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

识别

• 批准号: 8170482
• 负责人: Pierre-Francois VAN DE MOORTELE
• 金额: $ 1.28万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-06-01 至 2011-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8170482
• 关键词: Alzheimer' s Disease; Biological Markers; Classification; Clinical; Complex; Computer Retrieval of Information on Scientific Projects Database; Computer software; Disease; Double-Blind Method; Epilepsy; Funding; Grant; Hippocampus (Brain); Human; Image; Imaging Techniques; Institution; Magnetic Resonance Imaging; Manuals; Measures; Medial; Methods; Noise; Outcome Study; Patients; Protocols documentation; Research; Research Personnel; Resources; Shapes; Signal Transduction; Source; Structure; Temporal Lobe; Therapeutic; Tissues; United States National Institutes of Health; Validation; base; diagnostic accuracy; follow-up; healthy volunteer; improved; tool

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.

该子项目是利用该技术的众多研究子项目之一 资源由 NIH/NCRR 资助的中心拨款提供。子项目及 研究者 (PI) 可能已从 NIH 的另一个来源获得主要资金， 因此可以在其他 CRISP 条目中表示。列出的机构是 对于中心来说，它不一定是研究者的机构。 该项目的总体目标是开发半自动分割方法，以便在 7 特斯拉时无创地识别和测量海马体的内部结构。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（例如 1.5T MRI）仍无法观察到。对这种复杂结构进行成像对于癫痫和阿尔茨海默病等内侧颞叶疾病具有重要的临床意义，并且已经表明，在 4 特斯拉时，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7特斯拉获得的具有更高信噪比和更好组织对比度的MR图像，我们的目标是开发半自动分割工具来识别和测量海马体的不同子区域。这项研究的一个关键成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。我们将在 7Tesla 下采集具有不同体素大小和不同 MRI 对比度的健康志愿者的海马图像，以确定用于识别海马子区域的最佳采集方案。将开发一种半自动方法来分割人类海马体的内部结构。 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使用半自动软件获得的组织分类之间进行双盲比较。在稍后的步骤中，还将评估全自动分割方法。