Volumetric transformation of brain anatomy

Presents diffeomorphic transformations of three-dimensional (3-D) anatomical image data of the macaque occipital lobe and whole brain cryosection imagery and of deep brain structures in human brains as imaged via magnetic resonance imagery. These transformations are generated in a hierarchical manne...

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Bibliographic Details
Published in:IEEE transactions on medical imaging 1997-12, Vol.16 (6), p.864-877
Main Authors: Christensen, G.E., Joshi, S.C., Miller, M.I.
Format: Article
Language:English
Subjects:
Anatomy
Animals
Biological and medical sciences
Brain
Brain - anatomy & histology
Brain Mapping
Computer Simulation
Deformable models
Eigenvalues and eigenfunctions
Elasticity
Green's function
Green's function methods
Humans
Image Processing, Computer-Assisted
Investigative techniques, diagnostic techniques (general aspects)
Jacobian matrices
Macaca
Magnetic resonance
Magnetic Resonance Imaging
Mathematical transformations
Medical sciences
Navier Stokes equations
Nervous system
Neurophysiology
Pattern recognition
Physiological models
Radiodiagnosis. Nmr imagery. Nmr spectrometry
Target tracking
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Summary:Presents diffeomorphic transformations of three-dimensional (3-D) anatomical image data of the macaque occipital lobe and whole brain cryosection imagery and of deep brain structures in human brains as imaged via magnetic resonance imagery. These transformations are generated in a hierarchical manner, accommodating both global and local anatomical detail. The initial low-dimensional registration is accomplished by constraining the transformation to be in a low-dimensional basis. The basis is defined by the Green's function of the elasticity operator placed at predefined locations in the anatomy and the eigenfunctions of the elasticity operator. The high-dimensional large deformations are vector fields generated via the mismatch between the template and target-image volumes constrained to be the solution of a Navier-Stokes fluid model. As part of this procedure, the Jacobian of the transformation is tracked, insuring the generation of diffeomorphisms. It is shown that transformations constrained by quadratic regularization methods such as the Laplacian, biharmonic, and linear elasticity models, do not ensure that the transformation maintains topology and, therefore, must only be used for coarse global registration.
ISSN:0278-0062
1558-254X
DOI:10.1109/42.650882