RETROSPECTIVE QUALITY ASSESSMENT OF FMRI DATA

FMRI 数据的回顾性质量评估

• 批准号: 7600809
• 负责人: MARK A ELLIOTT
• 金额: $ 1.65万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-06-01 至 2008-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7600809
• 关键词: Address; Archives; Automation; Brain region; Clinical Research; Computer Retrieval of Information on Scientific Projects Database; Computer software; Data; Data Quality; Data Set; Data Storage and Retrieval; Detection; Disease; Functional Magnetic Resonance Imaging; Funding; Grant; Image; Institution; Intervention; Lead; Magnetic Resonance Imaging; Maintenance; Manufacturer Name; Methods; Metric; Motion; Noise; Performance; Protocols documentation; Provider; Research; Research Personnel; Resources; Signal Transduction; Source; Stimulus; System; Testing; Time; United States National Institutes of Health; comparison group

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. Functional magnetic resonance imaging (fMRI) is a specialized application of MRI which places additional demands on scanner performance and stability compared to routine clinical studies. Since fMRI attempts to identify brain regions activated in the presence of particular stimuli or tasks, instabilities or excessive noise in the MRI signal can lead to either false positive or negative detection of activation. When applied to group comparisons as is commonly the case in the study of disease or pharmacological interventions, changes in data quality over time can be misinterpreted as differences between groups. The requirements for fMRI generally exceed the specifications covered by commercial manufacturer quality assessment (QA) and preventative maintenance. To address these needs, we have developed QA methods tailored for fMRI data. Specifically, several metrics are computed from an fMRI data set to test for temporal stability, overall signal-to-noise ratio (SNR), and scanner signal spiking, and subject motion. Moreover, software was developed to cull the data storage archives of the Resource. Using a completely automated system, the QA metrics were assessed on over 1000 fMRI studies that have been conducted since 2004. This task would be insurmountable without complete automation of data detection and analysis. The results reveal that periods of scanner instability are observed, despite regular maintenance by the commercial provider. In addition, differences in data quality due to experimental protocol, such as selection of imaging coil, are readily apparent. With this information, safeguards against over-interpretation of substandard data can be implemented. Retrospective assessment of the results will also be used to support optimization of experimental protocols.

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。子项目和 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。 功能性磁共振成像 (fMRI) 是 MRI 的一种特殊应用，与常规临床研究相比，它对扫描仪的性能和稳定性提出了额外的要求。由于功能磁共振成像试图识别在存在特定刺激或任务时激活的大脑区域，因此 MRI 信号中的不稳定或过多噪声可能会导致激活检测的误报或阴性。当应用于疾病或药物干预研究中常见的分组比较时，数据质量随时间的变化可能会被误解为组间差异。 fMRI 的要求通常超出商业制造商质量评估 (QA) 和预防性维护所涵盖的规范。 为了满足这些需求，我们开发了针对 fMRI 数据的 QA 方法。具体来说，根据功能磁共振成像数据集计算几个指标，以测试时间稳定性、总体信噪比 (SNR)、扫描仪信号尖峰和对象运动。此外，还开发了软件来剔​​除资源的数据存储档案。使用完全自动化的系统，对自 2004 年以来进行的 1000 多项 fMRI 研究进行了 QA 指标评估。如果没有完全自动化的数据检测和分析，这项任务将难以完成。 结果表明，尽管商业提供商定期进行维护，但仍观察到扫描仪不稳定的时期。此外，由于实验协议（例如成像线圈的选择）造成的数据质量差异是显而易见的。有了这些信息，就可以防止对不合格数据的过度解释。结果的回顾性评估也将用于支持实验方案的优化。