Physics-informed vessel segmentation for χ-separation (chi-separation)

Motivation: χ-separation is an advanced QSM method that provides para- and diamagnetic susceptibility maps. Despite its potential utilities, both maps reveal (erroneously) high intensity signals from vessels, hampering their applications and quantification. Goal(s): Proposing a vessel segmentation method designed for χ-separation by utilizing physical properties of vessel signals in χ-separation. Approach: Acquiring seeds by informing physics of vessels in χ-separation, and vessel geometry characteristics guided-region growing is implemented to generate vessel masks. Results: Our method successfully creates vessel masks for both susceptibility maps and demonstrates to be robust to various input types. When applied to an ROI analysis, reduced variability in measurements was shown. Impact: The novel vessel segmentation method utilizes physical properties of vessels in χ-separation for reliable and robust segmentation, providing substantially improved segmentation results. It may help us to improve downstream analysis when quantifying susceptibility of myelin or tissue iron excluding vessels.