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Abstract #1961

Automatic 3D to 2D reformatting in 4D flow MRI using continuous reinforced learning

Javier Bisbal1,2,3, Julio Sotelo1,3,4, Cristobal Arrieta1,3, Pablo Irarrázabal1,2,3, Marcelo Andia1,3,5, Cristian Tejos1,2,3, and Sergio Uribe1,3,5
1Biomedical Imaging Center UC, Pontificia Universidad Catolica de Chile, Santiago, Chile, 2Electrical Engineering Department, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile, 3ANID – Millennium Science Initiative Program – Millennium Nucleus for Cardiovascular Magnetic Resonance, Santiago, Chile, 4School of Biomedical Engineering, Universidad de Valparaíso, Valparaíso, Chile, 5Department of Radiology, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile


One major limitation on 4D flow MRI is the time-consuming and user-dependent post-processing. We developed an automated reinforced deep learning framework for plane planning in 4D flow data. This method sequentially updates plane parameters towards a target plane based on a continuous policy. A total of 83 4D flow MRI scans were considered, 41 for training, 14 for validation and 28 for test. Our method achieves good results in terms of angulation and distance error (9.21 ± 3.85 degrees and 3.72 ± 2.19 mm).

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