Dense feature deformation morphometry: Incorporating DTI data into conventional MRI morphometry.
Registration based mapping of geometric differences in MRI anatomy allows the detection of subtle and complex changes in brain anatomy over time that provides an important quantitative window on the process of both brain development and degeneration. However, methods developed for this have so far been aimed at using conventional structural MRI data (T1W imaging) and the resulting maps are limited in their ability to localize patterns of change within sub-regions of uniform tissue. Alternative MRI contrast mechanisms, in particular Diffusion Tensor Imaging (DTI) data are now more commonly being used in serial studies and provide valuable complementary microstructural information within white matter. This paper describes a new approach which incorporates information from DTI data into deformation tensor morphometry of conventional MRI. The key problem of using the additional information provided by DTI data is addressed by proposing a novel mutual information (MI) derived criterion termed diffusion paired MI. This combines conventional and diffusion data in a single registration measure. We compare different formulations of this measure when used in a diffeomorphic fluid registration scheme to map local volume changes. Results on synthetic data and example images from clinical studies of neurodegenerative conditions illustrate the improved localization of tissue volume changes provided by the incorporation of DTI data into the morphometric registration.
Studholme
Colin
C
Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA. colin.studholme@ieee.org
D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000473:pathology*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D019636:Neurodegenerative Diseases / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique