Thermal noise-reduction strategies for multi-echo fMRI
Poster No:
1585
Submission Type:
Late-Breaking Abstract Submission
Authors:
Marco Flores-Coronado1, Cesar Caballero-Gaudes2
Institutions:
1BCBL, Donotia, Guipuzkoa, 2Basque Center on Cognition, Brain and Language, San Sebastian, Spain
First Author:
Co-Author:
Late Breaking Reviewer(s):
Introduction:
In BOLD fMRI, head motion and physiological artifacts (e.g., body movement, and respiratory effects) (Satterthwaite et al., 2019) and noise sources (e.g., thermal noise from the scanners) (Wald & Polimeni, 2017) hinder signal quality. Multi-echo fMRI techniques (ME) have demonstrated superior data quality compared to conventional single-echo acquisitions. Combining the signals from different echo times (TEs) amplifies the contrast-to-noise ratio and overcomes signal dropouts (Poser et al., 2006). In addition, ME-based denoising approach, such as ME-ICA (Kundu et al., 2013) can reduce confounding effects due to changes in net magnetisation. Although thermal noise reduction has been explored for single-echo acquisitions with Marchenko-Pastur PCA (mppca) inspired methods (Comby et al., 2023), the optimal integration between ME and thermal noise reduction remains unformulated. Here we investigate several approaches for thermal noise reduction for ME-fMRI data, includingNordic, mppca and tensor-mppca (t-mppca, Herthum & Hetzer, 2024)
Methods:
4 healthy volunteers (2 women) were scanned in a 3T Siemens PrimaFit MAGNETOM MR scanner using a 64-channel head at the Basque Center on Cognition, Brain and Language. Multi-echo fMRI data was collected during two resting state runs with different voxel resolutions (2.4 mm isotropic voxels: TR=1.2s, TE= 11.2/28.1/45/61.9 ms, and 2 mm isotropic voxels: TR=1.7s, TE=13.4/36.1/58.8/81.5 ms, both runs with SMS=5, GRAPPA=2, 65/75 whole-head sagittal slices respectively). Single-band reference (SBRef) images were acquired for each TE. T1-weighted MP2RAGE and T2-weighted Turbo Spin Echo images were collected at 1 mm voxel resolution in each subject. ME-fMRI data was minimally preprocessed with AFNI, including the removal of the 10 first volumes to achieve a steady-state magnetization, image realignment of the first echo to the SBRef and applying this transformation to the other echoes, and T2*-weighed linear (optimal) combination of the echoes using TEDANA (ME-OC). Before this standard ME-fMRI preprocessing, different thermal denoising methods were applied independently to each echo dataset: Nordic, Nordic with only magnitude (Nordic_magn), and mppca. Alternatively applying Nordic over spatially concatenated echoes (Hydra), or applying t-mppca were tested. We hypothesize that reducing thermal noise from echoes simultaneously would better capture thermal noise because we assume it to be similar between echoes.
Results:
Temporal Signal-to-Noise Ratio (T-SNR) maps were computed from OC maps -TSNR=mean(OC)/sd(OC)- As previously reported, Nordic reduced overall signal variance as compared against mppca, or Nordic_magn. Moreover, simultaneous-echo denoising show an increment in TSNR values, whereas t-mppca is the method with the biggest increase overall. In FIgure 1, gray matter maps from a representative subject show a positive increment in overall TSNR values for t-mppca denoising (2mm isotropic voxel) on a representative subject. Additionally we present gray matter TSNR boxplots from all subjects (2mm isotropic voxel).
·TSNR maps and scatter plots from a representative subject at 2mm isotropic voxel. (OC, mppca+OC,Nordic-magn+OC, Nordic+OC, Hydra+OC, tmppca+OC )
Conclusions:
As reported in SE acquisitions, we found an advantage with Nordic against mppca, and nordic_mag. However, simultaneous methods outperform Nordic. We argue that, optimal approach for ME thermal denoising is simultaneously between echoes. Furthermore, doing so over the tensor space greatly improves TSNR values.
Modeling and Analysis Methods:
Exploratory Modeling and Artifact Removal
Methods Development 1
Novel Imaging Acquisition Methods:
BOLD fMRI 2
Keywords:
Data analysis
FUNCTIONAL MRI
MRI
Other - MRI denoising
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Please indicate which methods were used in your research:
For human MRI, what field strength scanner do you use?
Which processing packages did you use for your study?
Provide references using APA citation style.
Community, T. tedana, Ahmed, Z., Bandettini, P. A., Bottenhorn, K. L., Caballero-Gaudes, C., Dowdle, L. T., DuPre, E., Gonzalez-Castillo, J., Handwerker, D., Heunis, S., Kundu, P., Laird, A. R., Markello, R., Markiewicz, C. J., Maullin-Sapey, T., Moia, S., Molfese, P., Salo, T., Staden, I., … Whitaker, K. (2023). ME-ICA/tedana: 23.0.1 (23.0.1) [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.7926293
Herthum, H., & Hetzer, S. (2024). Tensor denoising of quantitative multi-parameter mapping. Magnetic Resonance in Medicine, 92(1), 145–157. https://doi.org/10.1002/mrm.30050
Kundu, P., Voon, V., Balchandani, P., Lombardo, M. V., Poser, B. A., & Bandettini, P. A. (2017). Multi-echo fMRI: A review of applications in fMRI denoising and analysis of BOLD signals. NeuroImage, 154, 59–80. https://doi.org/10.1016/j.neuroimage.2017.03.033
Poser, B. A., Versluis, M. J., Hoogduin, J. M., & Norris, D. G. (2006). BOLD contrast sensitivity enhancement and artifact reduction with multiecho EPI: Parallel-acquired inhomogeneity-desensitized fMRI. Magnetic Resonance in Medicine, 55(6), 1227–1235. https://doi.org/10.1002/mrm.20900
Satterthwaite, T. D., Ciric, R., Roalf, D. R., Davatzikos, C., Bassett, D. S., & Wolf, D. H. (2019). Motion artifact in studies of functional connectivity: Characteristics and mitigation strategies. Human Brain Mapping, 40(7), 2033–2051. https://doi.org/10.1002/hbm.23665
Vizioli, L., Moeller, S., Dowdle, L., Akçakaya, M., De Martino, F., Yacoub, E., & Uğurbil, K. (2021). Lowering the thermal noise barrier in functional brain mapping with magnetic resonance imaging. Nature Communications, 12(1), Article 1. https://doi.org/10.1038/s41467-021-25431-8
Wald, L. L., & Polimeni, J. R. (2017). Impacting the effect of fMRI noise through hardware and acquisition choices – Implications for controlling false positive rates. NeuroImage, 154, 15–22. https://doi.org/10.1016/j.neuroimage.2016.12.057