Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
452-02-017 / 452-02-017 ISMRM Abstract

Denoising submillimeter functional MRI with BM4D-PC

Primary: Brain Function and fMRI - fMRI Analysis
Secondary: Brain Function and fMRI - Task/Intervention Based fMRI
452-02-017 · Methodology: Acquisition and Analysis · Tuesday, 12 May, 1:40 PM–3:16 PM · Power Pitch Theatre 2
452-02-017 · Methodology: Acquisition and Analysis · Tuesday, 12 May, 1:40 PM–3:16 PM · Power Pitch Theatre 2
Keywords: Functional magnetic resonance imaging Denoising BOLD-fMRI BM4D-PC NORDIC
Accepted
Vinicius P Campos1, Tales Santini2, Diego Szczupak1, Lovisa LjungQvist-Brinson1,3,4, Cong Chu2, Isabela Zimmermann Rollin2, Michael R Corigliano1,3,4, Laurel Dieckhaus5, David J Schaeffer1,2, Tamer Ibrahim2, Afonso C Silva1,2,3,4,6
1Neurobiology, University of Pittsburgh, Pittsburgh, United States of America
2Bioengineering, University of Pittsburgh, Pittsburgh, United States of America
3Center for Neuroscience, University of Pittsburgh, Pittsburgh, United States of America
4Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, United States of America
5Cardiology, University of Pittsburgh, Pittsburgh, United States of America
6Aging Institute, University of Pittsburgh, Pittsburgh, United States of America
Presenting Author: Madison Lewis

Synopsis

Motivation:
Goals:
Approach:
Results:
Full abstract & presentation

The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.

Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.

To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.

Log in

References

1. Vizioli L, Moeller S, Dowdle L, et al. Lowering the thermal noise barrier in functional brain mapping with magnetic resonance imaging. Nat Commun. 2021;12(1):5181. https://doi.org/10.1038/s41467-021-25431-8 [doi]
2. Bandettini PA, Wong EC, Hinks RS, Tikofsky RS, Hyde JS. Time course EPI of human brain function during task activation. Magn Reson Med. 1992;25(2):390-397. https://doi.org/10.1002/mrm.1910250220 [doi]
3. De Martino F, Yacoub E, Kemper V, et al. The impact of ultra-high field MRI on cognitive and computational neuroimaging. Neuroimage. 2018;168:366-382. https://doi.org/10.1016/j.neuroimage.2017.03.060 [doi]
4. Faes LK, Lage-Castellanos A, Valente G, et al. Evaluating the effect of denoising submillimeter auditory fMRI data with NORDIC. Imaging Neurosci. 2024;2:1-18. https://doi.org/10.1162/imag_a_00270 [doi]
5. Moeller S, Pisharady PK, Ramanna S, et al. NOise reduction with DIstribution Corrected (NORDIC) PCA in dMRI with complex-valued parameter-free locally low-rank processing. Neuroimage. 2021;226:117539. https://doi.org/10.1016/j.neuroimage.2020.117539 [doi]
6. Campos VP, Szczupak D, Santini T, et al. Denoising diffusion MRI data: Principal components meet non-local block-matching. In Proceedings of Intl. Soc. Mag. Reson. Med. 33 (2025). https://doi.org/10.58530/2025/4783 [doi]
7. Maggioni M, Katkovnik V, Egiazarian K, Foi A. Nonlocal transform-domain filter for volumetric data denoising and reconstruction. IEEE Trans Image Process. 2012;22(1):119-133. https://doi.org/10.1109/TIP.2012.2210725 [doi]
8. Mäkinen Y, Azzari L, Foi A. Collaborative filtering of correlated noise: Exact transform-domain variance for improved shrinkage and patch matching. IEEE Trans Image Process. 2020;29:8339-8354. https://doi.org/10.1109/TIP.2020.3014721 [doi]
9. Dabov K, Foi A, Katkovnik V, Egiazarian K. Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans Image Process. 2007;16(8):2080-2095. https://doi.org/10.1109/TIP.2007.901238 [doi]
10. Sajewski AN, Santini T, DeFranco A, et al. RF shimming strategy for an open 60-channel RF transmit 7T MRI head coil for routine use on the single transmit mode. Magn Reson Med. Published online 2025. https://doi.org/10.1002/mrm.30563 [doi]
11. Peirce JW. PsychoPy—psychophysics software in Python. J Neurosci Methods. 2007;162(1-2):8-13. https://doi.org/10.1016/j.jneumeth.2006.11.017 [doi]
12. Penny WD, Friston KJ, Ashburner JT, Kiebel SJ, Nichols TE. Statistical Parametric Mapping: The Analysis of Functional Brain Images. Elsevier; 2011.
13. Tian X, Chen Y, Majka P, et al. An integrated resource for functional and structural connectivity of the marmoset brain. Nat Commun. 2022;13(1):7416. https://doi.org/10.1038/s41467-022-35197-2 [doi]
14. Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM. Fsl. Neuroimage. 2012;62(2):782-790. https://doi.org/10.1016/j.neuroimage.2011.09.015 [doi]
15. Andersson JL, Sotiropoulos SN. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage. 2016;125:1063-1078. https://doi.org/10.1016/j.neuroimage.2015.10.019 [doi]
16. Cox RW. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res. 1996;29(3):162-173. https://doi.org/10.1006/cbmr.1996.0014 [doi]
17. Liu C, Frank QY, Yen CCC, et al. A digital 3D atlas of the marmoset brain based on multi-modal MRI. Neuroimage. 2018;169:106-116. https://doi.org/10.1016/j.neuroimage.2017.12.004 [doi]
18. Tustison NJ, Cook PA, Holbrook AJ, et al. The ANTsX ecosystem for quantitative biological and medical imaging. Sci Rep. 2021;11(1):9068. https://doi.org/10.1038/s41598-021-87564-6 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/452-02-017