Intensity-based 2-D - 3-D registration of cerebral angiograms

• Published in: IEEE transactions on medical imaging Vol. 22; no. 11; pp. 1417 - 1426
• Main Authors: Hipwell, J.H., Penney, G.P., McLaughlin, R.A., Rhode, K., Summers, P., Cox, T.C., Byrne, J.V., Noble, J.A., Hawkes, D.J.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01-11-2003; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Angiography; Biological and medical sciences; Blood Flow Velocity; Cerebral Angiography - methods; Computed tomography; Image Interpretation, Computer-Assisted - methods; Image reconstruction; Image segmentation; Imaging phantoms; Imaging, Three-Dimensional - methods; Intracranial Aneurysm - diagnosis; Intracranial Aneurysm - diagnostic imaging; Intracranial Aneurysm - physiopathology; Intracranial Arteriovenous Malformations - diagnosis; Intracranial Arteriovenous Malformations - diagnostic imaging; Intracranial Arteriovenous Malformations - physiopathology; Magnetic resonance; Magnetic Resonance Angiography - methods; Medical imaging; Medical sciences; Phantoms, Imaging; Radiographic Image Enhancement - methods; Radiography; Radiotherapy. Instrumental treatment. Physiotherapy. Reeducation. Rehabilitation, orthophony, crenotherapy. Diet therapy and various other treatments (general aspects); Registers; Reproducibility of Results; Sensitivity and Specificity; Studies; Subtraction Technique; Technology. Biomaterials. Equipments. Material. Instrumentation; Testing; X-ray imaging; Biomedical engineering
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2003.819283

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.