PARTNERSHIP FOR MR SPECTROSCOPIC IMAGING DATA PROCESSING

描述（由申请人提供）：MR光谱成像（MRSI）能够 非侵入性测量多种组织代谢物分布， 提供了作为诊断成像技术的相当大的潜力。广泛 MRSI的采用受到数据处理复杂要求的限制 和分析，这最好需要密切结合已知的光谱和 空间信息，包括MRI衍生的组织分割、形态学 分析，代谢产物NMR特征，以及正常代谢产物的详细知识。 组织代谢物分布。该生物医学研究伙伴关系将 解决这一局限性，并通过开发一种 一套集成的数据处理工具，强调相当程度的自动化 和常规诊断成像研究的适用性。这一努力将 联合收割机结合MRSI和MRI数据处理的多个专业领域， 项目分布在4个机构。将开发软件工具， 自动化MRSI处理、组织分割、脑区域映射、 统计分析和临床表现。由此产生的技术 然后，将在几个合作伙伴之间分享发展情况， 美国的医学研究中心欧洲和日本， 将评估诊断神经影像学应用，重点是 肿瘤、癫痫和神经退行性疾病的1H MRSI。结果 代谢物成像研究将转换为标准化强度单位 并转换为标准化的空间坐标，使数据能够 合并以形成MR测量的人体代谢物值的数据库， 采集、空间和主体参数。这些信息将 用于增强个体MRSI研究的统计分析。发达 方法将有助于增加MRSI用于诊断成像，鼓励 标准化MRSI采集、处理和分析的发展 方法，并绘制人脑中的代谢物分布图。

DESCRIPTION (provided by applicant): MR Spectroscopic Imaging (MRSI) enables non-invasive measurement of a number of tissue metabolite distributions and offers considerable potential as a diagnostic imaging technique. Widespread adoption of MRSI has been limited by complex requirements for data processing and analysis, which optimally require close integration of known spectral and spatial information, including MRI-derived tissue segmentation, morphological analysis, metabolite NMR characteristics, and detailed knowledge of normal tissue metabolite distributions. This Biomedical Research Partnership will address this limitation and increase the effectiveness of MRSI by developing an integrated set of data processing tools that emphasizes considerable automation and suitability for routine diagnostic imaging studies. This effort will combine multiple areas of expertise in MRSI and MRI data processing under 5 projects located at 4 institutions. Software tools will be developed for automated MRSI processing, tissue segmentation, brain region mapping, statistical analysis, and clinical presentation. The resultant technical developments will then be shared among several partners at collaborating medical research centers in the U.S.A., Europe, and Japan, where the package will be evaluated for diagnostic neuroimaging applications, with an emphasis on 1H MRSI of cancer, epilepsy and neurodegenerative disease. Results from metabolite imaging studies will be converted to standardized intensity units and transformed into normalized spatial coordinates, enabling the data to be pooled to form a database of MR-measured human metabolite values as a function of acquisition, spatial, and subject parameters. This information will then be used to enhance statistical analysis of individual MRSI studies. The developed methods will facilitate increased use of MRSI for diagnostic imaging, encourage the development of standardized MRSI acquisition, processing, and analysis methods, and map metabolite distributions in human brain.