IDENTI & VOLUMETRIC ANALYSIS OF THE INTERNAL STRUCTURE OF THE HIPPOCAMPUS AT 7T

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This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. The overall aim of this project is to develop semi automatic segmentation methods in order to non invasively identify and measure the internal structures of the hippocampus at 7Tesla. Rationale. The human hippocampus has a complex shape and organization which, until recently, was not visible using standard imaging techniques such as MRI at 1.5T. Imaging this complex structure has important clinical implications in diseases of the medial temporal lobe such as epilepsy and Alzheimer's disease, and it has been shown that at 4 Tesla hippocampal subfields can be imaged and measured based on manual volumetric methods. By using MR images obtained at 7 Tesla, with higher signal to noise ratio and better tissue contrast, we aim at developing semi automatic, segmentation tools to identify and measure the different sub-regions of the hippocampus. A critical outcome of this study is to provide non invasive biomarkers that could improve diagnosis accuracy and therapeutic follow up in epileptic patients. Objectives. We will acquire images of the hippocampus at 7Tesla in healthy volunteers with different voxel size and different MRI contrast in order to determine an optimal acquisition protocol for the purpose of identifying hippocampus subfields. A semi-automatic method will be developed in order to segment the internal structure of the human hippocampus. The validation of the segmentation results will include double blind comparisons by different users between tissue classification obtained manually and obtained with the semi automatic software. In a later step a fully automatic segmentation approach will also be evaluated.