Fully automated AI-based cardiac motion parameter extraction – application to mitral and tricuspid valves on long-axis cine MR images

Yoon SS, Fischer C, Amsel D, Monzon M, Toupin S, Pezel T, Garot J, Wetzl J, Maier A, Giese D, Giese D (2023)

Publication Type: Journal article

Publication year: 2023

Journal

European Journal of Radiology Elsevier

Book Volume: 166

Article Number: 110978

DOI: 10.1016/j.ejrad.2023.110978

Abstract

Purpose: In cardiac MRI, valve motion parameters can be useful for the diagnosis of cardiac dysfunction. In this study, a fully automated AI-based valve tracking system was developed and evaluated on 2- or 4-chamber view cine series on a large cardiac MR dataset. Automatically derived motion parameters include atrioventricular plane displacement (AVPD), velocities (AVPV), mitral or tricuspid annular plane systolic excursion (MAPSE, TAPSE), or longitudinal shortening (LS). Method: Two sequential neural networks with an intermediate processing step are applied to localize the target and track the landmarks throughout the cardiac cycle. Initially, a localisation network is used to perform heatmap regression of the target landmarks, such as mitral, tricuspid valve annulus as well as apex points. Then, a registration network is applied to track these landmarks using deformation fields. Based on these outputs, motion parameters were derived. Results: The accuracy of the system resulted in deviations of 1.44 ± 1.32 mm, 1.51 ± 1.46 cm/s, 2.21 ± 1.81 mm, 2.40 ± 1.97 mm, 2.50 ± 2.06 mm for AVPD, AVPV, MAPSE, TAPSE and LS, respectively. Application on a large patient database (N = 5289) revealed a mean MAPSE and LS of 9.5 ± 3.0 mm and 15.9 ± 3.9 % on 2-chamber and 4-chamber views, respectively. A mean TAPSE and LS of 13.4 ± 4.7 mm and 21.4 ± 6.9 % was measured. Conclusion: The results demonstrate the versatility of the proposed system for automatic extraction of various valve-related motion parameters.

Authors with CRIS profile

Seung Su Yoon Lehrstuhl für Informatik 5 (Mustererkennung) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung) Daniel Giese

Involved external institutions

Cardiovascular Institute Paris Sud (ICPS) FR France (FR) Siemens FR France (FR) Siemens AG, Healthcare Sector DE Germany (DE)

How to cite

APA:

Yoon, S.S., Fischer, C., Amsel, D., Monzon, M., Toupin, S., Pezel, T.,... Giese, D. (2023). Fully automated AI-based cardiac motion parameter extraction – application to mitral and tricuspid valves on long-axis cine MR images. European Journal of Radiology, 166. https://dx.doi.org/10.1016/j.ejrad.2023.110978

MLA:

Yoon, Seung Su, et al. "Fully automated AI-based cardiac motion parameter extraction – application to mitral and tricuspid valves on long-axis cine MR images." European Journal of Radiology 166 (2023).

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