Departments of Radiology and Medical Physics University of Wisconsin-Madison Vessel Branch Segmentation of Phase Contrast Vastly undersampled PRojection Magnetic Resonance Imaging Kevin Johnson

Overview Background Motivation Theory –Phase Coherence Mixture Model –Discrepancies Methods –Segmentation –Traces Results Conclusions

Phase Contrast MR MRI –Fourier encodes atoms of non-zero spin –Complex Acquisition Phase Contrast MR –Encoded velocity in phase –5-Images per Acquisition: 1 H Density Vascular Velocity A/PVelocity L/RVelocity S/I

PC VIPR Highly efficient acquisition More accurate than conventional methods High Resolution (0.5x0.5x0.5mm)

Motivation Phase image is difficult to analyze due to noise Segmentations allows visualization In neurological cases vasculature is complex –Need to separate branches Frydrychowicz et al. ICVTS

Theory (Chung et al.) Phase Coherence May allow for automated separation Mixture Model Phase Coherence Thresholds

Theory in Practice Phase Coherence Histogram Simply due to the higher accuracy –In paper they had phase offsets –Shift PDF of Tissue –No effect on background

Methods Segmentation 1) Determine phase coherence 2) Threshold phase coherence (T=0) 3) Threshold CD Image (T manually set) 4) Take intersection of images 5) Open image 6) Determine connected regions 7) Remove small regions Centerline 1)Filter with LPF 2)Initialize by 3D watershed filling up to threshold 3)3D Watershed with bridges built as connectivity

Results: Segmentation Complex DifferenceInitial ThresholdEroded Image Dilated ImageCleanup/Connectivity 3D Final Result

Results: Segmentation Separate out individual components Remove components ECA BranchICA BranchSap Vein Branch No ECA/SAP Un-segmented

Results: Centerline Initial Connectivity Connectivity (50% max) Final

Conclusions Phase Coherence mixture model ineffective for PC VIPR Segmentation effective at separating components Small vessels lost due to opening mask Center line effective in most vessels Failures in cases of complex flow