivity of detection of erroneous output was 95% for volume-derived parameters and 93% for FT strain. Finally, reference values were automatically derived from 2,029 CMR exams in healthy subjects. Conclusions The study demonstrates a DL-based framework for automated, quality-controlled characterization of cardiac function from cine CMR, without the need for direct clinician oversight.

中文翻译：

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通过 CMR 验证和大规模应用来表征心脏功能的全自动、质量控制的心脏分析

目的 本研究旨在开发一个全自动的心脏磁共振 (CMR) 心脏功能分析框架，包括用于检测错误输出的综合质量控制 (QC) 算法。使用深度学习 (DL) 算法对电影 CMR 成像进行背景分析可以自动进行心室功能评估。然而，可变的图像质量、疾病表型的可变性以及 DL 算法训练中不可避免的弱点目前阻碍了它们在临床实践中的应用。方法 该框架由分析前 DL 图像 QC、用于在长轴和短轴视图中进行双心室分割的 DL 算法、心肌特征跟踪 (FT) 和用于检测错误结果的分析后 QC 组成。该研究通过与手动分析 (n = 100) 进行比较并评估 QC 步骤检测错误结果的能力 (n = 700)，在健康受试者和心脏病患者中验证了该框架。接下来，该方法用于从英国生物银行获取心脏功能指标的参考值。结果 自动分析与手动分析左右心室容积（所有 r > 0.95）、应变（圆周 r = 0.89、纵向 r > 0.89）以及充盈率和射血率（所有 r ≥ 0.93）高度相关。除了右心室收缩末期容积（偏差+1.80 ml；p = 0.01）外，心脏容量和充盈率和射血率没有显着偏差。FT 应变的偏差<1.3%。体积衍生参数的错误输出检测灵敏度为 95%，FT 应变的灵敏度为 93%。最后，参考值是从健康受试者的 2,029 次 CMR 检查中自动得出的。结论 该研究展示了一个基于 DL 的框架，用于从电影 CMR 中自动、质量控制地表征心脏功能，而无需临床医生的直接监督。