Deep Learning Reconstruction for Accelerated Spine MRI: Prospective Analysis of Interchangeability

被引:44
|
作者
Almansour, Haidara [1 ]
Herrmann, Judith [1 ]
Gassenmaier, Sebastian [1 ]
Afat, Saif [1 ]
Jacoby, Johann [2 ]
Koerzdoerfer, Gregor [3 ]
Nickel, Dominik [3 ]
Mostapha, Mahmoud [4 ]
Nadar, Mariappan [4 ]
Othman, Ahmed E. [1 ,5 ]
机构
[1] Eberhard Karls Univ Tubingen, Tuebingen Univ Hosp, Dept Diagnost & Intervent Radiol, Hoppe Seyler Str 3, D-72076 Tubingen, Germany
[2] Eberhard Karls Univ Tubingen, Tuebingen Univ Hosp, Inst Clin Epidemiol & Appl Biometry, Hoppe Seyler Str 3, D-72076 Tubingen, Germany
[3] Siemens Healthineers, Dept MR Applicat Predev, Erlangen, Germany
[4] Siemens Healthineers, Dept Digital Technol & Innovat, Princeton, NJ USA
[5] Univ Med Ctr Mainz, Dept Neuroradiol, Mainz, Germany
关键词
PROTOCOL;
D O I
10.1148/radiol.212922
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Background: Deep learning (DL)-based MRI reconstructions can reduce examination times for turbo spin-echo (TSE) acquisitions. Studies that prospectively employ DL-based reconstructions of rapidly acquired, undersampled spine MRI are needed.Purpose: To investigate the diagnostic interchangeability of an unrolled DL-reconstructed TSE (hereafter, TSEDL) T1-and T2 -weighted acquisition method with standard TSE and to test their impact on acquisition time, image quality, and diagnostic confidence.Materials and Methods: This prospective single-center study included participants with various spinal abnormalities who gave written consent from November 2020 to July 2021. Each participant underwent two MRI examinations: standard fully sampled T1-and T2-weighted TSE acquisitions (reference standard) and prospectively undersampled TSEDL acquisitions with threefold and fourfold acceleration. Image evaluation was performed by five readers. Interchangeability analysis and an image quality-based analysis were used to compare the TSE and TSEDL images. Acquisition time and diagnostic confidence were also compared. Interchangeability was tested using the individual equivalence index regarding various degenerative and nondegenerative entities, which were analyzed on each vertebra and defined as discordant clinical judgments of less than 5%. Interreader and intrareader agreement and concor-dance (kappa and Kendall tau and W statistics) were computed and Wilcoxon and McNemar tests were used.Results: Overall, 50 participants were evaluated (mean age, 46 years +/- 18 [SD]; 26 men). The TSEDL method enabled up to a 70% reduction in total acquisition time (100 seconds for TSEDL vs 328 seconds for TSE, P < .001). All individual equivalence indexes were less than 4%. TSEDL acquisition was rated as having superior image noise by all readers (P < .001). No evidence of a difference was found between standard TSE and TSEDL regarding frequency of major findings, overall image quality, or diagnostic confidence.Conclusion: The deep learning (DL)-reconstructed turbo spin-echo (TSE) method was found to be interchangeable with standard TSE for detecting various abnormalities of the spine at MRI. DL-reconstructed TSE acquisition provided excellent image quality, with a 70% reduction in examination time. German Clinical Trials Register no. DRKS00023278
引用
收藏
页数:8
相关论文
共 50 条
  • [31] k-Space Deep Learning for Accelerated MRI
    Han, Yoseob
    Sunwoo, Leonard
    Ye, Jong Chul
    IEEE TRANSACTIONS ON MEDICAL IMAGING, 2020, 39 (02) : 377 - 386
  • [32] Bayesian Deep Learning for Accelerated MR Image Reconstruction
    Schlemper, Jo
    Castro, Daniel C.
    Bai, Wenjia
    Qin, Chen
    Oktay, Ozan
    Duan, Jinming
    Price, Anthony N.
    Hajnal, Jo
    Rueckert, Daniel
    MACHINE LEARNING FOR MEDICAL IMAGE RECONSTRUCTION, MLMIR 2018, 2018, 11074 : 64 - 71
  • [33] Deep learning for efficient reconstruction of highly accelerated 3D FLAIR MRI in neurological deficits
    Liebrand, Luka C.
    Karkalousos, Dimitrios
    Poirion, Emilie
    Emmer, Bart J.
    Roosendaal, Stefan D.
    Marquering, Henk A.
    Majoie, Charles B. L. M.
    Savatovsky, Julien
    Caan, Matthan W. A.
    MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE, 2025, 38 (01): : 1 - 12
  • [34] Review of Deep Learning Methods for MRI Reconstruction
    Deng, Gewen
    Wei, Guohui
    Ma, Zhiqing
    Computer Engineering and Applications, 2023, 59 (20) : 67 - 76
  • [35] Adaptive Deep Dictionary Learning for MRI Reconstruction
    Lewis, D. John
    Singhal, Vanika
    Majumdar, Angshul
    NEURAL INFORMATION PROCESSING (ICONIP 2018), PT I, 2018, 11301 : 3 - 11
  • [36] Deep learning based MRI reconstruction with transformer
    Wu, Zhengliang
    Liao, Weibin
    Yan, Chao
    Zhao, Mangsuo
    Liu, Guowen
    Ma, Ning
    Li, Xuesong
    COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2023, 233
  • [37] Recurrent Variational Network: A Deep Learning Inverse Problem Solver applied to the task of Accelerated MRI Reconstruction
    Yiasemis, George
    Sonke, Jan-Jakob
    Sanchez, Clarisa
    Teuwen, Jonas
    2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022), 2022, : 722 - 731
  • [38] Clinical Impact of Deep Learning Reconstruction in MRI
    Kiryu, Shigeru
    Akai, Hiroyuki
    Yasaka, Koichiro
    Tajima, Taku
    Kunimatsu, Akira
    Yoshioka, Naoki
    Akahane, Masaaki
    Abe, Osamu
    Ohtomo, Kuni
    RADIOGRAPHICS, 2023, 43 (06)
  • [39] Feasibility of an accelerated 2D-multi-contrast knee MRI protocol using deep-learning image reconstruction: a prospective intraindividual comparison with a standard MRI protocol
    Herrmann, Judith
    Keller, Gabriel
    Gassenmaier, Sebastian
    Nickel, Dominik
    Koerzdoerfer, Gregor
    Mostapha, Mahmoud
    Almansour, Haidara
    Afat, Saif
    Othman, Ahmed E.
    EUROPEAN RADIOLOGY, 2022, 32 (09) : 6215 - 6229
  • [40] Using Deep Learning to Accelerate Knee MRI at 3 T: Results of an Interchangeability Study
    Recht, Michael P.
    Zbontar, Jure
    Sodickson, Daniel K.
    Knoll, Florian
    Yakubova, Nafissa
    Sriram, Anuroop
    Murrell, Tullie
    Defazio, Aaron
    Rabbat, Michael
    Rybak, Leon
    Kline, Mitchell
    Ciavarra, Gina
    Alaia, Erin F.
    Samim, Mohammad
    Walter, William R.
    Lin, Dana J.
    Lui, Yvonne W.
    Muckley, Matthew
    Huang, Zhengnan
    Johnson, Patricia
    Stern, Ruben
    Zitnick, C. Lawrence
    AMERICAN JOURNAL OF ROENTGENOLOGY, 2020, 215 (06) : 1421 - 1429