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

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 该项目的总体目标是开发半自动分割方法，以非侵入性地识别和测量海马在7特斯拉的内部结构。理由。人类海马体具有复杂的形状和组织，直到最近，使用标准成像技术（如1.5T的MRI）还无法看到。对这种复杂结构进行成像在内侧颞叶疾病如癫痫和阿尔茨海默病中具有重要的临床意义，并且已经表明，在4特斯拉下，可以基于手动体积方法对海马子场进行成像和测量。通过使用在7特斯拉获得的MR图像，具有更高的信噪比和更好的组织对比度，我们的目标是开发半自动分割工具来识别和测量海马的不同子区域。这项研究的一个关键结果是提供非侵入性生物标志物，可以提高癫痫患者的诊断准确性和治疗随访。目标.我们将采集健康志愿者海马在7特斯拉的图像，不同的体素大小和不同的MRI对比度，以确定一个最佳的采集协议的目的，识别海马子字段。将开发一种半自动方法，以分割人类海马的内部结构。 分割结果的确认将包括由不同用户对手动获得的组织分类和使用半自动软件获得的组织分类进行双盲比较。在稍后的步骤中，还将评估全自动分割方法。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. 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.