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J Pruessner

Publications and source records attributed to J Pruessner.

4 recordsLinked to original sources
PubMed2003

[Volumetric MRI for evaluation of regional pattern and progressin of neocortical degeneration in Alzheimer's disease].

PURPOSE: Volumetric analysis of the corpus callosum and hippocampus using MRI in Alzheimer's disease (AD) to evaluate the regional pattern and progression of neocortical neurodegeneration. METHODS: In subsequent studies we investigated patients with AD and healthy controls. Volumetry was based on MRI-data from a sagittal 3D T1w-gradient echo sequence. The corpus callosum (CC) was measured in a midsagittal slice, and subdivided into 5 subregions. Volumetry of the hippocampus/amygdala-formation (HAF) was performed by segmentation in coronary reoriented slices. RESULTS: In AD patients we found a significant atrophy in the rostrum und splenium of CC. The atrophy was correlated with the severity of dementia, but no correlation was found with the load of white matter lesions. In comparison with (18)FDG-PET, we found a significant correlation of regional CC-atrophy with the regional decline of cortical glucose metabolism. A ROC-analysis demonstrated no significant differences in the diagnostic accuracy of HAF volumetry and regional CC volumetry of the splenium (region C5) even in mild stages of dementia. CONCLUSION: Regional atrophy of CC can be used as a marker of neocortical degeneration even in early stages of dementia in AD.

Alzheimer Disease↗
PubMed2002

Appearance-based segmentation of medial temporal lobe structures.

A new paradigm for the characterization of structure appearance is proposed, based on a combination of gray-level MRI intensity data and a shape descriptor derived from a priori principal components analysis of 3D deformation vector fields. Generated without external intervention, it extends into 3D more classic, 2D manual landmark-based shape models. Application of this novel concept led to a method for the segmentation of medial temporal lobe structures from brain magnetic resonance images. The strategy employed for segmentation aims at synthesizing, using the appearance model, a deformation field that maps a new volume onto a reference target. Any information defined on the reference can then be propagated back on the new volume instance, thereby achieving segmentation. The proposed method was tested on a data set of 80 normal subjects and compared against manual segmentation as well as automated segmentation results from ANIMAL, a nonlinear registration and segmentation technique. Experimental results demonstrated the robustness and flexibility of the new method. Segmentation accuracy, measured by overlap statistics, is marginally lower (< 2%) than ANIMAL, while processing time is six times faster. Finally, the applicability of this concept toward shape deformation analysis is presented.

Algorithms↗