Blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) studies using parallel imaging to reduce the readout window have reported a loss in temporal signal-to-noise ratio (SNR) that is less than would be expected given a purely thermal noise model. In this study, the impact of parallel imaging on the noise components and functional sensitivity of both BOLD and perfusion-based fMRI data was investigated. Dual-echo arterial spin labeling data were acquired on five subjects using sensitivity encoding (SENSE), at reduction factors (R) of 1, 2 and 3. Direct recording of cardiac and respiratory activity during data acquisition enabled the retrospective removal of physiological noise. The temporal SNR of the perfusion time series closely followed the thermal noise prediction of a radicalR loss in SNR as the readout window was shortened, with temporal SNR values (relative to the R=1 data) of 0.72 and 0.56 for the R=2 and R=3 data, respectively, after accounting for physiological noise. However, the BOLD temporal SNR decreased more slowly than predicted even after accounting for physiological noise, with relative temporal SNR values of 0.80 and 0.63 for the R=2 and R=3 data, respectively. Spectral analysis revealed that the BOLD trends were dominated by low-frequency fluctuations, which were not dominant in the perfusion data due to signal processing differences. The functional sensitivity, assessed using mean F values over activated regions of interest (ROIs), followed the temporal SNR trends for the BOLD data. However, results for the perfusion data were more dependent on the threshold used for ROI selection, most likely due to the inherently low SNR of functional perfusion data.
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F-Florbetaben (FBB) uptake in the supratentorial cortex is indicative of amyloid positivity. Due to PET's low spatial resolution, image noise, and spill-over of signal from adjacent white-matter into gray-matter, there are inconsistencies in ratings among trained readers. A set of 264 F-Florbetaben (amyloid) PET/MRI exams were reconstructed using conventional ordered subset expectation maximization (OSEM) method and MR-guided block sequential regularized expectation maximization (MRgBSREM) method.
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Amrita School for Sustainable Futures, Amrita Vishwa Vidyapeetham, Amritapuri, 690525, Kerala, India.
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View Article and Find Full Text PDFFront Biosci (Landmark Ed)
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