Synopsis
Motivation:
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351-02-005 / 351-02-005
ISMRM Abstract
Is 2-minute Simultaneous CT-like Bone-weighted Imaging and UTE-T2* Quantification Clinically Possible?
Accepted
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1. Hennig J, Nauerth A, Friedburg HR. RARE imaging: a fast-imaging method for clinical MR. Magnetic resonance in medicine. 1986 Dec;3(6):823-33; PMID: 3821461 DOI: 10.1002/mrm.1910030602 [doi] [pmid]
2. Florkow MC, Willemsen K, Mascarenhas VV, Oei EH, van Stralen M, Seevinck PR. Magnetic resonance imaging versus computed tomography for three-dimensional bone imaging of musculoskeletal pathologies: a review. Journal of Magnetic Resonance Imaging. 2022 Jul;56(1):11-34; PMID: 35044717 PMCID: PMC9305220 DOI: 10.1002/jmri.28067 [doi] [pmid]
3. Chang, Eric Y., Jiang Du, and Christine B. Chung. "UTE imaging in the musculoskeletal system." Journal of magnetic resonance imaging 41.4 (2015): 870-883; PMID: 25045018 PMCID: PMC4297256 DOI: 10.1002/jmri.24713 [doi] [pmid]
4. C. S. Rajapakse, M. Bashoor-Zadeh, C. Li, W. Sun, A. C. Wright, and F. W. Wehrli, “Volumetric Cortical Bone Porosity Assessment with MR Imaging: Validation and Clinical Feasibility,” Radiology, vol. 276, no. 2, pp. 526–535, Aug. 2015; PMID: 26203710 doi: 10.1148/radiol.15141850 [doi] [pmid]
5. F. Wiesinger et al., “Zero TE MR bone imaging in the head,” Magn. Reson. Med., vol. 75, no. 1, pp. 107–114, 2016, doi: 10.1002/mrm.25545; PMID: 25639956 DOI: 10.1002/mrm.25545 [doi] [pmid]
6. Aydıngöz Ü, Yıldız AE, Ergen FB. Zero echo time musculoskeletal MRI: technique, optimization, applications, and pitfalls. Radiographics. 2022 Sep;42(5):1398-414; PMID: 35904982 DOI: 10.1148/rg.220029 [doi] [pmid]
7. Carretero-Gómez L, Fung M, Wiesinger F, Carl M, McKinnon G, de Arcos J, Mandava S, Arauz S, Sánchez-Lacalle E, Nagrani S, López-Alcorocho JM. Deep learning-enhanced zero echo time MRI for glenohumeral assessment in shoulder instability: a comparative study with CT. Skeletal Radiology. 2025 Jun;54(6):1263-73; PMID: 39572485 PMCID: PMC12000158 DOI: 10.1007/s00256-024-04830-0 [doi] [pmid]
8. Einarsson E, Svensson J, Folkesson E, Kestilä I, Tjörnstrand J, Peterson P, Finnilä MA, Hughes HV, Turkiewicz A, Saarakkala S, Englund M. Relating MR relaxation times of ex vivo meniscus to tissue degeneration through comparison with histopathology. Osteoarthritis and cartilage open. 2020 Jun 1;2(2):100061; PMID: 33972933 PMCID: PMC7610736 DOI: 10.1016/j.ocarto.2020.100061 [doi] [pmid]
9. K. P. Pruessmann, M. Weiger, P. Börnert, and P. Boesiger, “Advances in sensitivity encoding with arbitrary k-space trajectories,” Magn. Reson. Med., vol. 46, no. 4, pp. 638–651, 2001, doi: 10.1002/mrm.1241; PMID: 11590639 DOI: 10.1002/mrm.1241 [doi] [pmid]
10. R. M. Lebel, “Performance characterization of a novel deep learning-based MR image reconstruction pipeline,” ArXiv200806559 Cs Eess, Aug. 2020, Accessed: Sep. 28, 2021. [Online]. Available: http://arxiv.org/abs/2008.06559; https://doi.org/10.48550/arXiv.2008.06559 [doi]
11. Kidoh M, Shinoda K, Kitajima M, Isogawa K, Nambu M, Uetani H, Morita K, Nakaura T, Tateishi M, Yamashita Y, Yamashita Y. Deep Learning Based Noise Reduction for Brain MR Imaging: Tests on Phantoms and Healthy Volunteers. Magn Reson Med Sci. 2020 Aug; PMID: 31484849 PMCID: PMC7553817 DOI: 10.2463/mrms.mp.2019-0018 [doi] [pmid]
Cite this abstract
http://echo.ismrm.org/p/ISMRM2026/351-02-005