Aligning 3D time-of-flight MRA datasets for quantitative longitudinal studies: evaluation of rigid registration techniques

• Published in: Magnetic resonance imaging 2014-12, Vol.32 (10), p.1390-1395
• Main Authors: Verleger, Tobias, Schönfeld, Michael, Säring, Dennis, Siemonsen, Susanne, Fiehler, Jens, Forkert, Nils Daniel
• Format: Article
• Language: English
• Subjects: Aged; Algorithms; Aneurysm - pathology; Cerebrovascular Circulation; Cerebrovascular disorders; Databases, Factual; Evaluation; Female; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image Processing, Computer-Assisted; Imaging, Three-Dimensional - methods; Longitudinal Studies; Magnetic Resonance Angiography - methods; Magnetic resonance imaging; Male; Middle Aged; Middle Cerebral Artery - pathology; Radiology; Reproducibility of Results; Retrospective Studies; Rigid registration; Software; Stroke - pathology; Subtraction Technique
• ISSN: 0730-725X; 1873-5894
• DOI: 10.1016/j.mri.2014.08.011

Abstract Objective 3D Time-of-flight (TOF) magnetic resonance angiography is commonly used for vascular analyses. A quantification of longitudinal morphological changes usually requires the registration of TOF image sequences acquired at different time points. The aim of this study was to evaluate the precision of different 3D rigid registration setups such that an optimal quantification of morphological changes can be achieved. Methods Eight different rigid registration techniques were implemented and evaluated in this study using the target registration error (TRE) calculated based on 554 landmarks defined in twenty TOF datasets. The registration techniques differed in integration of brain and vessel segmentation masks and usage of a multi-resolution framework. Furthermore, the benefit of a prior volume-of-interest definition for registration accuracy was evaluated. Results The results revealed that the highest registration accuracies can be achieved using a multi-resolution framework and a cerebrovascular segmentation as mask. Numerically, a mean TRE of 1.1 mm was calculated. If applicable, a prior definition of a volume-of-interest allows a reduction of the TRE to only 0.6 mm. Conclusion TOF datasets should be registered using vessel segmentations as mask, multi-resolution framework and previous volume-of-interest definition if possible to obtain the highest registration precision. This is especially the case for longitudinal datasets that are separated by several months while the registration technique seems less important for datasets that are only separated by a few days.