%0 Journal Article %1 10.1371/journal.pone.0052075 %A Hutton, Chloe %A Balteau, Evelyne %A Lutti, Antoine %A Josephs, Oliver %A Weiskopf, Nikolaus %D 2012 %I Public Library of Science %J PLOS ONE %K archiv %N 12 %P 1-10 %R 10.1371/journal.pone.0052075 %T Modelling Temporal Stability of EPI Time Series Using Magnitude Images Acquired with Multi-Channel Receiver Coils %U https://doi.org/10.1371/journal.pone.0052075 %V 7 %X 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.

@article{10.1371/journal.pone.0052075, 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.}, added-at = {2022-07-22T10:40:44.000+0200}, author = {Hutton, Chloe and Balteau, Evelyne and Lutti, Antoine and Josephs, Oliver and Weiskopf, Nikolaus}, biburl = {https://www.bibsonomy.org/bibtex/26504111e4d6012e24a48c290694bec9b/poerdy}, doi = {10.1371/journal.pone.0052075}, interhash = {1dd595539b82deb6ebb63748979254fa}, intrahash = {6504111e4d6012e24a48c290694bec9b}, journal = {PLOS ONE}, keywords = {archiv}, month = {12}, number = 12, pages = {1-10}, publisher = {Public Library of Science}, timestamp = {2022-07-22T10:40:44.000+0200}, title = {Modelling Temporal Stability of EPI Time Series Using Magnitude Images Acquired with Multi-Channel Receiver Coils}, url = {https://doi.org/10.1371/journal.pone.0052075}, volume = 7, year = 2012 }