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Publication Detail
Intensity-based 2-D-3-D registration of cerebral angiograms
  • Publication Type:
    Journal article
  • Publication Sub Type:
  • Authors:
    Hipwell JH, Penney GP, McLaughlin RA, Rhode K, Summers P, Cox TC, Byrne JV, Noble JA, Hawkes DJ
  • Publication date:
  • Pagination:
    1417, 1426
  • Journal:
  • Volume:
  • Issue:
    278-0062 (Print), 11
  • Keywords:
    Algorithms, analysis, Angiography, Blood Flow Velocity, Cerebral Angiography, Comparative Study, diagnosis, Evaluation Studies, Image Interpretation, Computer-Assisted, Imaging, Three-Dimensional, Intracranial Aneurysm, Intracranial Arteriovenous Malformations, London, Magnetic Resonance Angiography, methods, Phantoms, Imaging, physiopathology, Radiographic Image Enhancement, radiography, Reproducibility of Results, Research Support, Non-U.S.Gov't, Sensitivity and Specificity, Subtraction Technique, Cerebral, Rads
  • Addresses:
    Division of Imaging Sciences, UMDS, Guy's & St Thomas' Hospitals, London SE1 9RT, UK. john.hipwell@kcl.ac.uk
  • Notes:
    DA - 20031110
We propose a new method for aligning three-dimensional (3-D) magnetic resonance angiography (MRA) with 2-D X-ray digital subtraction angiograms (DSA). Our method is developed from our algorithm to register computed tomography volumes to X-ray images based on intensity matching of digitally reconstructed radiographs (DRRs). To make the DSA and DRR more similar, we transform the MRA images to images of the vasculature and set to zero the contralateral side of the MRA to that imaged with DSA. We initialize the search for a match on a user defined circular region of interest. We have tested six similarity measures using both unsegmented MRA and three segmentation variants of the MRA. Registrations were carried out on images of a physical neuro-vascular phantom and images obtained during four neuro-vascular interventions. The most accurate and robust registrations were obtained using the pattern intensity, gradient difference, and gradient correlation similarity measures, when used in conjunction with the most sophisticated MRA segmentations. Using these measures, 95% of the phantom start positions and 82% of the clinical start positions were successfully registered. The lowest root mean square reprojection errors were 1.3 mm (standard deviation 0.6) for the phantom and 1.5 mm (standard deviation 0.9) for the clinical data sets. Finally, we present a novel method for the comparison of similarity measure performance using a technique borrowed from receiver operator characteristic analysis
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