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.

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 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fMRI试图识别在特定刺激或任务存在时激活的大脑区域，因此MRI信号中的不稳定性或过度噪声可能导致激活的假阳性或阴性检测。当应用于疾病或药物干预研究中常见的组间比较时，数据质量随时间的变化可能被误解为组间差异。功能磁共振成像的要求通常超过商业制造商质量评估（QA）和预防性维护所涵盖的规格。 为了满足这些需求，我们开发了针对fMRI数据量身定制的QA方法。具体而言，从fMRI数据集计算若干度量以测试时间稳定性、总体信噪比（SNR）和扫描器信号 加标和主题运动。此外，还开发了软件，以挑选资源的数据储存档案。使用一个完全自动化的系统，QA指标进行了评估，超过1000 fMRI研究，自2004年以来进行。如果没有数据检测和分析的完全自动化，这项任务将是无法完成的。 结果表明，尽管商业供应商进行定期维护，但仍观察到扫描仪不稳定的周期。此外，由于实验协议，如成像线圈的选择，在数据质量的差异是显而易见的。有了这些信息，就可以实施防止过度解释不合标准数据的保障措施。对结果的回顾性评估也将用于支持实验方案的优化。