CineVN: Variational network reconstruction for rapid functional cardiac cine MRI.

Autor: Vornehm M; Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.; Magnetic Resonance, Siemens Healthineers AG, Erlangen, Germany., Wetzl J; Magnetic Resonance, Siemens Healthineers AG, Erlangen, Germany., Giese D; Magnetic Resonance, Siemens Healthineers AG, Erlangen, Germany.; Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany., Fürnrohr F; Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany., Pang J; Siemens Medical Solutions USA Inc, Chicago, Illinois, USA., Chow K; Siemens Medical Solutions USA Inc, Chicago, Illinois, USA., Gebker R; MVZ Diagnostikum Berlin 2020 GmbH, Berlin, Germany., Ahmad R; Biomedical Engineering, The Ohio State University, Columbus, Ohio, USA., Knoll F; Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.; Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA.
Jazyk: angličtina
Zdroj: Magnetic resonance in medicine [Magn Reson Med] 2025 Jan; Vol. 93 (1), pp. 138-150. Date of Electronic Publication: 2024 Aug 26.
DOI: 10.1002/mrm.30260
Abstrakt: Purpose: To develop a reconstruction method for highly accelerated cardiac cine MRI with high spatiotemporal resolution and low temporal blurring, and to demonstrate accurate estimation of ventricular volumes and myocardial strain in healthy subjects and in patients.
Methods: The proposed method, called CineVN, employs a spatiotemporal Variational Network combined with conjugate gradient descent for optimized data consistency and improved image quality. The method is first evaluated on retrospectively undersampled cine MRI data in terms of image quality. Then, prospectively accelerated data are acquired in 18 healthy subjects both segmented over two heartbeats per slice as well as in real time with 1.6 mm isotropic resolution. Ventricular volumes and strain parameters are computed and compared to a compressed sensing reconstruction and to a conventional reference cine MRI acquisition. Lastly, the method is demonstrated in 46 patients and ventricular volumes and strain parameters are evaluated.
Results: CineVN outperformed compressed sensing in image quality metrics on retrospectively undersampled data. Functional parameters and myocardial strain were the most accurate for CineVN compared to two state-of-the-art compressed sensing methods.
Conclusion: Deep learning-based reconstruction using our proposed method enables accurate evaluation of cardiac function in real-time cine MRI with high spatiotemporal resolution. This has the potential to improve cardiac imaging particularly for patients with arrhythmia or impaired breath-hold capability.
(© 2024 Siemens Healthineers AG and The Author(s). Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)
Databáze: MEDLINE
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