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. 2020 Apr:67:28-32.
doi: 10.1016/j.mri.2019.12.004. Epub 2019 Dec 12.

Machine learning based quantification of ejection and filling parameters by fully automated dynamic measurement of left ventricular volumes from cardiac magnetic resonance images

Neha Goyal  1 , Victor Mor-Avi  1 , Valentina Volpato  1 , Akhil Narang  1 , Shuo Wang  1 , Michael Salerno  2 , Roberto M Lang  1 , Amit R Patel  3
Affiliations

Machine learning based quantification of ejection and filling parameters by fully automated dynamic measurement of left ventricular volumes from cardiac magnetic resonance images

Neha Goyal et al. Magn Reson Imaging. 2020 Apr.

Abstract

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.

Keywords: Artificial intelligence; Left ventricle; Time-volume curves.

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Conflict of interest statement

Declaration of competing interest ARP has received research support from Philips Healthcare and Neosoft. Akhil Narang was funded by a T32 Cardiovascular Sciences Training Grant (5T32HL7381) from the National Institutes of Health (USA).

Figures

Fig. 1.
Fig. 1.
Analysis of left ventricular volume-time curves, resulting in dynamic ejection and filling parameters: end-diastolic and end-systolic volumes (EDV, ESV) and stroke volume (SV), volume at 50% ejection time (ET), volumes at 25, 50 and 75% filling time (FT), volume at diastasis (DIA), rapid filling volume (RFV) and atrial filling volume (AFV).
Fig. 2.
Fig. 2.
Example of end-systolic short-axis images from left-ventricular base to apex with endocardial boundaries traced manually (left) and detected automatically by the machine learning algorithm (right).
Fig. 3.
Fig. 3.
Example of left ventricular volume-time curves (dark-blue, thick lines) and their time-derivatives (light-blue, thin lines), obtained in one study subject using the two analysis techniques: manual tracing (left), and machine learning (right). See text for details. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
See this image and copyright information in PMC

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