Weighted Mean Frequencies: a handcraft Fourier feature for 4D Flow MRI segmentation

2025-06-25

AI Summary

Low resolution and noise in 4D flow MRI hinder accurate vascular segmentation, compromising the quantitative accuracy of hemodynamic biomarkers such as wall shear stress. Method: We propose a novel handcrafted feature—Weighted Mean Frequency (WMF)—derived from Fourier analysis of 3D pulsatile blood flow signals to characterize velocity voxel envelope structures, enabling more robust representation of vascular spatial distribution, especially in complex regions like the heart and brain. WMF outperforms conventional PC-MRA features in morphological expressiveness and segmentation guidance. Contribution/Results: Evaluated via optimal thresholding and deep learning-based segmentation, WMF boosts IoU and Dice scores by 0.12 and 0.13, respectively, significantly improving segmentation accuracy and robustness. This work achieves a principled integration of signal-processing priors with deep learning.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Feature Construction/Reformulation
• Humans and AI: Brain-Sensing and Analysis

Application Category

• Web Mining and Content Analysis: Content-based information diffusion
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Improves 4D Flow MRI segmentation accuracy
• Addresses noise and resolution limitations in biomarkers
• Introduces Weighted Mean Frequencies for better visualization

Innovation

• Weighted Mean Frequencies for 4D Flow MRI segmentation
• Enhances segmentation via optimal thresholding and deep learning
• Improves IoU and Dice scores compared to PC-MRA