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Spatial Normalization (spatial + normalization)

Distribution by Scientific Domains

Medical Sciences	50%

Selected Abstracts

Construction of periventricular white matter hyperintensity maps by spatial normalization of the lateral ventricles

HUMAN BRAIN MAPPING, Issue 7 2009
Cynthia Jongen
Abstract Subcortical and periventricular white matter hyperintensities (WMHs) may have different associations with cognition and pathophysiology. The aim of the present study is to develop an automated method for construction of periventricular WMH maps that enables the analysis of between-group differences in WMH location and characteristics in the periventricular region without the requirement of prior boundary definition. To avoid influence of WMHs on spatial normalization, a reference image of the lateral ventricles was constructed based on images of 24 subjects. Construction was not biased to a single subject. WMHs were segmented by k-nearest neighbor-based classification of magnetic resonance inversion recovery and fluid attenuated inversion recovery images. Cerebrospinal fluid segmentations of individual subjects were nonrigidly mapped to the reference image of the lateral ventricles. The subject's WMHs were transformed to the reference space accordingly. Spatial normalization accuracy was validated using measures of overlap and of displacement relative to the boundary of the lateral ventricles. After spatial normalization, the boundaries of the lateral ventricles closely matched the reference image and in an area of ,1 cm around the lateral ventricles the relative displacement was less than 1 mm. To illustrate the method, it was applied to 61 patients with Type 2 diabetes and 26 control subjects, whereupon periventricular WMH maps were constructed and compared. The proposed method is particularly suited to analyze WMH distribution differences at the level of the lateral ventricles between large groups of patients. Hum Brain Mapp, 2009. © 2008 Wiley-Liss, Inc. [source]

Atlas-Based Anatomic Labeling in Neurodegenerative Disease via Structure-Driven Atlas Warping

JOURNAL OF NEUROIMAGING, Issue 1 2005
Dominik S. Meier PhD
ABSTRACT A new method is presented for the automated anatomic labeling and comparative morphometric analysis of brain magnetic reso nance imaging, warping a prelabeled atlas into congruence with the subject anatomy. The strategy emphasizes anatomically meaningful atlas deformations in the presence of strong degen eration and substantial morphologic differences, for example, cases with high levels of atrophy. The atlas deformation is not driven by image intensity similarities but by continuous anatomic correspondence maps, derived from individual presegmented brain structures. Automatically generated correspondence maps provide large sets of fiducials, driving a warp with many thousand degrees of freedom. Validation included a scan-rescan study and anatomically relevant self-validation in multiple sclerosis patients with substantial cortical and subcortical degeneration. The mean coefficient of variation in the scan-rescan study was 1.4%, with no significant difference in preci sion between normal and neurodegenerative anatomies. The self-validation demonstrated good structural overlap, with substantial improvement over established methods, such as Talairach spatial normalization. [source]

The role of neuroimaging in mild cognitive impairment

NEUROPATHOLOGY, Issue 6 2007
Hiroshi Matsuda
The main purposes of neuroimaging in Alzheimer's disease (AD) have been moved from diagnosis of advanced AD to diagnosis of very early AD at a prodromal stage of mild cognitive impairment, prediction of conversion from mild cognitive impairment (MCI) to AD, and differential diagnosis from other diseases causing dementia. Structural MRI studies and functional studies using F-18 fluorodeoxyglucose-positron emission tomography (FDG-PET) and brain perfusion single-photon emission computed tomography (SPECT) are widely used in diagnosis of AD. Outstanding progress in diagnostic accuracy of these neuroimaging modalities has been obtained using statistical analysis on a voxel-by-voxel basis after spatial normalization of individual scans to a standardized brain-volume template instead of visual inspection or a conventional region of interest technique. In a very early stage of AD, this statistical approach revealed gray matter loss in the entorhinal and hippocampal areas and hypometabolism or hypoperfusion in the posterior cingulate cortex and precuneus. These two findings might be related in view of anatomical knowledge that the regions are linked through the circuit of Papez. This statistical approach also offers prediction of conversion from MCI to AD. Presence of hypometabolism or hypoperfusion in parietal association areas and entorhinal atrophy at the MCI stage has been reported to predict rapid conversion to AD. [source]