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Automated three‐dimensional registration of magnetic resonance and positron emission tomography brain images by multiresolution optimization of voxel similarity measures
Author(s) -
Studholme Colin,
Hill Derek L. G.,
Hawkes David J.
Publication year - 1997
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.598130
Subject(s) - mutual information , image registration , artificial intelligence , voxel , positron emission tomography , computer science , computer vision , similarity (geometry) , mathematics , pattern recognition (psychology) , entropy (arrow of time) , magnetic resonance imaging , similarity measure , nuclear medicine , physics , image (mathematics) , medicine , radiology , quantum mechanics
Approaches using measures of voxel intensity similarity are showing promise in fully automating magnetic resonance (MR) and positron emission tomography (PET) image registration in the head, without requiring extraction and identification of corresponding structures. In this paper a method of multiresolution optimization of these measures is described and five alternative measures are compared: cross correlation, minimization of corresponding PET intensity variation, moments of the distribution of values in the intensity feature space, entropy of the intensity feature space and mutual information. Their ability to recover registration is examined for ten clinically acquired image pairs with respect to the size of initial misregistration, the precision of the final result, and the accuracy assessed by visual inspection. The mutual information measure proved the most robust to initial starting estimate, successfully registering 98.8% of 900 trial misregistrations. Success is defined as providing a visually acceptable solution to a trained observer. A high resolution search (1 16mm step size) of 30 trial misregistrations showed that optimization using the mutual information measure provided solutions with 0.13 mm, 0.11 mm and 0.17 mm standard deviations in the three Cartesian axes of the translation vector and 0.2°, 0.3° and 0.2° standard deviations for rotations about the three axes. The algorithm takes between 4 and 8 minutes to run on a typical workstation, including visual inspection of the result.