Abstract
In 2001, Krueger and Glover introduced a model describing the temporal SNR (tSNR) of an EPI time series as a function of image SNR (SNR0). 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/SNR0 (under the condition of SNR0>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 SNR0 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.
Highlights
The optimization of EPI acquisition parameters is important for maximizing the sensitivity to relatively small BOLD signal changes in fMRI studies
We extend the model proposed in [1] to allow the temporal SNR (tSNR) of an EPI time course acquired without acceleration using multi-channel receiver coils to be characterized, without the necessity for complex custom-made image reconstruction of absolute image SNR estimates
In the following we extend the model so that instead of the actual SNR0, the apparent SNR90 measured from RSS combined magnitude images using the approach proposed by [8] can be used
Summary
The optimization of EPI acquisition parameters is important for maximizing the sensitivity to relatively small BOLD signal changes in fMRI studies. Factors such as spatial resolution, echo time (TE), flip angle, and parallel imaging strategies can be optimized for a given magnetic field strength and receiver coil arrangement. These factors can influence the contribution of physiological noise, for example from cardiac and respiratory functions as well as motion, to the temporal noise, thereby impacting on BOLD sensitivity. This approach is inefficient and in many cases even impossible, since it requires export, storage and computation of large amounts of raw data (e.g. ,70 GB for ,10 minutes of highresolution fMRI acquired using a 32-channel receiver coil)
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