ICBM35 Affine

FIGURE 17 Pathology detection with a deformable probabilistic atlas. A family of high-dimensional volumetric warps relating a new subject's scan to each normal scan in a brain image database is calculated (I—II, above), and then used to quantify local structural variations. Differences in anatomy are recorded in the form of vector field transformations in 3D stereotaxic space that drive both subcortical anatomy and the gyral/sulcal patterns of different subjects into register. The resulting family of warps encodes the distribution in stereotaxic space of anatomic points that correspond across a normal population (III). Their dispersion is used to determine the likelihood (IV) of local regions of the new subject's anatomy being in their actual configuration. Easily interpretable, color-coded topographic maps can then be created to highlight regional patterns of deformity in the anatomy of each new subject [112]. This approach quantifies abnormal structural patterns locally and maps them in three dimensions.

FIGURE 17 Pathology detection with a deformable probabilistic atlas. A family of high-dimensional volumetric warps relating a new subject's scan to each normal scan in a brain image database is calculated (I—II, above), and then used to quantify local structural variations. Differences in anatomy are recorded in the form of vector field transformations in 3D stereotaxic space that drive both subcortical anatomy and the gyral/sulcal patterns of different subjects into register. The resulting family of warps encodes the distribution in stereotaxic space of anatomic points that correspond across a normal population (III). Their dispersion is used to determine the likelihood (IV) of local regions of the new subject's anatomy being in their actual configuration. Easily interpretable, color-coded topographic maps can then be created to highlight regional patterns of deformity in the anatomy of each new subject [112]. This approach quantifies abnormal structural patterns locally and maps them in three dimensions.

growth ([109]; Fig. 18), and in neurodegenerative disorders such as Alzheimer's disease (Fig. 19; [112,115]). Similar multivariate linear models can be used to test for the effect of explanatory variables (e.g., age, gender, clinical test scores) on a set of deformation field images [5, 47].

enlargement were detected using the theory developed in Thompson et al. [112]. Because of the asymmetrical progression of many degenerative disorders [115], abnormal asymmetry may prove to be an additional, sensitive index of pathology in individual subjects or groups.

4.8 Abnormal Asymmetry

In related work, Thirion et al. [108] applied a warping algorithm to a range of subjects' scans, in each case matching each brain hemisphere with a reflected version of the opposite hemisphere. The resulting asymmetry fields were treated as observations from a spatially parameterized random vector field, and deviations due to lesion growth or ventricular

4.9 Shape Theory Approaches

Deformation fields expressing neuroanatomic differences have also been analyzed with Procrustes methods, developed for the statistical analysis of biological shape [8,11]. In Procrustes methods, affine components of neuroanatomic difference are factored out by rotating and scaling configurations of point landmarks in each subject into least-squares correspondence

0 0

Post a comment