The bumps on the hippocampus

Yi Gao; Lawrence Ver Hoef

doi:10.1117/12.2216095

21 March 2016 The bumps on the hippocampus

Yi Gao, Lawrence Ver Hoef

Proceedings Volume 9784, Medical Imaging 2016: Image Processing; 97840I (2016) https://doi.org/10.1117/12.2216095
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

The hippocampus has been the focus of more imaging research than any other subcortical structure in the human brain. However a feature that has been almost universally overlooked are the bumpy ridges on the inferior aspect of the hippocampus, which we refer to as hippocampal dentation. These bumps arise from folds in the CA1 layer of Ammon's horn. Similar to the folding of the cerebral cortex, hippocampal dentation allows for greater surface area in a confined space. However, while quantitative studies of radiologic brain images have been advancing for decades, examining numerous approaches to hippocampal segmentation and morphology analysis, virtually all published 3D renderings of the hippocampus show the under surface to be quite smooth or mildly irregular; we have rarely seen the characteristic bumpy structure in the reconstructed 3D scene, one exception being the 9.4T postmortem study. This is presumably due to the fact that, based on our experience with high resolution images, there is a dramatic degree of variability in hippocampal dentation between individuals from very smooth to highly dentated. An apparent question is, does this indicate that this specific morphological signature can only be captured using expensive ultra-high field techniques? Or, is such information buried in the data we commonly acquire, awaiting a computation technique that can extract and render it clearly? In this study, we propose a super-resolution technique that captures the fine scale morphometric features of the hippocampus based on common T1-weighted 3T MR images.

Citation Download Citation

Yi Gao and Lawrence Ver Hoef "The bumps on the hippocampus", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97840I (21 March 2016); https://doi.org/10.1117/12.2216095

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