Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
360-04-016 ISMRM Abstract

Uncertainty Quantification in Dictionary Matching: an Efficient Framework for Statistically Interpretable Quantitative MRI

Primary: Acquisition & Reconstruction - Quantitative Imaging: relaxometry, multi-parametric, MR Fingerprinting, and synthetic MRI
Secondary: Acquisition & Reconstruction - Signal Modeling
360-04-016 · Quantitative Mapping: Acquisition and Reconstruction · Monday, 11 May, 4:10 PM–5:05 PM · Digital Posters Row A
Keywords: Quantitative Imaging Relaxometry Uncertainty quantification Quantitative MRI Noise modeling
Accepted
Brian P Toner 1, Ute Goerke2, Eze Ahanonu3, Kevin Johnson4, Vibhas Deshpande5, Holden H Wu6,7, Maria Altbach4,8, Ali Bilgin1,3,4,8
1Applied Mathematics, University of Arizona, Tucson, United States of America
2Siemens Medical Solutions, Tucson, United States of America
3Electrical and Computer Engineering, University of Arizona, Tucson, United States of America
4Radiology and Imaging Science, University of Arizona, Tucson, United States of America
5Siemens Medical Solutions, Austin, United States of America
6Radiological Sciences, University of California Los Angeles, Los Angeles, United States of America
7Bioengineering, University of California Los Angeles, Los Angeles, United States of America
8Biomedical Engineering, University of Arizona, Tucson, United States of America
Presenting Author: Brian P Toner

Synopsis

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References

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2. Kim KA, Park MS, Kim IS, et al. Quantitative evaluation of liver cirrhosis using T1 relaxation time with 3 Tesla MRI before and after oxygen inhalation. Journal of Magnetic Resonance Imaging. 2012;36(2):405-410.
3. Pavlides M, Banerjee R, Sellwood J, et al. Multiparametric magnetic resonance imaging predicts clinical outcomes in patients with chronic liver disease. Journal of Hepatology. 2016;64(2):308-315.
4. Luetkens JA, Klein S, Traeber F, et al. Quantitative liver MRI including extracellular volume fraction for non-invasive quantification of liver fibrosis: a prospective proof-of-concept study. Gut. 2018;67(3):593-594.
5. Keerthivasan M, Toner B, Galons JP, et al. Diagnosing abdominal neoplasms using a T2 mapping radial turbo spin-echo technique with partial volume correction. European Radiology. 2025.
6. Ma D, Gulani V, Seiberlich N, et al. Magnetic resonance fingerprinting. Nature. 2013;495(7440):187-192.
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9. Metzner S, Wübbeler G, Flassbeck S, et al. Bayesian uncertainty quantification for magnetic resonance fingerprinting. Physics in Medicine & Biology. 2021;66(7):075006.
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12. Altbach M, Bilgin A, Li Z, et al. Processing of radial fast spin-echo data for obtaining T2 estimates from a single k-space data set. Magnetic Resonance in Medicine. 2005;54:549-559.
13. Huang C, Graff CG, Clarkson EW, et al. T2 mapping from highly undersampled data by reconstruction of principal component coefficient maps using compressed sensing. Magnetic Resonance in Medicine. 2012;67:1355-1366.
14. Keerthivasan M, Galons JP, Johnson K, et al. Abdominal T2-weighted imaging and T2 mapping using a variable flip angle radial turbo spin-echo technique. Journal of Magnetic Resonance Imaging. 2022;55:289-300.
15. Toner B, Arberet S, Zhang S, et al. Accelerated free-breathing abdominal T2 mapping with deep learning reconstruction of radial turbo spin-echo data. Magnetic Resonance in Medicine. 2025;94(6):2475-2491.
16. Lebel M, Wilman A. Transverse relaxometry with stimulated echo compensation. Magnetic Resonance in Medicine. 2010;64(4):1005-1014.
17. Chung S, Kim D, Breton E, et al. Rapid B1+ mapping using a preconditioning RF pulse with turboFLASH readout. Magnetic Resonance in Medicine. 2010;64(2):439-446.
18. Fessler JA and Sutton BP. Nonuniform fast Fourier transforms using min-max interpolation. IEEE Transactions on Signal Processing. 2003;51(2):560-574.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/360-04-016