Contrast-to-noise ratio (CNR) as a quality parameter in fMRI
Contrast-to-noise ratio (CNR) as a quality parameter in fMRI
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DOI：
10.1002/jmri.20935
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发表时间：
2007-06-01
影响因子：
4.4
通讯作者：
Barth, Markus
中科院分区：
文献类型：
--
作者：
Geissler, Alexander;Gartus, Andreas;Barth, Markus
目的：评估数据质量对功能磁共振成像 (fMRI) 中大脑激活定位的影响，并探讨时间对比度噪声比 (CNR) 是否提供了估计 fMRI 质量的定量参数。材料和方法：我们通过在单次运行、单次会话以及分组基础上进行比较，研究了两种定义 CNR 的方法。使用两种不同的策略计算健康受试者和一组脑损伤患者的 CNR：一种基于一般线性模型 (GLM) 分析 (CNR_SPM)，另一种充当自适应低通滤波器并假设高频分量包含时间噪声 (CNR_SG)。使用通用排除标准（2 x 标准差 (SD)）将具有低 CNR 的运行识别为异常值。结果：两种 CNR 方法的结果高度相关。在受试者和患者之间以及受试者和患者之间，CNR 显示出相当大的差异，但健康受试者组和患者组之间的平均 CNR 没有差异。总共，使用 CNR_SG 时必须排除 213 次运行中的 7 次（占所有运行的 3.3%），使用 CNR_SPM 时必须排除 213 次运行中的 14 次（占所有运行的 3.3%）。结论：使用自适应低通滤波器计算 CNR 可以为基于 GLM 的方法提供结果，并且对于血流动力学响应函数 (HRF) 与常见假设显着不同的情况可能是有利的。 CNR 可用于识别数据质量不足的会话。 CNR 可作为定量和直观的参数来评估临床 fMRI 研究的性能和质量，包括功能性能（对比度）和数据质量（系统和生理学引起的噪声）的信息。
Purpose: To evaluate the impact of data quality on the localization of brain activation in functional magnetic resonance imaging (fMRI) and to explore whether the temporal contrast-to-noise-ratio (CNR) provides a quantitative parameter to estimate fMRI quality.Materials and Methods: We investigated two methods for defining the CNR by comparing them on a single-run, single session, as well as on a group-wise basis. The CNRs of healthy subjects and a group of patients with brain lesions were calculated using two different strategies: one based on a general linear model (GLM) analysis (CNR_SPM), and one that acts as an adaptive low-pass filter and assumes that a high-frequency components contain the temporal noise (CNR_SG). Runs with low CNR were identified as outliers using a common exclusion criterion (2 x standard deviation (SD)).Results: The results of the two CNR methods are highly correlated. Both between and within subjects and patients the CNR showed quite large variations but the average CNR did not differ between a group of healthy subjects and a patient group. In total, seven of 213 runs (3.3% of all runs) had to be excluded when CNR_SG was used, and 14 of 213 (6.6%) runs had to be excluded when CNR_SPM was used.Conclusions: Calculating the CNR using an adaptive low-pass filter gives results to a GLM-based approach and could be advantageous for cases in which the hemodynamic response function (HRF) differs significantly from common assumptions. The CNR can be used to identify sessions with insufficient data quality. The CNR may serve as a quantitative and intuitive parameter to assess the performance and quality of clinical fMRI investigations, including information on both functional performance (contrast) and data quality (noise caused by the system and physiology).