CineVN: Variational network reconstruction for rapid functional cardiac cine MRI

Vornehm M, Wetzl J, Giese D, Fürnrohr F, Pang J, Chow K, Gebker R, Ahmad R, Knoll F (2024)

Publication Language: English

Publication Type: Journal article

Publication year: 2024

Journal

Magnetic Resonance in Medicine Wiley

Original Authors: Marc Vornehm, Jens Wetzl, Daniel Giese, Florian Fürnrohr, Jianing Pang, Kelvin Chow, Rolf Gebker, Rizwan Ahmad, Florian Knoll

DOI: 10.1002/mrm.30260

Abstract


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.

Authors with CRIS profile

Marc Vornehm Professur für Computational Imaging Daniel Giese Institute of Radiology Florian Knoll Professur für Computational Imaging

Involved external institutions

Ohio State University US United States (USA) (US) Siemens Healthcare USA US United States (USA) (US) Siemens Healthineers DE Germany (DE)

How to cite

APA:

Vornehm, M., Wetzl, J., Giese, D., Fürnrohr, F., Pang, J., Chow, K.,... Knoll, F. (2024). CineVN: Variational network reconstruction for rapid functional cardiac cine MRI. Magnetic Resonance in Medicine. https://doi.org/10.1002/mrm.30260

MLA:

Vornehm, Marc, et al. "CineVN: Variational network reconstruction for rapid functional cardiac cine MRI." Magnetic Resonance in Medicine (2024).

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