Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
364-01-013 ISMRM Abstract

A deep-learning model to enhance the quality and fidelity of resting-state cerebrovascular reactivity quantification

Primary: Brain Function and fMRI - fMRI Analysis
Secondary: Brain Function and fMRI - Functional Connectivity
364-01-013 · fMRI: Analyses · Monday, 11 May, 8:20 AM–9:15 AM · Digital Posters Row E
Keywords: fMRI (resting state) Machine Learning/Artificial Intelligence Cerebrovascular reactivity
Accepted
Ali Golestani 1, J. Jean Chen2,3
1University of Calgary, Calgary, Canada
2Rotman Research Institute at Baycrest, Toronto, Canada
3University of Toronto, Toronto, Canada
Presenting Author: Ali Golestani

Synopsis

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References

1. Liu, P., De Vis, J. B. & Lu, H. Cerebrovascular reactivity (CVR) MRI with CO2 challenge: A technical review. Neuroimage 187, 104–115 (2019).
2. Zaca, D., Hua, J. & Pillai, J. J. Cerebrovascular reactivity mapping for brain tumor presurgical planning. World J Clin Oncol 2, 289–298 (2011).
3. Pillai, J. J. & Zacà, D. Comparison of BOLD cerebrovascular reactivity mapping and DSC MR perfusion imaging for prediction of neurovascular uncoupling potential in brain tumors. Technol Cancer Res Treat 11, 361–374 (2012).
4. Golestani, A. M., Wei, L. L. & Chen, J. J. Quantitative mapping of cerebrovascular reactivity using resting-state BOLD fMRI: Validation in healthy adults. Neuroimage 138, 147–163 (2016).
5. Golestani, A. M., Chang, C., Kwinta, J. B., Khatamian, Y. B. & Jean Chen, J. Mapping the end-tidal CO2 response function in the resting-state BOLD fMRI signal: spatial specificity, test-retest reliability and effect of fMRI sampling rate. Neuroimage 104, 266–277 (2015).
6. Islam, K. T. et al. Improving portable low-field MRI image quality through image-to-image translation using paired low- and high-field images. Sci Rep 13, 21183 (2023).

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/364-01-013