BACKGROUND Although analysis of cardiac magnetic resonance (CMR) images provides accurate and reproducible measurements of left ventricular (LV) volumes, these measurements are usually not performed throughout the cardiac cycle because of lack of tools that would allow such analysis within a reasonable timeframe. A fully-automated machine-learning (ML) algorithm was recently developed to automatically generate LV volume-time curves. Our aim was to validate ejection and filling parameters calculated from these curves using conventional analysis as a reference. METHODS We studied 21 patients undergoing clinical CMR examinations. LV volume-time curves were obtained using the ML-based algorithm (Neosoft), and independently using slice-by-slice, frame-by-frame manual tracing of the endocardial boundaries. Ejection and filling parameters derived from these curves were compared between the two techniques. For each parameter, Bland-Altman bias and limits of agreement (LOA) were expressed in percent of the mean measured value. RESULTS Time-volume curves were generated using the automated ML analysis within 2.5 ± 0.5 min, considerably faster than the manual analysis (43 ± 14 min per patient, including ~10 slices with 25-32 frames per slice). Time-volume curves were similar between the two techniques in magnitude and shape. Size and function parameters extracted from these curves showed no significant inter-technique differences, reflected by high correlations, small biases (<10%) and mostly reasonably narrow LOA. CONCLUSION ML software for dynamic LV volume measurement allows fast and accurate, fully automated analysis of ejection and filling parameters, compared to manual tracing based analysis. The ability to quickly evaluate time-volume curves is important for a more comprehensive evaluation of the patient's cardiac function.

中文翻译：

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通过基于心脏磁共振图像的左心室容积的全自动动态测量，基于机器学习的射血和充盈参数的量化。

背景技术尽管对心脏磁共振（CMR）图像的分析提供了对左心室（LV）体积的准确且可重复的测量，但是由于缺乏在合理的时间内允许进行这种分析的工具，这些测量通常不会在整个心动周期中执行。最近开发了一种全自动的机器学习（ML）算法，以自动生成LV体积-时间曲线。我们的目的是使用常规分析作为参考，验证从这些曲线计算出的喷射和填充参数。方法我们研究了21例接受临床CMR检查的患者。使用基于ML的算法（Neosoft）并独立使用心内膜边界的逐层，逐帧手动跟踪，可获得LV体积-时间曲线。从这两种曲线得出的喷射和填充参数在两种技术之间进行了比较。对于每个参数，均以平均测量值的百分比表示Bland-Altman偏差和一致性极限（LOA）。结果使用自动ML分析在2.5±0.5分钟内生成了时间-体积曲线，比手动分析要快得多（每位患者43±14分钟，包括〜10片，每片25-32帧）。两种技术在大小和形状上的时间-体积曲线相似。从这些曲线中提取的大小和功能参数没有显着的技术间差异，反映出较高的相关性，较小的偏差（<10％）和大部分合理的LOA。结论用于动态LV体积测量的ML软件可实现快速，准确，与基于手动跟踪的分析相比，可以对喷射和填充参数进行全自动分析。快速评估时间-容量曲线的能力对于更全面地评估患者的心脏功能非常重要。