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Abstract #1031

Comparison of Rigid Registration Methods for Time-Of-Flight MRA Datasets

Tobias Verleger1, Dennis Sring1, Michael Schnfeld2, Susanne Siemonsen2, Jens Fiehler2, Nils Daniel Forkert1

1Department of Computational Neurosience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; 2Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Germany

Intra-patient registration of Time-of-Flight MRA image sequences is required for several quantitative analyses and therapy monitoring of cerebrovascular diseases. The aim of this study was to evaluate different rigid registration approaches for aligning TOF MRA image sequences. For this, eight rigid registration approaches were evaluated with the target registration error (TRE) calculated based on 308 landmarks defined in twenty TOF datasets. Each dataset included a baseline and follow-up image sequence. The results revealed that the highest registration accuracies can be achieved using a multi-resolution framework and a cerebrovascular segmentation as a mask with a mean TRE of 1.1mm.