Background: While applications of real-time functional magnetic resonance imaging (rtfMRI) are growing rapidly, there are still limitations in real-time data processing compared to off-line analysis.
New methods: We developed a proof-of-concept real-time fMRI processing (rtfMRIp) system utilizing a personal computer (PC) with a dedicated graphic processing unit (GPU) to demonstrate that it is now possible to perform intensive whole-brain fMRI data processing in real-time. The rtfMRIp performs slice-timing correction, motion correction, spatial smoothing, signal scaling, and general linear model (GLM) analysis with multiple noise regressors including physiological noise modeled with cardiac (RETROICOR) and respiration volume per time (RVT).
Results: The whole-brain data analysis with more than 100,000voxels and more than 250volumes is completed in less than 300ms, much faster than the time required to acquire the fMRI volume. Real-time processing implementation cannot be identical to off-line analysis when time-course information is used, such as in slice-timing correction, signal scaling, and GLM. We verified that reduced slice-timing correction for real-time analysis had comparable output with off-line analysis. The real-time GLM analysis, however, showed over-fitting when the number of sampled volumes was small.
Comparison with existing methods: Our system implemented real-time RETROICOR and RVT physiological noise corrections for the first time and it is capable of processing these steps on all available data at a given time, without need for recursive algorithms.
Conclusions: Comprehensive data processing in rtfMRI is possible with a PC, while the number of samples should be considered in real-time GLM.
Keywords: Functional connectivity; Image processing; Neurofeedback; Physiological noise correction; Real-time fMRI processing.
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