44
Views
0
CrossRef citations to date
0
Altmetric
ORIGINAL RESEARCH

Validation and Feasibility of Ultrafast Cervical Spine MRI Using a Deep Learning-Assisted 3D Iterative Image Enhancement System

, , &
Pages 2499-2509 | Received 20 Feb 2024, Accepted 13 May 2024, Published online: 23 May 2024

References

  • Ghaffari-Rafi A, Peterson C, Leon-Rojas JE, et al. The role of magnetic resonance imaging to inform clinical decision-making in acute spinal cord injury: a systematic review and meta-analysis. J Clin Med. 2021;10(21):4948. doi:10.3390/jcm10214948
  • Michelini G, Corridore A, Torlone S, et al. Dynamic MRI in the evaluation of the spine: state of the art. Acta Biomed. 2018;89(1–S):89.
  • Zaitsev M, Maclaren J, Herbst M. Motion artifacts in MRI: a complex problem with many partial solutions. J Magn Reson Imaging. 2015;42(4):887–901. doi:10.1002/jmri.24850
  • Gao T, Lu Z, Wang F, Zhao H, Wang J, Pan S. Using the compressed sensing technique for lumbar vertebrae imaging: comparison with conventional parallel imaging. Curr Med Imaging. 2021;17(8):1010–1017. doi:10.2174/1573405617666210126155814
  • Longo MG, Fagundes J, Huang S, et al. Simultaneous multislice-based 5-minute lumbar spine MRI protocol: initial experience in a clinical setting. J Neuroimaging. 2017;27(5):442–446. doi:10.1111/jon.12453
  • Nolte I, Gerigk L, Brockmann MA, Kemmling A, Groden C. MRI of degenerative lumbar spine disease: comparison of non-accelerated and parallel imaging. Neuroradiology. 2008;50(5):403–409. doi:10.1007/s00234-008-0363-0
  • Qiu J, Liu J, Bi Z, et al. An investigation of 2D spine Magnetic Resonance Imaging (MRI) with Compressed Sensing (CS). Skeletal Radiol. 2022;51(6):1273–1283. doi:10.1007/s00256-021-03954-x
  • Glockner JF, Hu HH, Stanley DW, Angelos L, King K. Parallel MR imaging: a user’s guide. Radiographics. 2005;25(5):1279–1297. doi:10.1148/rg.255045202
  • Stenger VA, Noll DC, Boada FE. Partial Fourier reconstruction for three-dimensional gradient echo functional MRI: comparison of phase correction methods. Magn Reson Med. 1998;40(3):481–490. doi:10.1002/mrm.1910400320
  • Jaspan ON, Fleysher R, Lipton ML. Compressed sensing MRI: a review of the clinical literature. Br J Radiol. 2015;88(1056):20150487. doi:10.1259/bjr.20150487
  • Johnson PM, Recht MP, Knoll F. Improving the Speed of MRI with Artificial Intelligence. Semin Musculoskelet Radiol. 2020;24(1):12–20. doi:10.1055/s-0039-3400265
  • Kashiwagi N, Tanaka H, Yamashita Y, et al. Applicability of deep learning-based reconstruction trained by brain and knee 3T MRI to lumbar 1.5T MRI. Acta Radiol Open. 2021;10(6):20584601211023939. doi:10.1177/20584601211023939
  • Xie Y, Tao H, Li X, et al. Prospective comparison of standard and deep learning-reconstructed turbo spin-echo MRI of the shoulder. Radiology. 2024;310(1):e231405. doi:10.1148/radiol.231405
  • Herrmann J, Gassenmaier S, Nickel D, et al. Diagnostic confidence and feasibility of a deep learning accelerated HASTE sequence of the abdomen in a single breath-hold. Invest Radiol. 2021;56(5):313–319. doi:10.1097/RLI.0000000000000743
  • Ogawa R, Kido T, Nakamura M, et al. Reconstruction of cardiovascular black-blood T2-weighted image by deep learning algorithm: a comparison with intensity filter. Acta Radiologica Open. 2021;10(9):205846012110447. doi:10.1177/20584601211044779
  • Zhao ST, Cahill DG, Li SY, et al. Denoising of three-dimensional fast spin echo magnetic resonance images of knee joints using spatial-variant noise-relevant residual learning of convolution neural network. Comput Biol Med. 2022;151:106295.
  • Kumar MG, Das Goswami A. Automatic classification of the severity of knee osteoarthritis using enhanced image sharpening and CNN. Appl Sci-Basel. 2023;13(3):1658.
  • Chaudhari AS, Fang ZN, Kogan F, et al. Super-resolution musculoskeletal MRI using deep learning. Magnet Reson Med. 2018;80(5):2139–2154. doi:10.1002/mrm.27178
  • Yoon MA, Gold GE, Chaudhari AS. Accelerated musculoskeletal magnetic resonance imaging. J Magn Reson Imaging. 2023. doi:10.1002/jmri.29205
  • Fujiwara M, Kashiwagi N, Matsuo C, et al. Ultrafast lumbar spine MRI protocol using deep learning-based reconstruction: diagnostic equivalence to a conventional protocol. Skeletal Radiol. 2023;52(2):233–241. doi:10.1007/s00256-022-04192-5
  • Kashiwagi N, Sakai M, Tsukabe A, et al. Ultrafast cervcial spine MRI protocol using deep learning-based reconstruction: diagnostic equivalence to a conventional protocol. Eur J Radiol. 2022;156:110531. doi:10.1016/j.ejrad.2022.110531
  • Coupé P, Yger P, Prima S, Hellier P, Kervrann C, Barillot C. An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images. IEEE Transac Med Imaging. 2008;27(4):425–441. doi:10.1109/TMI.2007.906087
  • Bustin A, Voilliot D, Menini A, et al. Isotropic reconstruction of MR images using 3D patch-based self-similarity learning. IEEE Transac Med Imaging. 2018;37(8):1932–1942. doi:10.1109/TMI.2018.2807451
  • Manjón JV, Coupé P, Buades A, Fonov V, Collins DL, Robles M. Non-local MRI upsampling. Med Image Anal. 2010;14(6):784–792. doi:10.1016/j.media.2010.05.010
  • Padole A, Singh S, Ackman JB, et al. Submillisievert chest CT with filtered back projection and iterative reconstruction techniques. AJR Am J Roentgenol. 2014;203(4):772–781. doi:10.2214/AJR.13.12312
  • Kanemaru N, Takao H, Amemiya S, Abe O. The effect of a post-scan processing denoising system on image quality and morphometric analysis. J Neuroradiol. 2022;49(2):205–212. doi:10.1016/j.neurad.2021.11.007
  • Buades A, Coll B, Morel JM. A non-local algorithm for image denoising. Conference on Computer Vision and Pattern Recognition; San Diego, CA; 2005:60–65.
  • Fardon DF, Williams AL, Dohring EJ, Murtagh FR, Gabriel Rothman SL, Sze GK. Lumbar disc nomenclature: version 2.0: recommendations of the combined task forces of the North American Spine Society, the American Society of Spine Radiology and the American Society of Neuroradiology. Spine J. 2014;14(11):2525–2545. doi:10.1016/j.spinee.2014.04.022
  • Fu MC, Webb ML, Buerba RA, et al. Comparison of agreement of cervical spine degenerative pathology findings in magnetic resonance imaging studies. Spine J. 2016;16(1):42–48. doi:10.1016/j.spinee.2015.08.026
  • Kang Y, Lee JW, Koh YH, et al. New MRI grading system for the cervical canal stenosis. AJR Am J Roentgenol. 2011;197(1):W134–W140. doi:10.2214/AJR.10.5560
  • Kim SJ, Lee TH, Yi S. Prevalence of disc degeneration in asymptomatic Korean subjects. Part 3: cervical and lumbar relationship. J Korean Neurosurg Soc. 2013;53(3):167–173. doi:10.3340/jkns.2013.53.3.167
  • Lee KH, Park HJ, Lee SY, et al. Inter-observer reliability and clinical validity of the MRI grading system for cervical central stenosis based on sagittal T2-weighted image. Eur J Radiol. 2020;127:108987. doi:10.1016/j.ejrad.2020.108987
  • Lee S, Lee JW, Yeom JS, et al. A practical MRI grading system for lumbar foraminal stenosis. AJR Am J Roentgenol. 2010;194(4):1095–1098. doi:10.2214/AJR.09.2772
  • Miyazaki M, Hong SW, Yoon SH, Morishita Y, Wang JC. Reliability of a magnetic resonance imaging-based grading system for cervical intervertebral disc degeneration. J Spinal Disord Tech. 2008;21(4):288–292. doi:10.1097/BSD.0b013e31813c0e59
  • Modic MT, Ross JS. Lumbar degenerative disk disease. Radiology. 2007;245(1):43–61. doi:10.1148/radiol.2451051706
  • Obuchowski NA, Subhas N, Schoenhagen P. Testing for interchangeability of imaging tests. Acad Radiol. 2014;21(11):1483–1489. doi:10.1016/j.acra.2014.07.004
  • Zanchi F, Richard R, Hussami M, Monier A, Knebel JF, Omoumi P. MRI of non-specific low back pain and/or lumbar radiculopathy: do we need T1 when using a sagittal T2-weighted Dixon sequence? Eur Radiol. 2020;30(5):2583–2593. doi:10.1007/s00330-019-06626-6
  • Galley J, Sutter R, Germann C, Wanivenhaus F, Nanz D. High-resolution in vivo MR imaging of intraspinal cervical nerve rootlets at 3 and 7 Tesla. Eur Radiol. 2021;31(7):4625–4633. doi:10.1007/s00330-020-07557-3
  • Yasaka K, Tanishima T, Ohtake Y, et al. Deep learning reconstruction for 1.5 T cervical spine MRI: effect on interobserver agreement in the evaluation of degenerative changes. Eur Radiol. 2022;32(9):6118–6125. doi:10.1007/s00330-022-08729-z
  • Uetani H, Nakaura T, Kitajima M, et al. A preliminary study of deep learning-based reconstruction specialized for denoising in high-frequency domain: usefulness in high-resolution three-dimensional magnetic resonance cisternography of the cerebellopontine angle. Neuroradiology. 2021;63(1):63–71. doi:10.1007/s00234-020-02513-w
  • Mishro PK, Agrawal S, Panda R, Abraham A. A survey on State-of-the-Art Denoising techniques for brain magnetic resonance images. IEEE Rev Biomed Eng. 2022;15:184–199. doi:10.1109/RBME.2021.3055556