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SMURF: Scalable method for unsupervised reconstruction of flow in 4D flow MRI
Arxiv
|June 12, 2025
Summary
SMURF, a new machine learning method, accurately segments vascular structures and reconstructs blood flow from 4D flow MRI. This scalable, unsupervised approach improves diagnostic accuracy for vascular conditions.
Area of Science:
- Medical Imaging
- Machine Learning
- Biomedical Engineering
Background:
- 4D flow MRI is crucial for assessing vascular diseases.
- Accurate segmentation and velocity field reconstruction are vital for clinical interpretation.
- Current methods face challenges with noise and computational efficiency.
Purpose of the Study:
- Introduce SMURF, a scalable, unsupervised machine learning method.
- Simultaneously segment vascular geometries and reconstruct velocity fields from 4D flow MRI.
- Enhance the accuracy and diagnostic utility of 4D flow MRI.
Main Methods:
- SMURF utilizes multilayer perceptrons with Fourier feature embeddings and random weight factorization.
- A measurement model links modeled fields to observed MRI magnitude and phase data.
- Maximum likelihood estimation and subsampling ensure efficient processing of high-dimensional data.
Main Results:
- Achieved quarter-voxel segmentation accuracy on synthetic aneurysm data, outperforming state-of-the-art methods.
- Reduced velocity reconstruction RMSE by ~34% in vitro.
- Attained near half-voxel accuracy on in vivo data, decreasing velocity divergence residuals by ~31%.
Conclusions:
- SMURF demonstrates robustness to noise and preserves complex flow structures.
- The method accurately identifies patient-specific vascular morphologies.
- SMURF significantly advances 4D flow MRI accuracy for clinical applications.
