Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Berlin, Germany.
Working Group On Cardiovascular Magnetic Resonance, Experimental and Clinical Research Center, a joint cooperation between the Charité Medical Faculty and the Max-Delbrück Center for Molecular Medicine and HELIOS Hospital Berlin-Buch, Department of Cardiology and Nephrology, Berlin, Germany.
Eur Radiol. 2024 Feb;34(2):1003-1015. doi: 10.1007/s00330-023-10127-y. Epub 2023 Aug 18.
The analysis of myocardial deformation using feature tracking in cardiovascular MR allows for the assessment of global and segmental strain values. The aim of this study was to compare strain values derived from artificial intelligence (AI)-based contours with manually derived strain values in healthy volunteers and patients with cardiac pathologies.
A cohort of 136 subjects (60 healthy volunteers and 76 patients; of those including 46 cases with left ventricular hypertrophy (LVH) of varying etiology and 30 cases with chronic myocardial infarction) was analyzed. Comparisons were based on quantitative strain analysis and on a geometric level by the Dice similarity coefficient (DSC) of the segmentations. Strain quantification was performed in 3 long-axis slices and short-axis (SAX) stack with epi- and endocardial contours in end-diastole. AI contours were checked for plausibility and potential errors in the tracking algorithm.
AI-derived strain values overestimated radial strain (+ 1.8 ± 1.7% (mean difference ± standard deviation); p = 0.03) and underestimated circumferential (- 0.8 ± 0.8%; p = 0.02) and longitudinal strain (- 0.1 ± 0.8%; p = 0.54). Pairwise group comparisons revealed no significant differences for global strain. The DSC showed good agreement for healthy volunteers (85.3 ± 10.3% for SAX) and patients (80.8 ± 9.6% for SAX). In 27 cases (27/76; 35.5%), a tracking error was found, predominantly (24/27; 88.9%) in the LVH group and 22 of those (22/27; 81.5%) at the insertion of the papillary muscle in lateral segments.
Strain analysis based on AI-segmented images shows good results in healthy volunteers and in most of the patient groups. Hypertrophied ventricles remain a challenge for contouring and feature tracking.
AI-based segmentations can help to streamline and standardize strain analysis by feature tracking.
• Assessment of strain in cardiovascular magnetic resonance by feature tracking can generate global and segmental strain values. • Commercially available artificial intelligence algorithms provide segmentation for strain analysis comparable to manual segmentation. • Hypertrophied ventricles are challenging in regards of strain analysis by feature tracking.
心血管磁共振中的心肌变形分析通过特征跟踪可评估整体和节段应变值。本研究的目的是比较人工智能（AI）轮廓得出的应变值与健康志愿者和心脏病变患者的手动得出的应变值。
分析了 136 名受试者（60 名健康志愿者和 76 名患者；其中包括 46 例不同病因的左心室肥厚（LVH）和 30 例慢性心肌梗死）。比较基于定量应变分析和分段的 Dice 相似系数（DSC）的几何水平。应变定量在 3 个长轴切片和短轴（SAX）堆栈中进行，在舒张末期进行心内膜和心外膜的轮廓。检查 AI 轮廓的跟踪算法是否合理且可能存在误差。
AI 衍生的应变值高估了径向应变（+1.8±1.7%（平均值差异±标准差）；p=0.03），低估了环向（-0.8±0.8%；p=0.02）和纵向应变（-0.1±0.8%；p=0.54）。两组间比较显示整体应变无显著差异。DSC 在健康志愿者（SAX 为 85.3±10.3%）和患者（SAX 为 80.8±9.6%）中具有良好的一致性。在 27 例（76 例中的 27 例；35.5%）中发现了跟踪误差，主要（27 例中的 24 例；88.9%）在 LVH 组中，其中 22 例（27 例中的 22 例；81.5%）位于外侧节段的乳头肌插入处。
基于 AI 分割图像的应变分析在健康志愿者和大多数患者组中均取得了良好的结果。肥厚的心室仍然是轮廓和特征跟踪的挑战。
基于人工智能的分割可以通过特征跟踪帮助简化和标准化应变分析。
特征跟踪的心血管磁共振中的应变评估可产生整体和节段应变值。
商用人工智能算法可提供与手动分割相媲美的应变分析分割。
肥厚的心室在特征跟踪的应变分析方面具有挑战性。
