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 the 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. CONCLUSION: Calculating the CNR using an adaptive low-pass filter gives similar 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 and exclude runs with suboptimal CNR, and 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). (c) 2007 Wiley-Liss, Inc.
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 the 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. CONCLUSION: Calculating the CNR using an adaptive low-pass filter gives similar 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 and exclude runs with suboptimal CNR, and 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). (c) 2007 Wiley-Liss, Inc.
Authors: Peter E Yoo; Jon O Cleary; Scott C Kolbe; Roger J Ordidge; Terence J O'Brien; Nicholas L Opie; Sam E John; Thomas J Oxley; Bradford A Moffat Journal: MAGMA Date: 2018-05-29 Impact factor: 2.310
Authors: R Beisteiner; S Robinson; M Wurnig; M Hilbert; K Merksa; J Rath; I Höllinger; N Klinger; Ch Marosi; S Trattnig; A Geissler Journal: Neuroimage Date: 2011-05-17 Impact factor: 6.556