Four-dimensional flow magnetic resonance imaging (4D Flow MRI) enables visualization of intra-cardiac blood flow and quantification of cardiac function using time-resolved three directional velocity data. Segmentation of cardiac 4D flow data is a big challenge due to the extremely poor contrast between the blood pool and myocardium. The magnitude and velocity images from a 4D flow acquisition provide complementary information, but how to extract and fuse these features efficiently is unknown. Automated cardiac segmentation methods from 4D flow MRI have not been fully investigated yet. In this work, we take the velocity and magnitude image as the inputs of two branches separately, then propose a Transformer based cross- and self-fusion layer to explore the inter-relationship from two modalities and model the intra-relationship in the same modality.
The official paper can be found here