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Open Access 28-06-2023 | Magnetic Resonance Imaging | Scientific Article

Fast high-quality MRI protocol of the lumbar spine with deep learning-based algorithm: an image quality and scanning time comparison with standard protocol

Authors: Marta Zerunian, Francesco Pucciarelli, Damiano Caruso, Domenico De Santis, Michela Polici, Benedetta Masci, Ilaria Nacci, Antonella Del Gaudio, Giuseppe Argento, Andrea Redler, Andrea Laghi

Published in: Skeletal Radiology | Issue 1/2024

Abstract

Objective

The objective of this study is to prospectively compare quantitative and subjective image quality, scanning time, and diagnostic confidence between a new deep learning-based reconstruction(DLR) algorithm and standard MRI protocol of lumbar spine.

Materials and methods

Eighty healthy volunteers underwent 1.5T MRI examination of lumbar spine from September 2021 to May 2023. Protocol acquisition comprised sagittal T1- and T2-weighted fast spin echo and short-tau inversion recovery images and axial multislices T2-weighted fast spin echo images. All sequences were acquired with both DLR algorithm and standard protocols. Two radiologists, blinded to the reconstruction technique, performed quantitative and qualitative image quality analysis in consensus reading; diagnostic confidence was also assessed. Quantitative image quality analysis was assessed by calculating signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Qualitative image quality analysis and diagnostic confidence were assessed with a five-point Likert scale. Scanning times were also compared.

Results

DLR SNR was higher in all sequences (all p<0.001). CNR of the DLR was superior to conventional dataset only for axial and sagittal T2-weighted fast spin echo images (p<0.001). Qualitative analysis showed DLR had higher overall quality in all sequences (all p<0.001), with an inter-rater agreement of 0.83 (0.78–0.86).

DLR total protocol scanning time was lower compared to standard protocol (6:26 vs 12:59 min, p<0.001).

Diagnostic confidence for DLR algorithm was not inferior to standard protocol.

Conclusion

DLR applied to 1.5T MRI is a feasible method for lumbar spine imaging providing morphologic sequences with higher image quality and similar diagnostic confidence compared with standard protocol, enabling a remarkable time saving (up to 50%).

Corp N, Mansell G, Stynes S, Wynne-Jones G, Morsø L, Hill JC, et al. Evidence-based treatment recommendations for neck and low back pain across Europe: a systematic review of guidelines. Eur J Pain. 2021;25(2):275–95.CrossRefPubMed

Collaborators GDaIIaP. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392(10159):1789–858.CrossRef

Kovacs FM, Arana E, Royuela A, Estremera A, Amengual G, Asenjo B, et al. Disc degeneration and chronic low back pain: an association which becomes nonsignificant when endplate changes and disc contour are taken into account. Neuroradiology. 2014;56(1):25–33.CrossRefPubMed

Jensen TS, Karppinen J, Sorensen JS, Niinimäki J, Leboeuf-Yde C. Vertebral endplate signal changes (Modic change): a systematic literature review of prevalence and association with non-specific low back pain. Eur Spine J. 2008;17(11):1407–22.CrossRefPubMedPubMedCentral

Brinjikji W, Diehn FE, Jarvik JG, Carr CM, Kallmes DF, Murad MH, et al. MRI findings of disc degeneration are more prevalent in adults with low back pain than in asymptomatic controls: a systematic review and meta-analysis. AJNR Am J Neuroradiol. 2015;36(12):2394–9.CrossRefPubMedPubMedCentral

Lebel RM. Performance characterization of a novel deep learning-based MR image reconstruction pipeline. arXiv; 2020. Available from: http://arxiv.org/abs/2008.06559.

Lundervold AS, Lundervold A. An overview of deep learning in medical imaging focusing on MRI. Z Med Phys. 2019;29(2):102–27.CrossRefPubMed

Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J. 2019;6(2):94–8.CrossRefPubMedPubMedCentral

Dong C, Loy CC, He K, Tang X. Learning a deep convolutional network for image super-resolution. In: In Computer Vision–ECCV 2014: 13th European Conference. Zurich, Switzerland: Springer International Publishing; 2014. p. 184–99.CrossRef

10.

Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. In: In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference. Munich, Germany: Springer International Publishing; 2015. p. 234–41.CrossRef

11.

Chartrand G, Cheng PM, Vorontsov E, Drozdzal M, Turcotte S, Pal CJ, et al. Deep learning: a primer for radiologists. Radiographics. 2017;37(7):2113–31.CrossRefPubMed

12.

Tsai JY, Hung IY, Guo YL, Jan YK, Lin CY, Shih TT, et al. Lumbar disc herniation automatic detection in magnetic resonance imaging based on deep learning. Front Bioeng Biotechnol. 2021;9:708137.CrossRefPubMedPubMedCentral

13.

JTPD H, Zhu L, Yang K, Makmur A, DAR A, Thian YL, et al. Deep learning model for automated detection and classification of central canal, lateral recess, and neural foraminal stenosis at lumbar spine MRI. Radiology. 2021;300(1):130–8.CrossRef

14.

LewandrowskI KU, Muraleedharan N, Eddy SA, Sobti V, Reece BD, Ramírez León JF, et al. Feasibility of deep learning algorithms for reporting in routine spine magnetic resonance imaging. Int J Spine Surg. 2020;14(s3):S86–97.CrossRefPubMedPubMedCentral

15.

Zhang W, Zhu J, Xu X, Fan G. Synthetic MRI of the lumbar spine at 3.0 T: feasibility and image quality comparison with conventional MRI. Acta Radiol. 2020;61(4):461–70.CrossRefPubMed

16.

De Santis D, Jin KN, Schoepf UJ, Grant KL, De Cecco CN, Nance JW, et al. Heavily calcified coronary arteries: advanced calcium subtraction improves luminal visualization and diagnostic confidence in dual-energy coronary computed tomography angiography. Invest Radiol. 2018;53(2):103–9.CrossRefPubMed

17.

Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977;33(1):159–74.CrossRefPubMed

18.

Herrmann J, Koerzdoerfer G, Nickel D, Mostapha M, Nadar M, Gassenmaier S, et al. Feasibility and implementation of a deep learning MR reconstruction for TSE sequences in musculoskeletal imaging. Diagnostics (Basel). 2021;11(8):1484.CrossRefPubMedPubMedCentral

19.

Almansour H, Herrmann J, Gassenmaier S, Afat S, Jacoby J, Koerzdoerfer G, et al. Deep learning reconstruction for accelerated spine MRI: prospective analysis of interchangeability. Radiology. 2023;306(3):e212922.CrossRefPubMed

20.

Hahn S, Yi J, Lee HJ, Lee Y, Lim YJ, Bang JY, et al. Image quality and diagnostic performance of accelerated shoulder MRI with deep learning-based reconstruction. AJR Am J Roentgenol. 2021;218:3.

21.

van der Velde N, Hassing HC, Bakker BJ, Wielopolski PA, Lebel RM, Janich MA, et al. Improvement of late gadolinium enhancement image quality using a deep learning-based reconstruction algorithm and its influence on myocardial scar quantification. Eur Radiol. 2021;31(6):3846–55.CrossRefPubMed

22.

Kim M, Kim HS, Kim HJ, Park JE, Park SY, Kim YH, et al. Thin-slice pituitary MRI with deep learning-based reconstruction: diagnostic performance in a postoperative setting. Radiology. 2021;298(1):114–22.CrossRefPubMed

23.

Schreiber-Zinaman J, Rosenkrantz AB. Frequency and reasons for extra sequences in clinical abdominal MRI examinations. Abdom Radiol (NY). 2017;42(1):306–11.CrossRefPubMed

24.

Bratke G, Rau R, Weiss K, Kabbasch C, Sircar K, Morelli JN, et al. Accelerated MRI of the lumbar spine using compressed sensing: quality and efficiency. J Magn Reson Imaging. 2019;49(7):e164–e75.CrossRefPubMed

Title: Fast high-quality MRI protocol of the lumbar spine with deep learning-based algorithm: an image quality and scanning time comparison with standard protocol
Authors: Marta Zerunian
Francesco Pucciarelli
Damiano Caruso
Domenico De Santis
Michela Polici
Benedetta Masci
Ilaria Nacci
Antonella Del Gaudio
Giuseppe Argento
Andrea Redler
Andrea Laghi
Publication date: 28-06-2023
Publisher: Springer Berlin Heidelberg
Keywords: Magnetic Resonance Imaging
Magnetic Resonance Imaging
Artificial Intelligence
Published in: Skeletal Radiology / Issue 1/2024
Print ISSN: 0364-2348
Electronic ISSN: 1432-2161
DOI: https://doi.org/10.1007/s00256-023-04390-9

ERS Congress 2024

Springer Medicine

Fast high-quality MRI protocol of the lumbar spine with deep learning-based algorithm: an image quality and scanning time comparison with standard protocol

Abstract

Objective

Materials and methods

Results

Conclusion

ERS Congress 2024

Springer Medicine

Abstract

Objective

Materials and methods

Results

Conclusion

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