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Deep learning–based reconstruction for acceleration of lumbar spine MRI: a prospective comparison with standard MRI

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Abstract

Objective

To compare the image quality and diagnostic performance between standard turbo spin-echo MRI and accelerated MRI with deep learning (DL)–based image reconstruction for degenerative lumbar spine diseases.

Materials and methods

Fifty patients who underwent both the standard and accelerated lumbar MRIs at a 1.5-T scanner for degenerative lumbar spine diseases were prospectively enrolled. DL reconstruction algorithm generated coarse (DL_coarse) and fine (DL_fine) images from the accelerated protocol. Image quality was quantitatively assessed in terms of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) and qualitatively assessed using five-point visual scoring systems. The sensitivity and specificity of four radiologists for the diagnosis of degenerative diseases in both protocols were compared.

Results

The accelerated protocol reduced the average MRI acquisition time by 32.3% as compared to the standard protocol. As compared with standard images, DL_coarse and DL_fine showed significantly higher SNRs on T1-weighted images (T1WI; both p < .001) and T2-weighted images (T2WI; p = .002 and p < 0.001), higher CNRs on T1WI (both p < 0.001), and similar CNRs on T2WI (p = .49 and p = .27). The average radiologist assessment of overall image quality for DL_coarse and DL_fine was higher on sagittal T1WI (p = .04 and p < .001) and axial T2WI (p = .006 and p = .01) and similar on sagittal T2WI (p = .90 and p = .91). Both DL_coarse and DL_fine had better image quality of cauda equina and paraspinal muscles on axial T2WI (both p = .04 for cauda equina; p = .008 and p = .002 for paraspinal muscles). Differences in sensitivity and specificity for the detection of central canal stenosis and neural foraminal stenosis between standard and DL-reconstructed images were all statistically nonsignificant (p ≥ 0.05).

Conclusion

DL-based protocol reduced MRI acquisition time without degrading image quality and diagnostic performance of readers for degenerative lumbar spine diseases.

Clinical relevance statement

The deep learning (DL)–based reconstruction algorithm may be used to further accelerate spine MRI imaging to reduce patient discomfort and increase the cost efficiency of spine MRI imaging.

Key Points

• By using deep learning (DL)–based reconstruction algorithm in combination with the accelerated MRI protocol, the average acquisition time was reduced by 32.3% as compared with the standard protocol.

• DL-reconstructed images had similar or better quantitative/qualitative overall image quality and similar or better image quality for the delineation of most individual anatomical structures.

• The average radiologist’s sensitivity and specificity for the detection of major degenerative lumbar spine diseases, including central canal stenosis, neural foraminal stenosis, and disc herniation, on standard and DL-reconstructed images, were similar.

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Abbreviations

CNN:

Convolutional neural network

CNR:

Contrast-to-noise ratio

CSF:

Cerebrospinal fluid

DL:

Deep learning

SNR:

Signal-to-noise ratio

SSIM:

Structural similarity index

T1WI:

T1-weighted images

T2WI:

T2-weighted images

TSE:

Turbo spin-echo

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Acknowledgements

We thank Yunhee Choi at the Division of Medical Statistics, Medical Research Collaborating Center, Seoul National University Hospital for providing advice on statistical interpretation of the data.

Funding

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2021R1A4A1028713 and NRF-2023R1A2C3003250) and the Korea Medical Device Development Fund grant funded by the Korean government (the Ministry of Science and ICT, South Korea; the Ministry of Trade, Industry and Energy; the Ministry of Health and Welfare, Republic of Korea; and the Ministry of Food and Drug Safety) (project no. 9991007218, KMDF_PR_20200901_0086). This study received technical support and a research grant (grant number 06- 2021–2210) from AIRS Medical Inc.

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Correspondence to Roh-Eul Yoo.

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Guarantor

The scientific guarantor of this publication is Roh-Eul Yoo, Seoul National University Hospital.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Roh-Eul Yoo is a Junior Deputy Editor of European Radiology. She has not taken part in the review or selection process of this article.

Statistics and biometry

Yunhee Choi at the Division of Medical Statistics, Medical Research Collaborating Center, Seoul National University Hospital, kindly provided statistical advice for this manuscript.

Informed consent

Written informed consent was obtained from all subjects in this study.

Ethical approval

This study was approved by the institutional review board of Seoul National University Hospital (IRB No.2103-174-1207).

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• diagnostic study

• performed at one institution

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Yoo, H., Yoo, RE., Choi, S.H. et al. Deep learning–based reconstruction for acceleration of lumbar spine MRI: a prospective comparison with standard MRI. Eur Radiol 33, 8656–8668 (2023). https://doi.org/10.1007/s00330-023-09918-0

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