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Application and research progress of MRI deep learning image reconstruction technology in clinical diagnosis of musculoskeletal system diseases

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  • Department of Radiology, Renmin Hospital of Wuhan University, Hubei Wuhan 430060, China

Received date: 2024-03-26

  Online published: 2024-07-04

Abstract

Deep learning based reconstruction (DLR) technology is currently one of the most cutting-edge technological advancements in the field of MRI image reconstruction. Compared to conventional MRI image reconstruction techniques, DLR technology redefines a new boundary between signal-to-noise ratio, spatial resolution, and scanning time on MRI. Its outstanding technical advantage is the effective removal of image noise and artifacts, significantly reducing scanning time, and also has potential advantages in improving the detection rate and accuracy qualitative diagnosis of lesions. With the continuous optimization of algorithms and the improvement of model generalization, DLR has been widely used in MRI examinations for multiple parts, such as the nervous system, musculoskeletal system, and heart. Its applicable scanning sequences and clinical application scenarios are also constantly expanding. DLR technology, while maintaining the original spatial resolution, reduces the number of signal acquisition times and increases the parallel acquisition acceleration factor to shorten the imaging time by more than 50%, achieving rapid imaging of the musculoskeletal system, and obtaining significantly better image quality than traditional reconstructed images. Currently, DLR is widely used in MRI exa-minations of musculoskeletal systems, such as the knee, shoulder, wrist, and spine, and has demonstrated its outstanding performance in shortening imaging time, improving image signal-to-noise ratio and resolution.

Cite this article

ZHA Yunfei, WU Xiaxia . Application and research progress of MRI deep learning image reconstruction technology in clinical diagnosis of musculoskeletal system diseases[J]. Journal of Diagnostics Concepts & Practice, 2024 , 23(02) : 114 -118 . DOI: 10.16150/j.1671-2870.2024.02.003

References

[1] CHAUDHARI A S, STEVENS K J, SVEINSSON B, et al. Combined 5-minute double-echo in steady-state with separated echoes and 2-minute proton-density-weighted 2D FSE sequence for comprehensive whole-joint knee MRI assessment[J]. J Magn Reson Imaging, 2019, 49(7):e183-e194.
[2] 严福华. 深度学习图像重建算法的临床应用和发展前景[J]. 中华放射学杂志, 2022, 56(11):1165-1167.
  YAN F H. The clinical application and development prospect of deep learning reconstruction algorithm[J]. Chin J Radiol, 2022, 56(11):1165-1167.
[3] FRITZ J, GUGGENBERGER R, DEL GRANDE F. Rapid musculoskeletal MRI in 2021: clinical application of advanced accelerated techniques[J]. AJR, 2021, 216(3):718-733.
[4] HAHN S, YI J, LEE HJ, et al. Comparison of deep lear-ning-based reconstruction of PROPELLER Shoulder MRI with conventional reconstruction[J]. Skeletal Radiol, 2023, 52(8):1545-1555.
[5] YANG R, ZOU Y, LIU W V, et al. High-resolution single-shot fast spin-echo mr imaging with deep learning reconstruction algorithm can improve repeatability and reproducibility of follicle counting[J]. J Clin Med, 2023, 12(9):3234.
[6] HILBERT T, SUMPF T J, WEILAND E, et al. Accele-rated T2 mapping combining parallel MRI and model-based reconstruction: GRAPPATINI[J]. J Magn Reson Imaging, 2018, 48(2):359-368.
[7] CHAUDHARI A S, GRISSOM M J, FANG Z, et al. Diagnostic accuracy of quantitative multicontrast 5-minute knee MRI using prospective artificial intelligence image quality enhancement[J]. AJR, 2021, 216(6):1614-1625.
[8] WANG S S, XIAO T H, LIU Q G, et al. Deep learning for fast MR imaging: a review for learning reconstruction from incomplete k-space data[J]. Biomed Signal Process Control, 2021, 68(5):102579.
[9] MANCO L, MAFFEI N, STROLIN S, et al. Basic of machine learning and deep learning in imaging for medical physicists[J]. Phys Med, 2021, 83:194-205.
[10] PETERS R D, HARRIS H, LAWSON S. The clinical be-nefits of AIRTM Recon DL for MR image reconstruction[EB/OL]. [2022-11-07]. https://www.gehealthcare.com/ensg/-/jssmedia/c943df5927a049bb9ac95a9f0349ad8c.pdf.
[11] LEBEL R M. Performance characterization of a novel deep learning-based MR image reconstruction pipeline[EB/OL]. [2022-11-07]. https://arxiv.org/ftp/arxiv/papers/2008/2008.06559.pdf.
[12] HERRMANN J, KOERZDOERFER G, NICKEL D, et al. Feasibility and implementation of a deep learning mr reconstruction for TSE sequences in musculoskeletal imaging[J]. Diagnostics(Basel), 2021, 11(8):1484.
[13] RECHT M P, ZBONTAR J, SODICKSON D K, et al. Usi-ng deep learning to accelerate knee MRI at 3 T: results of an interchangeability study[J]. AJR, 2020, 215(6):1421-1429.
[14] JOHNSON P M, LIN D J, ZBONTAR J, et al. Deep lear-ning reconstruction enables prospectively accelerated clinical knee MRI[J]. Radiology, 2023, 307(2):e220425.
[15] 武夏夏, 陆雪芳, 刘昌盛, 等. 深度学习图像重建算法在膝关节加速MRI中的临床应用研究[J]. 磁共振成像, 2023, 14(5):53-59.
  WU X X, LU X F, LIU C S, et al. Clinical feasibility of 2D FSE sequences of the knee MRI protocol using deep-learning image reconstruction[J]. Chin J Magn Reson Ima-ging, 2023, 14(5):53-59.
[16] HERRMANN J, KELLER G, GASSENMAIER S, et al. Feasibility of an accelerated 2D-multi-contrast knee MRI protocol using deep-learning image reconstruction: a prospective intraindividual comparison with a standard MRI protocol[J]. Eur Radiol, 2022, 32(9):6215-6229.
[17] CHANG P D, CHOW D S. Revolutionizing Shoulder MRI: Accelerated Imaging with Deep Learning Reconstruction[J]. Radiology, 2024, 310(1):e233301.
[18] HAHN S, YI J, LEE H J, et al. Image quality and diagnostic performance of accelerated shoulder MRI with deep learning-based reconstruction[J]. AJR, 2022, 218(3):506-516.
[19] KANIEWSKA M, DEININGER-CZERMAK E, GETZMANN J M, et al. Application of deep learning-based image reconstruction in MR imaging of the shoulder joint to improve image quality and reduce scan time[J]. Eur Radiol, 2023, 33(3):1513-1525.
[20] XIE Y, TAO H, LI X, et al. Prospective comparison of standard and deep learning-reconstructed turbo spin-echo MRI of the shoulder[J]. Radiology, 2024, 310(1):e231405.
[21] HERRMANN J, GASSENMAIER S, KELLER G, et al. Deep Learning MRI reconstruction for accelerating turbo spin echo hand and wrist imaging: A comparison of image quality, visualization of anatomy, and detection of common pathologies with standard imaging[J]. Acad Radiol, 2023, 30(11):2606-2615.
[22] YASAKA K, TANISHIMA T, OHTAKE Y, et al. Deep learning reconstruction for the evaluation of neuroforaminal stenosis using 1.5T cervical spine MRI: comparison with 3T MRI without deep learning reconstruction[J]. Neuroradiology, 2022, 64(10):2077-2083.
[23] JARDON M, TAN E T, CHAZEN J L, et al. Deep-learni-ng-reconstructed high-resolution 3D cervical spine MRI for foraminal stenosis evaluation[J]. Skeletal Radiol, 2023, 52(4):725-732.
[24] SUN S, TAN E T, MINTZ D N, et al. Evaluation of deep learning reconstructed high-resolution 3D lumbar spine MRI[J]. Eur Radiol, 2022, 32(9):6167-6177.
[25] ALMANSOUR H, HERRMANN J, GASSENMAIER S, et al. Deep learning reconstruction for accelerated spine MRI: prospective analysis of interchangeability[J]. Radio-logy, 2023, 306(3):e212922.
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