Fully automatic AI-based valve motion parameter extraction on long axis CINE images - application on N=11000 patient datasets

Abstract

Abstract Funding Acknowledgements Type of funding sources: Private company. Main funding source(s): Research support from Siemens Healthineers GmbH. Background Mitral valve (MV) motion parameters, assessable using CMR [1, 2], have been shown to help the diagnosis of cardiac dysfunction. To extract valve motion parameters, we propose a fully automatic AI-based prototype system that tracks annulus and apex landmarks by the registration network on time-resolved two- and four-chamber CMR cine views. Parameters such as displacements, velocities, mitral annular plane systolic excursion (MAPSE), or longitudinal shortening (LS) are automatically extracted and evaluated on a large CMR dataset (N=11000). Methods The system consists of two sequential neural networks with a processing step in between (Fig. 1a) [3]. Initially, a 2D UNet is applied to localize both MV annulus insertion points as well as the apex. Based on these points, the image processing step consists of rotating, cropping, and interpolating the images, allowing a standardized image impression for both long axis views. Finally, the registration network (VoxelMorph framework [4]) is applied to the processed series and tracks the MV annulus insertion points and apex over the cardiac cycle by the deformation fields obtained by the network. The system was trained on (N=166) multivendor, multi-field strength, ground-truth annotated datasets [5]. A total of 11000 datasets, acquired on a 1.5T scanner (MAGNETOM Aera, Siemens Healthcare, Erlangen, Germany) from January 2016 to September 2017 [6], were used for parameter extraction. 200 of these datasets were additionally annotated semi-automatically for the performance evaluation of the system. Five motion parameters were automatically derived by the system that are defined as follows (Fig. 1b): (1) The atrioventricular plane displacement (AVPD) as the distance of the plane spanned by the MV annulus points relative to the first frame, (2) the atrioventricular plane velocity (AVPV) as the discrete temporal derivate of the AVPD, (3) the diameter of the annulus as the maximum distance between the MV annulus points, (4) the lateral/inferior and septal/superior MAPSE, as the maximum MV points’ excursion, and (5) the LS as the percentage size difference of the distance between the mid valvular point and the apex point at end-systole and end-diastole. Results The accuracy of the system resulted in deviations on the annotated dataset of 1.02 ± 0.87 mm, 0.01 ± 0.02 mm/s, 1.54 ± 1.21 mm, 2.30 ± 1.35 mm, 2.1 ± 1.8 mm for AVPD, AVPV, diameter, MAPSE, and LS respectively. Initial statistics on all datasets (Fig. 2) revealed a mean lateral/inferior, septal/superior MAPSE and LS of 8.7 ± 2.7 mm, 10.5 ± 3.2 mm and 16.3 ± 4.2 % for two-chamber and 9.6 ± 2.6 mm, 8.7 ± 2.6 mm and 15.5 ± 3.9 % for four-chamber views, respectively. Conclusions The results demonstrate the versatility of the proposed system for automatic extraction of various MV motion parameters. The proposed system enables automatic extraction of clinically relevant parameters and can improve the automation of MV-based analyses. System overview & Parameter of interestsAnalysis of the extracted parameters