Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
431-03-005 / 431-03-005 ISMRM Abstract

Fast and Efficient Calculation of Noise and g-factor for Iterative Parallel Imaging Reconstructions

Primary: Acquisition & Reconstruction - Image Reconstruction: Non-AI
Secondary: Acquisition & Reconstruction - Open source software, sequences, and reconstruction algorithms
431-03-005 · Image Reconstruction · Tuesday, 12 May, 4:00 PM–5:36 PM · Roof Terrace
431-03-005 · Image Reconstruction · Tuesday, 12 May, 4:00 PM–5:36 PM · Roof Terrace
Keywords: Parallel Imaging Image Reconstruction Iterative reconstruction G-factor Fast SNR mapping
Accepted
Onat Dalmaz 1,2, Daniel R Abraham1,2, Alexander R Toews1,2, Akshay Chaudhari2,3, Kawin Setsompop1,2, Brian A Hargreaves1,2,4
1Electrical Engineering, Stanford University, Stanford, United States of America
2Department of Radiology, Stanford University, Stanford, United States of America
3Biomedical Data Science, Stanford University, Stanford, United States of America
4Bioengineering, Stanford University, Stanford, United States of America
Presenting Author: Onat Dalmaz

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. Sodickson DK, Griswold MA, Jakob PM, Edelman RR, Manning WJ. Signal-to-noise ratio and signal-to-noise efficiency in SMASH imaging. Magn Reson Med. 1999;41:1009–1022. https://doi.org/10.1002/mrm.1910410520 [doi]
2. Pruessmann KP, Weiger M, Scheidegger MB, Boesiger P. SENSE: Sensitivity encoding for fast MRI. Magn Reson Med. 1999;42:952–962. https://doi.org/10.1002/mrm.1910420523 [doi]
3. Griswold MA, Jakob PM, Heidemann RM, et al. Generalized autocalibrating partially parallel acquisitions (GRAPPA). Magn Reson Med. 2002;47:1202–1210. https://doi.org/10.1002/mrm.10171 [doi]
4. Breuer FA, Kannengiesser SAR, Blaimer M, Seiberlich N, Jakob PM, Griswold MA. General formulation for quantitative g-factor calculation in GRAPPA reconstructions. Magn Reson Med. 2009;62:739–746. https://doi.org/10.1002/mrm.22066 [doi]
5. Thunberg P, Zetterberg P. Noise distribution in SENSE- and GRAPPA-reconstructed images: a computer simulation study. Magn Reson Imaging. 2007;25:1089–1094. https://doi.org/10.1016/j.mri.2007.04.003 [doi]
6. Aja-Fernández S, Vegas-Sánchez-Ferrero G, Tristán-Vega A. Noise estimation in parallel MRI: GRAPPA and SENSE. Magn Reson Imaging. 2014;32:281–290. https://doi.org/10.1016/j.mri.2013.11.004 [doi]
7. Kellman P, McVeigh ER. Image reconstruction in SNR units: A general method for SNR measurement. Magn Reson Med. 2005;54:1439–1447. https://doi.org/10.1002/mrm.20689 [doi]
8. Dietrich O, Raya JG, Reeder SB, Ingrisch M, Reiser MF, Schoenberg SO. Influence of multichannel combination, parallel imaging and other reconstruction techniques on MRI noise characteristics. Magn Reson Imaging. 2008;26:754–762. https://doi.org/10.1016/j.mri.2008.01.002 [doi]
9. Kurihara Y, Yakushiji YK, Tani I, Nakajima Y, Van Cauteren M. Coil sensitivity encoding in MR imaging. AJR Am J Roentgenol. 2002;178:1087–1091. https://doi.org/10.2214/ajr.178.5.1781087 [doi]
10. Pruessmann KP, Weiger M, Börnert P, Boesiger P. Advances in sensitivity encoding with arbitrary k-space trajectories. Magn Reson Med. 2001;46:638–651. https://doi.org/10.1002/mrm.1241 [doi]
11. Bammer R, Aksoy M, Liu C. Augmented generalized SENSE reconstruction to correct for rigid-body motion. Magn Reson Med. 2007;57:90–102. https://doi.org/10.1002/mrm.21106 [doi]
12. Maier O, Baete SH, Fyrdahl A, Hammernik K, Harrevelt S, Kasper L, Karakuzu A, Loecher M, Patzig F, Tian Y, Wang K, Gallichan D, Uecker M, Knoll F. CG-SENSE revisited: Results from the first ISMRM reproducibility challenge. Magn Reson Med. 2021 Apr;85(4):1821-1839. doi: 10.1002/mrm.28569. Epub 2020 Nov 12. PMID: 33179826; PMCID: PMC8201869. [doi] [pmid]
13. Robson PM, Grant AK, Madhuranthakam AJ, Lattanzi R, Sodickson DK, McKenzie CA. Comprehensive quantification of SNR and g-factor for image-based and k-space-based parallel imaging reconstructions. Magn Reson Med. 2008;60:895–907. https://doi.org/10.1002/mrm.21728 [doi]
14. Akcakaya M, Basha TA, Manning WJ, Nezafat R. Efficient calculation of g-factors for CG-SENSE in high dimensions: noise amplification in random undersampling. J Cardiovasc Magn Reson. 2014;16:10. https://doi.org/10.1186/1532-429X-16-10 [doi]
15. Jaspan ON, Fleysher R, Lipton ML. Compressed sensing MRI: a review of the clinical literature. Br J Radiol. 2015;88:20150487. https://doi.org/10.1259/bjr.20150487 [doi]
16. Vasanawala SS, Alley MT, Hargreaves BA, Barth RA, Pauly JM, Lustig M. Improved pediatric MR imaging with compressed sensing. Radiology. 2010;256:607–616. https://doi.org/10.1148/radiol.10091747 [doi]
17. Dalmaz O, Desai AD, Chaudhari AS, Hargreaves BA. Noise-induced variability quantification in deep learning-based MRI reconstructions. Proc ISMRM. 2024.
18. Dawood P, Breuer F, Gram M, Homolya I, Jakob PM, Zaiss M, Blaimer M. Image space formalism of convolutional neural networks for k-space interpolation. Magn Reson Med. 2025; 94(6): 2680–2701. https://doi.org/10.1002/mrm.70002 [doi]
19. Dalmaz O, Desai AD, Heckel R, Cukur T, Chaudhari AS, Hargreaves BA. Efficient Noise Calculation in Deep Learning-based MRI Reconstructions. Proc 42nd ICML, PMLR 267, 2025: 12280–12313. https://proceedings.mlr.press/v267/dalmaz25a.html
20. Setsompop K, Gagoski BA, Polimeni JR, Witzel T, Wedeen VJ, Wald LL. Blipped-controlled aliasing in parallel imaging for simultaneous multislice echo planar imaging with reduced g-factor penalty. Magn Reson Med. 2012;67(5):1210–1224. https://doi.org/10.1002/mrm.23097 [doi]
21. Wang X, Ludwig D, Rawson M, Balan RV, Ernst T. Estimating noise propagation of neural network–based image reconstruction using automated differentiation. Proc. Intl. Soc. Magn. Reson. Med. 30 (2022): 0500. https://doi.org/10.58530/2022/0500 [doi]
22. Rodgers CT, Robson MD. Receive array magnetic resonance spectroscopy: Whitened singular value decomposition (WSVD) gives optimal Bayesian solution. Magnetic Resonance in Medicine. 2010;63(4):881–891. https://doi.org/10.1002/mrm.22230 [doi]
23. Bilgic, B., Gagoski, B. A., Cauley, S. F., Fan, A. P., Polimeni, J. R., Wald, L. L., & Setsompop, K. Wave-CAIPI for highly accelerated 3D imaging. Magnetic Resonance in Medicine, 72(6), 1934–1945 (2014). https://doi.org/10.1002/mrm.25011 [doi]
24. Hutchinson MF. A stochastic estimator of the trace of the influence matrix for Laplacian smoothing splines. Communications in Statistics – Simulation and Computation. 1990;19(2):433–450. https://doi.org/10.1080/03610919008812866 [doi]
25. Bekas C, Kokiopoulou E, Saad Y. An estimator for the diagonal of a matrix. Applied Numerical Mathematics. 2007;57(11):1214–1229. https://doi.org/10.1016/j.apnum.2007.01.003 [doi]
26. Ong F, Amin S, Vasanawala S, Lustig M. Mridata.org: An open archive for sharing MRI raw data. In: Proc 26th Annu Meeting ISMRM, Paris, France; 2018.
27. Knoll F, Zbontar J, Sriram A, Muckley MJ, Bruno M, Defazio A, et al. FastMRI: A publicly available raw k-space and DICOM dataset of knee images for accelerated MR image reconstruction using machine learning. Radiol Artif Intell. 2020;2(1):e190007. https://doi.org/10.1148/ryai.2020190007 [doi]
28. Knoll F, Bredies K, Pock T, Stollberger R. Second order total generalized variation (TGV) for MRI. Magn Reson Med. 2011;65(2):480–491. https://doi.org/10.1002/mrm.22595 [doi]
29. Uecker M, Lai P, Murphy MJ, Virtue P, Elad M, Pauly JM, Vasanawala SS, Lustig M. ESPIRiT--an eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA. Magn Reson Med. 2014 Mar;71(3):990-1001. doi: 10.1002/mrm.24751. PMID: 23649942; PMCID: PMC4142121. [doi] [pmid]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/431-03-005