Modelling temporal stability of EPI time series using magnitude images acquired with multi-channel receiver coils.
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2012-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC3527382?pdf=render |
| _version_ | 1818941539390324736 |
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| author | Chloe Hutton Evelyne Balteau Antoine Lutti Oliver Josephs Nikolaus Weiskopf |
| author_facet | Chloe Hutton Evelyne Balteau Antoine Lutti Oliver Josephs Nikolaus Weiskopf |
| author_sort | Chloe Hutton |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-20T06:57:09Z |
| format | Article |
| id | doaj.art-83d9738cb382417da667f60215337d94 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-20T06:57:09Z |
| publishDate | 2012-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-83d9738cb382417da667f60215337d942022-12-21T19:49:19ZengPublic Library of Science (PLoS)PLoS ONE1932-62032012-01-01712e5207510.1371/journal.pone.0052075Modelling temporal stability of EPI time series using magnitude images acquired with multi-channel receiver coils.Chloe HuttonEvelyne BalteauAntoine LuttiOliver JosephsNikolaus WeiskopfIn 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.http://europepmc.org/articles/PMC3527382?pdf=render |
| spellingShingle | Chloe Hutton Evelyne Balteau Antoine Lutti Oliver Josephs Nikolaus Weiskopf Modelling temporal stability of EPI time series using magnitude images acquired with multi-channel receiver coils. PLoS ONE |
| title | Modelling temporal stability of EPI time series using magnitude images acquired with multi-channel receiver coils. |
| title_full | Modelling temporal stability of EPI time series using magnitude images acquired with multi-channel receiver coils. |
| title_fullStr | Modelling temporal stability of EPI time series using magnitude images acquired with multi-channel receiver coils. |
| title_full_unstemmed | Modelling temporal stability of EPI time series using magnitude images acquired with multi-channel receiver coils. |
| title_short | Modelling temporal stability of EPI time series using magnitude images acquired with multi-channel receiver coils. |
| title_sort | modelling temporal stability of epi time series using magnitude images acquired with multi channel receiver coils |
| url | http://europepmc.org/articles/PMC3527382?pdf=render |
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