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.

该副本是利用众多研究子项目之一 由NIH/NCRR资助的中心赠款提供的资源。子弹和 调查员（PI）可能已经从其他NIH来源获得了主要资金， 因此可以在其他清晰的条目中代表。列出的机构是 对于中心，这不一定是调查员的机构。 功能磁共振成像（fMRI）是MRI的专门应用，与常规临床研究相比，对扫描仪性能和稳定性提出了更多要求。由于fMRI试图在特定刺激或任务的存在下识别激活的大脑区域，因此MRI信号中的不稳定性或过度噪声会导致误报或阴性检测激活。当对疾病或药理干预研究中通常情况下，将其应用于组比较时，随着时间的推移，数据质量的变化可能会被误解为组之间的差异。 FMRI的要求通常超出了商业制造商质量评估（QA）和预防性维护的规格。 为了满足这些需求，我们开发了针对fMRI数据量身定制的质量检查方法。具体而言，从fMRI数据集中计算了几个指标，以测试时间稳定性，整体信噪比（SNR）和扫描仪信号尖峰和受试者运动。此外，开发软件是为了收集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研究进行了评估，对质量检查指标进行了评估。如果没有完全自动化数据检测和分析，则该任务是无法克服的。 结果表明，尽管商业提供商定期维护，但仍观察到扫描仪不稳定的时期。此外，由于实验方案而引起的数据质量差异（例如选择成像线圈）很容易显而易见。有了这些信息，可以实施防止过度解释不合格数据的保障措施。对结果的回顾性评估也将用于支持实验方案的优化。