Incorporating DTI data as a constraint in deformation tensor morphometry between T1 MR images.
Deformation tensor morphometry provides a sensitive approach to detecting and mapping subtle volume changes in the brain from conventional high resolution T1W MRI data. However, it is limited in its ability to localize volume changes within sub-regions of uniform white matter in T1W MRI. In contrast, lower resolution DTI data provides valuable complementary microstructural information within white matter. An approach to incorporating information from DTI data into deformation tensor morphometry of conventional high resolution T1W imaging is described. A novel mutual information (MI) derived criteria is proposed, termed diffusion paired MI, using an approximation to collective many-channel MI between all images. This approximation avoids the evaluation of high dimensional joint probability distributions, but allows a combination of conventional and diffusion data in a single registration criteria. The local gradient of this measure is used to drive a viscous fluid registration between repeated DTI-MRI imaging studies. Results on example data from clinical studies of Alzheimer's disease illustrate the improved localization of tissue loss patterns within regions of white matter.
Studholme
Colin
C
Department of Radiology, University of California San Francisco, Northern California Institute for Research and Education, VAMC San Francisco, San Francisco, USA. colin.studholme@ieee.org
D000465:Algorithms; D000544:Alzheimer Disease / Q000473:pathology*; D001185:Artificial Intelligence; D001921:Brain / Q000473:pathology*; D016208:Databases, Factual; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D009413:Nerve Fibers, Myelinated / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique