Article: article from journal or magazin.
Modelling temporal stability of EPI time series using magnitude images acquired with multi-channel receiver coils.
Publication types: Journal Article ; Research Support, Non-U.S. Gov'tPublication Status: ppublish
In 2001, Krueger and Glover introduced a model describing the temporal SNR (tSNR) of an EPI time series as a function of image SNR (SNR(0)). This model has been used to study physiological noise in fMRI, to optimize fMRI acquisition parameters, and to estimate maximum attainable tSNR for a given set of MR image acquisition and processing parameters. In its current form, this noise model requires the accurate estimation of image SNR. For multi-channel receiver coils, this is not straightforward because it requires export and reconstruction of large amounts of k-space raw data and detailed, custom-made image reconstruction methods. Here we present a simple extension to the model that allows characterization of the temporal noise properties of EPI time series acquired with multi-channel receiver coils, and reconstructed with standard root-sum-of-squares combination, without the need for raw data or custom-made image reconstruction. The proposed extended model includes an additional parameter κ which reflects the impact of noise correlations between receiver channels on the data and scales an apparent image SNR (SNR'(0)) measured directly from root-sum-of-squares reconstructed magnitude images so that κ = SNR'(0)/SNR(0) (under the condition of SNR(0)>50 and number of channels ≤32). Using Monte Carlo simulations we show that the extended model parameters can be estimated with high accuracy. The estimation of the parameter κ was validated using an independent measure of the actual SNR(0) for non-accelerated phantom data acquired at 3T with a 32-channel receiver coil. We also demonstrate that compared to the original model the extended model results in an improved fit to human task-free non-accelerated fMRI data acquired at 7T with a 24-channel receiver coil. In particular, the extended model improves the prediction of low to medium tSNR values and so can play an important role in the optimization of high-resolution fMRI experiments at lower SNR levels.
Algorithms, Computer Simulation, Humans, Image Enhancement, Magnetic Resonance Imaging/methods, Models, Theoretical, Monte Carlo Method, Neuroimaging, Phantoms, Imaging, Reproducibility of Results
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