Weighted Mean Frequencies: a handcraft Fourier feature for 4D Flow MRI segmentation
Journal:
arXiv
Published Date:
Jun 25, 2025
Abstract
In recent decades, the use of 4D Flow MRI images has enabled the
quantification of velocity fields within a volume of interest and along the
cardiac cycle. However, the lack of resolution and the presence of noise in
these biomarkers are significant issues. As indicated by recent studies, it
appears that biomarkers such as wall shear stress are particularly impacted by
the poor resolution of vessel segmentation. The Phase Contrast Magnetic
Resonance Angiography (PC-MRA) is the state-of-the-art method to facilitate
segmentation. The objective of this work is to introduce a new handcraft
feature that provides a novel visualisation of 4D Flow MRI images, which is
useful in the segmentation task. This feature, termed Weighted Mean Frequencies
(WMF), is capable of revealing the region in three dimensions where a voxel has
been passed by pulsatile flow. Indeed, this feature is representative of the
hull of all pulsatile velocity voxels. The value of the feature under
discussion is illustrated by two experiments. The experiments involved
segmenting 4D Flow MRI images using optimal thresholding and deep learning
methods. The results obtained demonstrate a substantial enhancement in terms of
IoU and Dice, with a respective increase of 0.12 and 0.13 in comparison with
the PC-MRA feature, as evidenced by the deep learning task. This feature has
the potential to yield valuable insights that could inform future segmentation
processes in other vascular regions, such as the heart or the brain.
