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Application research of AI-assisted compressed sensing technology in MRI scanning of the knee joint: 3D-MRI perspective

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

Abstract

Objective

To investigate the potential applicability of AI-assisted compressed sensing (ACS) in knee MRI to enhance and optimize the scanning process.

Methods

Volunteers and patients with sports-related injuries underwent prospective MRI scans with a range of acceleration techniques. The volunteers were subjected to varied ACS acceleration levels to ascertain the most effective level. Patients underwent scans at the determined optimal 3D-ACS acceleration level, and 3D compressed sensing (CS) and 2D parallel acquisition technology (PAT) scans were performed. The resultant 3D-ACS images underwent 3.5 mm/2.0 mm multiplanar reconstruction (MPR). Experienced radiologists evaluated and compared the quality of images obtained by 3D-ACS-MRI and 3D-CS-MRI, 3.5 mm/2.0 mm MPR and 2D-PAT-MRI, diagnosed diseases, and compared the results with the arthroscopic findings. The diagnostic agreement was evaluated using Cohen’s kappa correlation coefficient, and both absolute and relative evaluation methods were utilized for objective assessment.

Results

The study involved 15 volunteers and 53 patients. An acceleration factor of 10.69 × was identified as optimal. The quality evaluation showed that 3D-ACS provided poorer bone structure visualization, and improved cartilage visualization and less satisfactory axial images with 3.5 mm/2.0 mm MPR than 2D-PAT. In terms of objective evaluation, the relative evaluation yielded satisfactory results across different groups, while the absolute evaluation revealed significant variances in most features. Nevertheless, high levels of diagnostic agreement (κ: 0.81–0.94) and accuracy (0.83–0.98) were observed across all diagnoses.

Conclusion

ACS technology presents significant potential as a replacement for traditional CS in 3D-MRI knee scans, allowing thinner MPRs and markedly faster scans without sacrificing diagnostic accuracy.

Clinical relevance statement

3D-ACS-MRI of the knee can be completed in the 160 s with good diagnostic consistency and image quality. 3D-MRI-MPR can replace 2D-MRI and reconstruct images with thinner slices, which helps to optimize the current MRI examination process and shorten scanning time.

Key Points

• AI-assisted compressed sensing technology can reduce knee MRI scan time by over 50%.

• 3D AI-assisted compressed sensing MRI and related multiplanar reconstruction can replace traditional accelerated MRI and yield thinner 2D multiplanar reconstructions.

• Successful application of 3D AI-assisted compressed sensing MRI can help optimize the current knee MRI process.

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Abbreviations

ACS:

AI-assisted compressed sensing

CS:

Compressed sensing

HF:

Half Fourier

MPR:

Multiplanar reconstruction

MSK:

Musculoskeletal

PAT:

Parallel acquisition technology

PD-FSE-FS:

Proton density-weighted fast spin‒echo with fat saturation

PSNR:

Peak signal-to-noise ratio

SSIM:

Structural similarity index

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Funding

This study received financial support from the National Natural Science Foundation of China (82171927), the Beijing Natural Science Foundation (7212126), and the Beijing New Health Industry Development Foundation (XM2020-02–006).

Author information

Author notes

  1. Ming Ni and Miao He contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Peking University Third Hospital, Beijing, People’s Republic of China

    Ming Ni, Yuqing Zhao, Lixiang Gao, Ruixin Yan, Jiajia Xu, Yarui Zhang, Wen Chen, Chenyu Jiang, Yali Li, Qiang Zhao & Huishu Yuan

  2. School of Biomedical Engineering, Capital Medical University, Beijing, 100069, People’s Republic of China

    Miao He, Chunlin Li & Junda Qu

  3. Beijing Key Laboratory of Fundamental Research On Biomechanics in Clinical Application, Capital Medical University, Beijing, People’s Republic of China

    Miao He, Chunlin Li & Junda Qu

  4. Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Capital Medical University, Beijing, People’s Republic of China

    Miao He, Chunlin Li & Junda Qu

  5. United Imaging Research Institute of Intelligent Imaging, Beijing, People’s Republic of China

    Yuxin Yang

  6. Institute of Statistics and Big Data, Renmin University of China, Beijing, People’s Republic of China

    Xiaoyi Wen

  7. United Imaging Healthcare Co, Shanghai, People’s Republic of China

    Peng Wu

Authors
  1. Ming Ni
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Corresponding authors

Correspondence to Junda Qu or Huishu Yuan.

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Guarantor

The scientific guarantor of this publication is Huishu Yuan.

Conflict of interest

The authors of this manuscript declare relationships with the following companies: United Imaging. Among the authors, Yunxin Yang and Peng Wu are employees of the company, and are mainly involved in the experimental protocol design, MRI scanning sequence debugging, and manuscript revision of this study. To ensure the authenticity and fairness of the results, we do not allow company personnel to touch the data processing process. The investigators of this study did not receive any compensation.

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

Statistics and biometry

Xiaoyi Wen, from the Institute of Statistics and Big Data, Renmin University of China, Beijing, People’s Republic of China, was responsible for all statistical content of this study and is listed as one of the authors.

Informed consent

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

Ethical approval

Institutional Review Board approval was obtained. The medical science research ethics committee of Peking University Third Hospital approved this prospective study.

Study subjects or cohorts overlap

No study subjects or cohorts have been previously reported.

Methodology

  • • prospective

  • • diagnostic or prognostic study

  • • performed at one institution

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Ni, M., He, M., Yang, Y. et al. Application research of AI-assisted compressed sensing technology in MRI scanning of the knee joint: 3D-MRI perspective. Eur Radiol 34, 3046–3058 (2024). https://doi.org/10.1007/s00330-023-10368-x

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  • DOI: https://doi.org/10.1007/s00330-023-10368-x

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