Image-based registration for a neurosurgical robot: comparison using iterative closest point and coherent point drift algorithms

Stereotactic neurosurgical robots allow quick, accurate location of small targets within the brain, relying on accurate registration of pre-operative MRI/CT images with patient and robot coordinate systems during surgery. Fiducial markers or a stereotactic frame are used as registration landmarks; the patient's head is fixed in position throughout surgery. An image-based system could be quicker and less invasive, allowing the head to be moved during surgery to give greater ease of access, but would be required to retain a surgical precision of similar to 1mm at the target point. We compare two registration algorithms, iterative closest point (ICP) and coherent point drift (CPD), by registering ideal point clouds taken from MRI data with re-meshed, noisy and smoothed versions. We find that ICP generally gives better and more consistent registration accuracy for the region of interest than CPD, with a best RMS distance of 0.884 +/- 0.050 mm between aligned point clouds, as compared to 0.995 +/- 0.170 mm or worse for CPD. (C) 2016 The Authors. Published by Elsevier B.V.