Adaptive Plane Reformatting for 4D Flow MRI using 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.
Authors
- M.D. Valdés (ORCID: https://orcid.org/0000-0001-6900-2790)
- Denis Parra (ORCID: https://orcid.org/0000-0001-9878-8761)
- Francesca Raimondi (ORCID: https://orcid.org/0000-0003-2580-151X)
- Sergio A. Uribe (ORCID: https://orcid.org/0000-0002-4970-9710)
- Cristián Tejos (ORCID: https://orcid.org/0000-0002-8367-155X)
- Julio A Sotelo (ORCID: https://orcid.org/0000-0002-0915-5215)
- Javier Bisbal
- Marcelo E. Andía (ORCID: https://orcid.org/0000-0002-1251-5832)
- José F. Rodríguez‐Palomares (ORCID: https://orcid.org/0000-0002-7229-9780)
- Cristóbal Arrieta (ORCID: https://orcid.org/0000-0001-8773-3722)
- Julio García (ORCID: https://orcid.org/0000-0001-9528-8155)
- Pablo Irarrázaval (ORCID: https://orcid.org/0000-0002-5186-2642)
Publication Details
- Journal
- Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
- Published
- 2024-11-26
- DOI
- https://doi.org/10.58530/2024/2232
- Citations
- 1
- Primary Topic
- Medical Image Segmentation Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.25