Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
552-02-012 / 552-02-012 ISMRM Abstract

Accelerated and Noise-Robust Carotid T1–T2 Mapping Using Deep Learning Reconstruction

Primary: Acquisition & Reconstruction - Image Reconstruction: AI
Secondary: Acquisition & Reconstruction - Quantitative Imaging: head and neck
552-02-012 · Quantitative Imaging: Relaxometry, Multiparametric, MR Fingerprinting, and Synthetic MRI · Wednesday, 13 May, 1:40 PM–3:16 PM · Power Pitch Theatre 2
552-02-012 · Quantitative Imaging: Relaxometry, Multiparametric, MR Fingerprinting, and Synthetic MRI · Wednesday, 13 May, 1:40 PM–3:16 PM · Power Pitch Theatre 2
Keywords: Deep learning reconstruction Carotid T1 T2 Mapping
Accepted
Deniz Karakay 1, Brian P Toner2, Arunbalaji Pugazhendhi3, Kevin Johnson4, Kirsten Concha-Moore5, Marina Ferguson6, Eze Ahanonu1, Ute Goerke7, Dimitris Mitsouras8, Jonas Schollenberger9, David Saloner9, Seong-Eun Kim10, John Roberts10, J. Rock Hadley10, Dennis Parker10, Scott McNally10, Gerald Treiman10, Yibin Xie11, Debiao Li11, Kim-Lien Nguyen12, Vibhas Deshpande13, Herman D Morris14, J. Kevin DeMarco15, Craig Weinkauf5, Ali Bilgin1,2,4,16, Maria Altbach4,16,17
1Electrical and Computer Engineering, University of Arizona, Tucson, United States of America
2Applied Mathematics, University of Arizona, Tucson, United States of America
3Department of Radiology and Imaging Sciences, University of Arizona, Tucson, United States of America
4Radiology and Imaging Science, University of Arizona, Tucson, United States of America
5Department of Surgery, University of Arizona, Tucson, United States of America
6Department of Radiology, University of Washington, Seattle, United States of America
7Siemens Medical Solutions, Tucson, United States of America
8Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, United States of America
9University of California San Francisco — Radiology and biomedical imaging, San Francisco, United States of America
10University of Utah School of Medicine, Salt Lake City, United States of America
11Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, United States of America
12Department of Radiological Sciences, University of California Los Angeles, Los Angeles, United States of America
13Siemens Medical Solutions, Austin, United States of America
14Radiology, Uniformed Services University of the Health Sciences, Bethesda, United States of America
15Department of Radiology and Radiological Sciences, Uniformed Services University of the Health Sciences, Bethesda, United States of America
16Biomedical Engineering, University of Arizona, Tucson, United States of America
17Medical Imaging, University of Arizona, Tucson, United States of America
Presenting Author: Deniz Karakay

Synopsis

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References

1. Qiao H, Li D, Cao J, Qi H, Han Y, Han H, Xu H, Wang T, Chen S, Chen H, Wang Y, Zhao X. Quantitative evaluation of carotid atherosclerotic vulnerable plaques using in vivo T1 mapping cardiovascular magnetic resonaonce: validation by histology. J Cardiovasc Magn Reson. 2020 May 21;22(1):38. doi: 10.1186/s12968-020-00624-0. PMID: 32434582; PMCID: PMC7240932. [doi] [pmid]
2. Alkhalil M, Biasiolli L, Akbar N, Galassi F, Chai JT, Robson MD, Choudhury RP. T2 mapping MRI technique quantifies carotid plaque lipid, and its depletion after statin initiation, following acute myocardial infarction. Atherosclerosis. 2018 Dec;279:100-106. doi: 10.1016/j.atherosclerosis.2018.08.033. Epub 2018 Sep 6. PMID: 30227984. [doi] [pmid]
3. Wang Y, Liu X, Wang J, Wang Y, Qi H, Kong X, Liu D, Liu J, Zheng H, Xiong F, Zhang L, Fu X, Zhang X, Guo R, Qiao H, Chen Z, Si D, Chen H. Simultaneous T1, T2, and T2* Mapping of Carotid Plaque: The SIMPLE* Technique. Radiology. 2023 May;307(3):e222061. doi: 10.1148/radiol.222061. Epub 2023 Feb 28. PMID: 36853181. [doi] [pmid]
4. Kim SE et al. J Magn Reson Imaging. 2011;34:1167-1175; 2Yao B, Eur Radiol. 2016, 26:3737-3743
5. Raman SV, et al. JACC Cardiovasc Imaging. 2008; 1: 49-57;
6. Saam T, Ferguson MS, Yarnykh VL, Takaya N, Xu D, Polissar NL, Hatsukami TS, Yuan C. Quantitative evaluation of carotid plaque composition by in vivo MRI. Arterioscler Thromb Vasc Biol. 2005 Jan;25(1):234-9. doi: 10.1161/01.ATV.0000149867.61851.31. Epub 2004 Nov 4. PMID: 15528475. [doi] [pmid]
7. Kassem M, Florea A, Mottaghy FM, van Oostenbrugge R, Kooi ME. Magnetic resonance imaging of carotid plaques: current status and clinical perspectives. Ann Transl Med. 2020 Oct;8(19):1266. doi: 10.21037/atm-2020-cass-16. PMID: 33178798; PMCID: PMC7607136. [doi] [pmid]
8. Toner B, Arberet S, Zhang S, Han F, Ahanonu E, Goerke U, Johnson K, Abouelfetouh Z, Codreanu I, Sridhar S, Arif-Tiwari H, Deshpande V, Martin DR, Nadar M, Altbach MI, Bilgin A. Accelerated free-breathing abdominal T2 mapping with deep learning reconstruction of radial turbo spin-echo data. Magn Reson Med. 2025 Dec;94(6):2475-2491. doi: 10.1002/mrm.70017. Epub 2025 Aug 5. PMID: 40762149; PMCID: PMC12396147. [doi] [pmid]
9. Ahanonu E, Goerke U, Johnson K, Toner B, Martin DR, Deshpande V, Bilgin A, Altbach M. Accelerated 2D radial Look-Locker T1 mapping using a deep learning-based rapid inversion recovery sampling technique. NMR Biomed. 2024 Dec;37(12):e5266. doi: 10.1002/nbm.5266. Epub 2024 Oct 2. PMID: 39358992; PMCID: PMC11892465. [doi] [pmid]
10. Hadley, R. et al. “Relative RF coil performance in carotid imaging.” Magnetic resonance imaging vol. 23,5 (2005): 629-39. doi:10.1016/j.mri.2005.04.009 [doi]
11. Schlemper, J., Caballero, J., Hajnal, J. V., Price, A., & Rueckert, D. (2017, May). A deep cascade of convolutional neural networks for MR image reconstruction. In International conference on information processing in medical imaging (pp. 647-658). Cham: Springer International Publishing. doi:10.48550/arXiv.1703.00555, [doi]
12. Schlemper, J. et al. (2019). Nonuniform Variational Network: Deep Learning for Accelerated Nonuniform MR Image Reconstruction. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11766. Springer, Cham. https://doi.org/10.1007/978-3-030-32248-9_7 [doi]
13. Hammernik, K., Klatzer, T., Kobler, E., Recht, M.P., Sodickson, D.K., Pock, T. and Knoll, F. (2018), Learning a variational network for reconstruction of accelerated MRI data. Magn. Reson. Med., 79: 3055-3071. https://doi.org/10.1002/mrm.26977 [doi]
14. K. Hammernik et al., "Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging: Combining physics and machine learning for improved medical imaging," in IEEE Signal Processing Magazine, vol. 40, no. 1, pp. 98-114, Jan. 2023, doi: 10.1109/MSP.2022.3215288. keywords: {Deep learning;Inverse problems;Magnetic resonance imaging;Computational modeling;Pipelines;Signal processing;Task analysis}, [doi]
15. Heckel, R., Jacob, M., Chaudhari, A. et al. Deep learning for accelerated and robust MRI reconstruction. Magn Reson Mater Phy 37, 335–368 (2024). https://doi.org/10.1007/s10334-024-01173-8 [doi]
16. Toner B, Arberet S, Zhang S, et al. Accelerated free-breathing abdominal T2 mapping with deep learning reconstruction of radial turbo spin-echo data. Magn Reson Med. 2025; 94(6): 2475-2491. doi: 10.1002/mrm.70017 [doi]
17. Huang, C., Graff, C.G., Clarkson, E.W., Bilgin, A. and Altbach, M.I. (2012), T2 mapping from highly undersampled data by reconstruction of principal component coefficient maps using compressed sensing†. Magn. Reson. Med., 67: 1355-1366. https://doi.org/10.1002/mrm.23128 [doi]
18. Yaman B, Hosseini SAH, Moeller S, Ellermann J, Uğurbil K, Akçakaya M. Self-supervised learning of physics-guided reconstruction neural networks without fully sampled reference data. Magn Reson Med. 2020; 84: 3172–3191. https://doi.org/10.1002/mrm.28378 [doi]
19. Mandava S, Keerthivasan MB, Martin DR, Altbach MI, Bilgin A. Improving subspace constrained radial fast spin echo MRI using block matching driven non-local low rank regularization. Phys Med Biol. 2021 Feb 11;66(4):04NT03. doi: 10.1088/1361-6560/abd4b8. PMID: 33333497; PMCID: PMC8321599. [doi] [pmid]
20. Altbach, M.I., Bilgin, A., Li, Z., Clarkson, E.W., Trouard, T.P. and Gmitro, A.F. (2005), Processing of radial fast spin-echo data for obtaining T2 estimates from a single k-space data set†. Magn. Reson. Med., 54: 549-559. https://doi.org/10.1002/mrm.20611 [doi]
21. J. A. Fessler and B. P. Sutton, "Nonuniform fast Fourier transforms using min-max interpolation," in IEEE Transactions on Signal Processing, vol. 51, no. 2, pp. 560-574, Feb. 2003, doi: 10.1109/TSP.2002.807005. [doi]
22. Lebel, R.M. and Wilman, A.H. (2010), Transverse relaxometry with stimulated echo compensation. Magn. Reson. Med., 64: 1005-1014. https://doi.org/10.1002/mrm.22487 [doi]
23. Marty B, Vignaud A, Greiser A, Robert B, de Sousa PL, Carlier PG. BLOCH equations-based reconstruction of myocardium t1 maps from modified look-locker inversion recovery sequence. PLoS One. 2015 May 11;10(5):e0126766. doi: 10.1371/journal.pone.0126766. PMID: 25962182; PMCID: PMC4427396. [doi] [pmid]

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