Coupling synthetic and real-world data for a deep learning-based segmentation process of 4D flow MRI.
Journal:
Computer methods and programs in biomedicine
Published Date:
Sep 6, 2023
Abstract
BACKGROUND AND OBJECTIVE: Phase contrast magnetic resonance imaging (4D flow MRI) is an imaging technique able to provide blood velocity in vivo and morphological information. This capability has been used to study mainly the hemodynamics of large vessels, such as the thoracic aorta. However, the segmentation of 4D flow MRI data is a complex and time-consuming task. In recent years, neural networks have shown great accuracy in segmentation tasks if large datasets are provided. Unfortunately, in the context of 4D flow MRI, the availability of these data is limited due to its recent adoption in clinical settings. In this study, we propose a pipeline for generating synthetic thoracic aorta phase contrast magnetic resonance angiography (PC) to expand the limited dataset of patient-specific PC images, ultimately improving the accuracy of the neural network segmentation even with a small real dataset.