Abstract #2232
Adaptive Plane Reformatting for 4D Flow MRI using Deep Reinforcement Learning
Javier Bisbal1,2,3, Julio Sotelo4, Cristóbal Arrieta2,5, Pablo Irarrazaval2,3,6,7, Marcelo E Andia1,2,8, denis Parra2,9,10, Maria Ignacia Valdes1,3, Cristián Tejos1,2,3, Julio Garcia11, José F. Rodríguez-Palomares12, Francesca Raimondi13, and Sergio Uribe2,14
1Biomedical Imaging Center, Pontificia Universidad Catolica de Chile, Santiago, Chile, 2Millennium Institute for Intelligent Healthcare Engineering, iHEALTH, Santiago, Chile, 3Department of Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile, 4Departamento de Informática, Universidad Técnica Federico Santa Maria, Santiago, Chile, 5Faculty of Engineering, Universidad Alberto Hurtado, Santiago, Chile, 6Biomedical Imaging Center, Pontificia Universidad Católica de chile, Santiago, Chile, 7Institute for Biological and Medical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile, 8Department of Radiology, Pontificia Universidad Católica de chile, Santiago, Chile, 9Computer Science Department, Pontifici Universidad Catolica de Chile, Santiago, Chile, 10Centro Nacional de Inteligencia Artificial, CENIA, Santiago, Chile, 11Stephenson Cardiac Imaging Centre, Department of Radiology, University of Calgary, Calgary, AB, Canada, 12Department of Cardiology, Hospital Universitari Vall d'Hebron, CIBER-CV, Vall d'Hebron Institut de Recerca (VHIR), Barcelona, Spain, 13Department of Cardiology and Cardiovascular Surgery, Papa Giovanni XXIII Hospital, Bergamo, Italy, 14Department of Medical Imaging and Radiation Sciences, Monash University, Melbourne, Australia
Synopsis
Keywords: Analysis/Processing, Velocity & Flow, Plane reformatting, Deep reinforcement learning
Motivation: The standard approach for plane reformatting in 4D flow MRI is manual, leading to time-consuming and user-dependent results.
Goal(s): Our goal was to enhance plane reformatting in 4D flow MRI and overcome limitations associated with existing automated methods.
Approach: We introduce a novel approach that employs deep reinforcement learning (DRL) with a flexible coordinate system for precise and adaptable plane reformatting.
Results: Results demonstrate superior performance compared to baseline DRL and similar outcomes compared to those of landmark-based techniques, showing its potential for use in complex medical imaging scenarios beyond 4D flow MRI.
Impact: The proposed framework allows for automated, precise, and adaptive plane reformatting, facilitating the use of 4D flow MRI in clinical routines. It was trained with data sets from different vendors, making this approach widely applicable.
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