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Accelerated 3D MR neurography of the brachial plexus using deep learning–constrained compressed sensing

  • Magnetic Resonance
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To explore the use of deep learning–constrained compressed sensing (DLCS) in improving image quality and acquisition time for 3D MRI of the brachial plexus.

Methods

Fifty-four participants who underwent contrast-enhanced imaging and forty-one participants who underwent unenhanced imaging were included. Sensitivity encoding with an acceleration of 2 × 2 (SENSE4x), CS with an acceleration of 4 (CS4x), and DLCS with acceleration of 4 (DLCS4x) and 8 (DLCS8x) were used for MRI of the brachial plexus. Apparent signal-to-noise ratios (aSNRs), apparent contrast-to-noise ratios (aCNRs), and qualitative scores on a 4-point scale were evaluated and compared by ANOVA and the Friedman test. Interobserver agreement was evaluated by calculating the intraclass correlation coefficients.

Results

DLCS4x achieved higher aSNR and aCNR than SENSE4x, CS4x, and DLCS8x (all p < 0.05). For the root segment of the brachial plexus, no statistically significant differences in the qualitative scores were found among the four sequences. For the trunk segment, DLCS4x had higher scores than SENSE4x (p = 0.04) in the contrast-enhanced group and had higher scores than SENSE4x and DLCS8x in the unenhanced group (all p < 0.05). For the divisions, cords, and branches, DLCS4x had higher scores than SENSE4x, CS4x, and DLCS8x (all p ≤ 0.01). No overt difference was found among SENSE4x, CS4x, and DLCS8x in any segment of the brachial plexus (all p > 0.05).

Conclusions

In three-dimensional MRI for the brachial plexus, DLCS4x can improve image quality compared with SENSE4x and CS4x, and DLCS8x can maintain the image quality compared to SENSE4x and CS4x.

Clinical relevance statement

Deep learning–constrained compressed sensing can improve the image quality or accelerate acquisition of 3D MRI of the brachial plexus, which should be benefit in evaluating the brachial plexus and its branches in clinical practice.

Key Points

•Deep learning–constrained compressed sensing showed higher aSNR, aCNR, and qualitative scores for the brachial plexus than SENSE and CS at the same acceleration factor with similar scanning time.

•Deep learning–constrained compressed sensing at acceleration factor of 8 had comparable aSNR, aCNR, and qualitative scores to SENSE4x and CS4x with approximately half the examination time.

•Deep learning–constrained compressed sensing may be helpful in clinical practice for improving image quality and acquisition time in three-dimensional MRI of the brachial plexus.

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Abbreviations

aCNR:

Apparent contrast-to-noise ratio

aSNR:

Apparent signal-to-noise ratio

CS:

Compressed sensing

CS4x:

Compressed sensing with an acceleration factor of 4

DLCS:

Deep learning–constrained CS

DLCS4x:

DLCS with an acceleration factor of 4

DLCS8x:

DLCS with an acceleration factor of 8

MRN:

Magnetic resonance neurography

SENSE4x:

Sensitivity encoding with an acceleration factor of 2 × 2

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Acknowledgements

We thank Hans Peeters (Philips, Best, the Netherlands) for the technical help of this study.

Funding

This study has received funding by the 1.3.5 project for disciplines of excellence, West China Hospital, Sichuan University (grant number: ZYGD18019) and Sichuan Province Science and Technology Support Program (grant number: 22QYCX0106).

Author information

Author notes

  1. Chun-Chao Xia and Zhen-Lin Li contributed equally to this work and should be considered as co-corresponding authors.

Authors and Affiliations

  1. Department of Radiology, West China Hospital, Sichuan University, 37 Guo Xue Xiang, Chengdu, 610041, Sichuan, China

    Si-xian Hu, Yi Xiao, Wan-lin Peng, Wen Zeng, Yu Zhang, Chun-chao Xia & Zhen-lin Li

  2. Clinical Science, Philips Healthcare, Chengdu, Sichuan, China

    Xiao-yong Zhang & Chun-tang Ling

  3. C&TS, Philips Healthcare, Guangzhou, Guangdong, China

    Hai-xia Li

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Corresponding authors

Correspondence to Chun-chao Xia or Zhen-lin Li.

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Guarantor

The scientific guarantor of this publication is Prof. Zhen-lin Li.

Conflict of interest

The authors of this manuscript declare relationships with the following companies: three authors of this manuscript (Xiao-yong Zhang, Chun-tang Ling, and Hai-Xia Li) are employees of Philips Healthcare, and they are participants in the technique support and analysis of the data. The remaining authors declare no relationships with any companies whose products or services may be related to the subject matter of the manuscript. Zhen-Lin Li of West China Hospital controls the data.

Statistics and biometry

One of the authors (Hai-xia Li) has significant statistical expertise.

Informed consent

Written informed consent was obtained from each subject in this study.

Ethical approval

Institutional Review Board approval was obtained from the Biomedical Research Ethics Committee of West China Hospital.

Study subjects or cohorts overlap

None.

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• prospective

• diagnostic or prognostic study

• performed at one institution

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Hu, Sx., Xiao, Y., Peng, Wl. et al. Accelerated 3D MR neurography of the brachial plexus using deep learning–constrained compressed sensing. Eur Radiol 34, 842–851 (2024). https://doi.org/10.1007/s00330-023-09996-0

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