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利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 该项目的总体目标是开发半自动分割方法，以便在7特斯拉的情况下非侵入性地识别和测量海马体的内部结构。基本原理。人类的海马体具有复杂的形状和组织，直到最近，使用1.5T的磁共振成像等标准成像技术都看不到这一点。对这种复杂结构的成像在癫痫和阿尔茨海默病等内侧颞叶疾病中具有重要的临床意义，并且已经表明，在4特斯拉的情况下，可以基于手动体积方法对海马亚区进行成像和测量。通过使用在7特斯拉获得的具有更高信噪比和更好的组织对比度的MR图像，我们的目标是开发半自动的分割工具来识别和测量海马区的不同亚区。这项研究的一个关键结果是提供了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。我们将在7Tesla采集不同体素大小和不同MRI对比度的健康志愿者的海马区图像，以确定识别海马区的最佳采集方案。为了分割人类海马体的内部结构，将开发一种半自动的方法。分割结果的验证将包括不同用户在手动获得的组织分类和利用半自动软件获得的组织分类之间的双盲比较。在后面的步骤中，还将对全自动分割方法进行评估。

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.