Automated ventricular mapping with multi-atlas fluid image alignment reveals genetic effects in Alzheimer's disease

Abstract

We developed and validated a new method to create automated 3D parametric surface models of the lateral ventricles in brain MRI scans, providing an efficient approach to monitor degenerative disease in clinical studies and drug trials. First, we used a set of parameterized surfaces to represent the ventricles in four subjects' manually labeled brain MRI scans (atlases). We fluidly registered each atlas and mesh model to MRIs from 17 Alzheimer's disease (AD) patients and 13 age-and gendermatched healthy elderly control subjects, and 18 asymptomatic ApoE4-carriers and 18 age-and gender-matched non-carriers. We examined genotyped healthy subjects with the goal of detecting subtle effects of a gene that confers heightened risk for Alzheimer's disease. We averaged the meshes extracted for each 3D MR data set, and combined the automated segmentations with a radial mapping approach to localize ventricular shape differences in patients. Validation experiments comparing automated and expert manual segmentations showed that (1) the Hausdorff labeling error rapidly decreased, and (2) the power to detect disease-and gene-related alterations improved, as the number of atlases, N, was increased from 1 to 9. In surface-based statistical maps, we detected more widespread and intense anatomical deficits as we increased the number of atlases. We formulated a statistical stopping criterion to determine the optimal number of atlases to use. Healthy ApoE4-carriers and those with AD showed local ventricular abnormalities. This high-throughput method for morphometric studies further motivates the combination of genetic and neuroimaging strategies in predicting AD progression and treatment response.

Improving intersubject image registration using tissue-class information benefits robustness and accuracy of multi-atlas based anatomical segmentation

“…Nevertheless, in applications that require delineation of a single region of interest, specialized approaches may be superior. For the lateral ventricles, Chou et al (2008) present a multi-atlas label propagation approach where the transformed labels are converted into mesh-based surface models and averaged. The resulting segmentations show a high level of agreement with reference segmentations and reveal ventricular shape differences related to the subject's genetic makeup.…”

Impact of Common Variations in PLD3 on Neuroimaging Phenotypes in Non-demented Elders

“…In regional volume analysis, rs10407447 also correlated with bilateral inferior lateral ventricle and lateral ventricle volume, which had been proved to be affected by atrophy process in subjects with AD and MCI [22,23]. Progressive lateral Fig.…”

Hyperbolic Ricci Flow and Its Application in Studying Lateral Ventricle Morphometry

“…The lateral ventricles -fluid-filled structures deep in the brain -are often enlarged in disease and can provide sensitive measures of disease progression [7,57,58,59]. Ventricular changes reflect atrophy in surrounding structures, and ventricular measures and surface-based maps can provide sensitive assessments of tissue reduction that correlate with cognitive deterioration in illnesses.…”