PURPOSE: To assess and model signal fluctuations induced by non-T(1)-related confounds in variable repetition time (TR) functional magnetic resonance imaging (fMRI) and to develop a compensation procedure to correct for the non-T(1)-related artifacts. MATERIALS AND METHODS: Radiofrequency disabled volume gradient sequences were effected at variable offsets between actual image acquisitions, enabling perturbation of the measurement system without perturbing longitudinal magnetization, allowing the study of non-T(1)-related confounds that may arise in variable TR experiments. Three imaging sessions utilizing a daily quality assurance (DQA) phantom were conducted to assess the signal fluctuations, which were then modeled as a second-order system. A modified projection procedure was implemented to correct for signal fluctuations arising from non-T(1)-related confounds, and statistical analysis was performed to assess the significance of the artifacts with and without compensation. RESULTS: Assessment using phantom data reveals that the signal fluctuations induced by non-T(1)-related confounds was consistent in shape across the phantom and well-modeled by a second-order system. The phantom exhibited significant spurious detections (at P < 0.01) almost uniformly across the central slices of the phantom. CONCLUSION: Second-order system modeling and compensation of non-T(1)-related confounds achieves significant reduction of spurious detection of fMRI activity in a phantom.
PURPOSE: To assess and model signal fluctuations induced by non-T(1)-related confounds in variable repetition time (TR) functional magnetic resonance imaging (fMRI) and to develop a compensation procedure to correct for the non-T(1)-related artifacts. MATERIALS AND METHODS: Radiofrequency disabled volume gradient sequences were effected at variable offsets between actual image acquisitions, enabling perturbation of the measurement system without perturbing longitudinal magnetization, allowing the study of non-T(1)-related confounds that may arise in variable TR experiments. Three imaging sessions utilizing a daily quality assurance (DQA) phantom were conducted to assess the signal fluctuations, which were then modeled as a second-order system. A modified projection procedure was implemented to correct for signal fluctuations arising from non-T(1)-related confounds, and statistical analysis was performed to assess the significance of the artifacts with and without compensation. RESULTS: Assessment using phantom data reveals that the signal fluctuations induced by non-T(1)-related confounds was consistent in shape across the phantom and well-modeled by a second-order system. The phantom exhibited significant spurious detections (at P < 0.01) almost uniformly across the central slices of the phantom. CONCLUSION: Second-order system modeling and compensation of non-T(1)-related confounds achieves significant reduction of spurious detection of fMRI activity in a phantom.
Authors: A R Guimaraes; J R Melcher; T M Talavage; J R Baker; P Ledden; B R Rosen; N Y Kiang; B C Fullerton; R M Weisskoff Journal: Hum Brain Mapp Date: 1998 Impact factor: 5.038