Keywords: Software Tools, Quantitative Imaging
Motivation: Deep medullary veins (DMVs) stenosis may be one of the causes of small vessel disease, so non-invasive tool for its assessment is desired.
Goal(s): Developing automatic DMV segmentation and diameter quantification methods for assessing DMV stenosis.
Approach: We trained an automatic segmentation model and proposed a DMV diameter quantification method by analyzing the complex MRI signals at sub-voxel scale.
Results: The segmentation model achieved satisfactory performance. The accuracy of the diameter quantification method was verified in phantoms. The fitted DMV diameter distribution was close to earlier ex-vivo report and showed strong correlation with DMV susceptibility from quantitative susceptibility mapping.
Impact: Our approach can serve as a useful automatic pipeline to study the role of DMV stenosis in the pathogenesis of small vessel disease.
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