A metric for testing the accuracy of cross-modality image registration: validation and application.

Abstract

PURPOSE Our goals were to (a) develop and validate a sensitive measure of image registration, applicable between as well as within imaging modalities; and (b) quantify the accuracy of the automated image registration (AIR) algorithm in retrospectively registering MR and PET images of baboon brain. METHOD We studied five monkeys, each with a surgically implanted "cap" that was bolted to an acrylic headholder containing fiducial markers. Anatomic MRI and PET H2 15O blood flow images were aligned using AIR (ignoring the fiducials). RESULTS (a) Fiducial points were localized to about one-tenth the voxel size. Distances computed from the fiducial points were correct to within 0.2% (MRI) and 1.4% (PET). (b) The mean error remaining after AIR, measured at the five fiducial points, varied from 2.9 to 5.3 mm. The average registration error within the brain was calculated to be 2.20 mm (maximum, 3.57 mm). This error was not primarily rotational about the center of the brain and was worsened by heavy smoothing of the PET images. CONCLUSION (a) We have defined a reliable, sensitive metric that can stringently test the accuracy of various image registration techniques. (b) The AIR algorithm leaves modest error after aligning PET blood flow and anatomic MR images of baboon brain.

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@article{Black1996AMF, title={A metric for testing the accuracy of cross-modality image registration: validation and application.}, author={Kevin J Black and Tom O. Videen and Joel S. Perlmutter}, journal={Journal of computer assisted tomography}, year={1996}, volume={20 5}, pages={855-61} }