A comparison of a similarity-based and a feature-based 2-D-3-D registration method for neurointerventional use.
Two-dimensional (2-D)-to-three-dimensional (3-D) registration can improve visualization which may aid minimally invasive neurointerventions. Using clinical and phantom studies, two state-of-the-art approaches to rigid registration are compared quantitatively: an intensity-based algorithm using the gradient difference similarity measure; and an iterative closest point (ICP)-based algorithm. The gradient difference approach was found to be more accurate, with an average registration accuracy of 1.7 mm for clinical data, compared to the ICP-based algorithm with an average accuracy of 2.8 mm. In phantom studies, the ICP-based algorithm proved more reliable, but with more complicated clinical data, the gradient difference algorithm was more robust. Average computation time for the ICP-based algorithm was 20 s per registration, compared with 14 min and 50 s for the gradient difference algorithm.
McLaughlin
Robert A
RA
Wolfson Medical Vision Laboratory, Department of Engineering Science, University of Oxford, Oxford OX2 0BU, UK. r.mclaughlin@ieee.org
Hipwell
John
J
Hawkes
David J
DJ
Noble
J Alison
JA
Byrne
James V
JV
Cox
Tim C
TC
D000465:Algorithms; D001165:Arteriovenous Malformations / Q000175:diagnosis; D001185:Artificial Intelligence; D016000:Cluster Analysis; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D002532:Intracranial Aneurysm / Q000175:diagnosis; D038361:Neuronavigation / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique