Deep Learning–Generated Synthetic MR Imaging STIR Spine Images Are Superior in Image Quality and Diagnostically Equivalent to Conventional STIR: A Multicenter, Multireader Trial

References

1. 1.↵
   1. Bash S,
   2. Johnson B,
   3. Gibbs W, et al

   . Deep learning image processing enables 40% faster spinal MR scans which match or exceed quality of standard of care. Clin Neuroradiol 2022;32:197–203 doi:10.1007/s00062-021-01121-2 pmid:34846555

   CrossRefPubMed
2. 2.↵
   1. Bash S,
   2. Wang L,
   3. Airriess C, et al

   . Deep learning enables 60% accelerated volumetric brain MRI while preserving quantitative performance: a prospective, multicenter, multireader trial. AJNR Am J Neuroradiol 2021;42:2130–37 doi:10.3174/ajnr.A7358 pmid:34824098

   Abstract/FREE Full Text
3. 3.↵
   1. Zaharchuk G,
   2. Gong E,
   3. Wintermark M, et al

   . Deep learning in neuroradiology. AJNR Am J Neuroradiol 2018;39:1776–84 doi:10.3174/ajnr.A5543 pmid:29419402

   Abstract/FREE Full Text
4. 4.↵
   1. Liu J,
   2. Pasumarthi S,
   3. Duffy B, et al

   . One model to synthesize them all: multi-contrast multi-scale transformer for missing data imputation. IEEE Trans Med Imaging 2023 Mar 4 [Epub ahead of print] doi:10.1109/TMI.2023.3261707 pmid:37030684

   CrossRefPubMed
5. 5.↵
   1. Shen E,
   2. Liu T,
   3. Peters, TM

   1. Li H,
   2. Paetzold JC,
   3. Sekuboyina A, et al

   . DiamondGAN: unified multi-modal generative adversarial networks for MRI sequences synthesis. In: Shen E, Liu T, Peters, TM, et al. Medical Image Computing and Computer Assisted Intervention–MICCAI. Springer International Publishing; 2019:795–803
6. 6.↵
   1. Denck J,
   2. Guehring J,
   3. Maier A, et al

   . Enhanced magnetic resonance image synthesis with contrast-aware generative adversarial networks. J Imaging 2021;7:133 doi:10.3390/jimaging7080133 pmid:34460769

   CrossRefPubMed
7. 7.↵
   1. Yurt M,
   2. Dar SU,
   3. Erdem A, et al

   . mustGAN: multi-stream Generative Adversarial Networks for MR image synthesis. Med Image Anal 2021;70:101944 doi:10.1016/j.media.2020.101944 pmid:33690024

   CrossRefPubMed
8. 8.↵
   1. Dar SU,
   2. Yurt M,
   3. Karacan L, et al

   . Image synthesis in multi-contrast MRI with conditional generative adversarial networks. IEEE Trans Med Imaging 2019;38:2375–88 doi:10.1109/TMI.2019.2901750 pmid:30835216

   CrossRefPubMed
9. 9.↵
   1. Lee D,
   2. Kim J,
   3. Moon WJ, et al

   . CollaGAN: Collaborative GAN for Missing Image Data Imputation. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Long Beach, California. June 15–20, 2019:2487–96 doi:10.1109/CVPR.2019.00259

   CrossRef
10. 10.↵
    1. Haubold J,
    2. Demircioglu A,
    3. Theysohn JM, et al

    . Generating virtual short tau inversion recovery (STIR) images from T1- and T2-weighted images using a conditional generative adversarial network in spine imaging. Diagnostics (Basel) 2021;11:1542 doi:10.3390/diagnostics11091542 pmid:34573884

    CrossRefPubMed
11. 11.↵
    1. Obuchowski NA,
    2. Subhas N,
    3. Schoenhagen P

    . Testing for interchangeability of imaging tests. Acad Radiol 2014;21:1483–89 doi:10.1016/j.acra.2014.07.004 pmid:25300725

    CrossRefPubMed
12. 12.↵
    1. Kingma DP,
    2. Ba J

    . Adam: A Method for Stochastic Optimization. arXiv. December 22, 2014. http://arxiv.org/abs/1412.6980. Accessed January 15, 2023
13. 13.↵
    1. Khatri G,
    2. Pedrosa I,
    3. Ananthakrishnan L, et al

    . Abbreviated-protocol screening MRI vs. complete-protocol diagnostic MRI for detection of hepatocellular carcinoma in patients with cirrhosis: an equivalence study using LI-RADS v2018. J Magn Reson Imaging 2020;51:415–25 doi:10.1002/jmri.26835 pmid:31209978

    CrossRefPubMed
14. 14.↵
    1. Giavarina D

    . Understanding Bland Altman analysis. Biochem Med (Zagreb) 2015;25:141–51 doi:10.11613/BM.2015.015 pmid:26110027

    CrossRefPubMed
15. 15.↵
    1. Altman DG,
    2. Bland JM

    . Measurement in medicine: the analysis of method comparison studies. The Statistician 1983;32:307–17

    CrossRef
16. 16.↵
    1. Henderson AR

    . Testing experimental data for univariate normality. Clin Chim Acta 2006;366:112–29 doi:10.1016/j.cca.2005.11.007 pmid:16388793

    CrossRefPubMed
17. 17.↵
    1. Sotgia S,
    2. Zinellu A,
    3. Pinna GA, et al

    . A new general regression-based approach for method comparison studies. Clin Chem Lab Med 2008;46:1046–49 doi:10.1515/CCLM.2008.194 pmid:18605944

    CrossRefPubMed
18. 18.↵
    1. Tanenbaum L

    . Quality, efficiency, and survival with patient centric imaging. In: Proceedings of 7th Annual Snowmass 2023: Hot Topics in Radiology: Advanced Imaging and Artificial Intelligence. Snowmass Village, Colorado. February 5–10, 2019
19. 19.↵
    1. Meléndez JC,
    2. McCrank E

    . Anxiety-related reactions associated with magnetic resonance imaging examinations. JAMA 1993;270:745–47 doi:10.1001/jama.1993.03510060091039 pmid:8336378

    CrossRefPubMedWeb of Science
20. 20.↵
    1. Zaitsev M,
    2. Maclaren J,
    3. Herbst M

    . Motion artifacts in MRI: a complex problem with many partial solutions. J Magn Reson Imaging 2015;42:887–901 doi:10.1002/jmri.24850 pmid:25630632

    CrossRefPubMed
21. 21.↵
    1. Andre JB,
    2. Bresnahan BW,
    3. Mossa-Basha M, et al

    . Toward quantifying the prevalence, severity, and cost associated with patient motion during clinical MR examinations. J Am Coll Radiol 2015;12:689–95 doi:10.1016/j.jacr.2015.03.007 pmid:25963225

    CrossRefPubMed