Deep Learning Superresolution for Simultaneous Multislice Parallel Imaging-Accelerated Knee MRI Using Arthroscopy Validation

被引:1
|
作者
Walter, Sven S. [1 ,2 ]
Vosshenrich, Jan [1 ,3 ]
Rodrigues, Tatiane Cantarelli [4 ]
Dalili, Danoob [5 ]
Fritz, Benjamin [6 ]
Kijowski, Richard [1 ]
Park, Eun Hae [1 ,7 ,8 ]
Serfaty, Aline [9 ]
Stern, Steven E. [10 ]
Brinkmann, Inge [11 ]
Koerzdoerfer, Gregor [12 ]
Fritz, Jan [1 ]
机构
[1] NYU Grossman Sch Med, Dept Radiol, Div Musculoskeletal Radiol, 660 1st Ave,3rd Fl,Rm 313, New York, NY 10016 USA
[2] Eberhard Karls Univ Tubingen, Univ Hosp Tubingen, Dept Diagnost & Intervent Radiol, Tubingen, Germany
[3] Univ Hosp Basel, Dept Radiol, Basel, Switzerland
[4] Hosp Coracao, Dept Radiol, Sao Paulo, Brazil
[5] South West London Elective Orthopaed Ctr SWLEOC, Acad Surg Unit, London, England
[6] Balgrist Univ Hosp, Dept Radiol, Zurich, Switzerland
[7] Chonbuk Natl Univ Hosp, Dept Radiol, Jeonju, South Korea
[8] Res Inst Clin Med Jeonbuk Natl Univ, Biomed Res Inst Jeonbuk Natl Univ Hosp, Jeonju 54907, Jeonbuk, South Korea
[9] Medscanlagos Radiol, Cabo Frio, Rio De Janeiro, Brazil
[10] Bond Univ, Ctr Data Analyt, Gold Coast, Australia
[11] Siemens Healthineers AG, Erlangen, Germany
[12] Siemens Med Solut USA, Malvern, PA USA
关键词
ARTICULAR-CARTILAGE; RESOLUTION; SEQUENCE; TEARS; RECONSTRUCTION; NETWORK;
D O I
10.1148/radiol.241249
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) methods can improve accelerated MRI but require validation against an independent reference standard to ensure robustness and accuracy. Purpose: To validate the diagnostic performance of twofold-simultaneous-multislice (SMSx2) twofold-parallel-imaging (PIx2)-accelerated DL superresolution MRI in the knee against conventional SMSx2-PIx2-accelerated MRI using arthroscopy as the reference standard. Materials and Methods: Adults with painful knee conditions were prospectively enrolled from December 2021 to October 2022. Participants underwent fourfold SMSx2-PIx2-accelerated standard-of-care and investigational DL superresolution MRI at 3 T. Seven radiologists independently evaluated the MRI examinations for overall image quality (using Likert scale scores: 1, very bad, to 5, very good) and the presence or absence of meniscus and ligament tears. Articular cartilage was categorized as intact, or partial or full-thickness defects. Statistical analyses included interreader agreements (Cohen kappa and Gwet AC2) and diagnostic performance testing used area under the receiver operating characteristic curve (AUC) values. Results: A total of 116 adults (mean age, 45 years +/- 15 [SD]; 74 men) who underwent arthroscopic surgery within 38 days +/- 22 were evaluated. Overall image quality was better for DL superresolution MRI (median Likert score, 5; range, 3-5) than conventional MRI (median Likert score, 4; range, 3-5) (P < .001). Diagnostic performances of conventional versus DL superresolution MRI were similar for medial meniscus tears (AUC, 0.94 [95% CI: 0.89, 0.97] vs 0.94 [95% CI: 0.90, 0.98], respectively; P > .99), lateral meniscus tears (AUC, 0.85 [95% CI: 0.78, 0.91] vs 0.87 [95% CI: 0.81, 0.94], respectively; P = .96), and anterior cruciate ligament tears (AUC, 0.98 [95% CI: 0.93, >0.99] vs 0.98 [95% CI: 0.93, >0.99], respectively; P > .99). DL superresolution MRI (AUC, 0.78; 95% CI: 0.75, 0.81) had higher diagnostic performance than conventional MRI (AUC, 0.71; 95% CI: 0.67, 0.74; P = .002) for articular cartilage lesions. DL superresolution MRI did not introduce hallucinations or erroneously omit abnormalities. Conclusion: Compared with conventional SMSx2-PIx2-accelerated MRI, fourfold SMSx2-PIx2-accelerated DL superresolution MRI in the knee provided better image quality, similar performance for detecting meniscus and ligament tears, and improved performance for depicting articular cartilage lesions. (c) RSNA, 2025
引用
收藏
页数:13
相关论文
共 39 条
  • [1] Deep Learning-Enhanced Parallel Imaging and Simultaneous Multislice Acceleration Reconstruction in Knee MRI
    Kim, MinWoo
    Lee, Sang-Min
    Park, Chankue
    Lee, Dongeon
    Kim, Kang Soo
    Jeong, Hee Seok
    Kim, Shinyoung
    Choi, Min-Hyeok
    Nickel, Dominik
    INVESTIGATIVE RADIOLOGY, 2022, 57 (12) : 826 - 833
  • [2] Five-minute Five-Sequence Knee MRI Using Combined Simultaneous Multislice and Parallel Imaging Acceleration: Comparison with 10-minute Parallel Imaging Knee MRI
    Del Grande, Filippo
    Rashidi, Ali
    Luna, Rodrigo
    Delcogliano, Marco
    Stern, Steven E.
    Dalili, Danoob
    Fritz, Jan
    RADIOLOGY, 2021, 299 (03) : 635 - 646
  • [3] Diagnostic Accuracy of an MRI Protocol of the Knee Accelerated Through Parallel Imaging in Correlation to Arthroscopy
    Schnaiter, Johannes Walter
    Roemer, Frank
    McKenna-Kuettner, Axel
    Patzak, Hans-Joachim
    May, Matthias Stefan
    Janka, Rolf
    Uder, Michael
    Wuest, Wolfgang
    ROFO-FORTSCHRITTE AUF DEM GEBIET DER RONTGENSTRAHLEN UND DER BILDGEBENDEN VERFAHREN, 2018, 190 (03): : 265 - 272
  • [4] Simultaneous Multislice Imaging in Dynamic Cardiac MRI at 7T Using Parallel Transmission
    Schmitter, Sebastian
    Moeller, Steen
    Wu, Xiaoping
    Auerbach, Edward J.
    Metzger, Gregory J.
    Van de Moortele, Pierre-Francois
    Ugurbil, Kamil
    MAGNETIC RESONANCE IN MEDICINE, 2017, 77 (03) : 1010 - 1020
  • [5] Model-based reconstruction for simultaneous multislice and parallel imaging accelerated multishot diffusion tensor imaging
    Dong, Zijing
    Dai, Erpeng
    Wang, Fuyixue
    Zhang, Zhe
    Ma, Xiaodong
    Yuan, Chun
    Guo, Hua
    MEDICAL PHYSICS, 2018, 45 (07) : 3196 - 3204
  • [6] Free-breathing Accelerated Cardiac MRI Using Deep Learning: Validation in Children and Young Adults
    Zucker, Evan J.
    Sandino, Christopher M.
    Kino, Aya
    Lai, Peng
    Vasanawala, Shreyas S.
    RADIOLOGY, 2021, 300 (03) : 539 - 548
  • [7] Simultaneous Multislice Accelerated Turbo Spin Echo Magnetic Resonance Imaging Comparison and Combination With In-Plane Parallel Imaging Acceleration for High-Resolution Magnetic Resonance Imaging of the Knee
    Fritz, Jan
    Fritz, Benjamin
    Zhang, Jialu
    Thawait, Gaurav K.
    Joshi, Dharmdev H.
    Pan, Li
    Wang, Dingxin
    INVESTIGATIVE RADIOLOGY, 2017, 52 (09) : 529 - 537
  • [8] Simultaneous image reconstruction and lesion segmentation in accelerated MRI using multitasking learning
    Sui, Bin
    Lv, Jun
    Tong, Xiangrong
    Li, Yan
    Wang, Chengyan
    MEDICAL PHYSICS, 2021, 48 (11) : 7189 - 7198
  • [9] Unpaired Deep Learning for Accelerated MRI Using Optimal Transport Driven CycleGAN
    Oh, Gyutaek
    Sim, Byeongsu
    Chung, HyungJin
    Sunwoo, Leonard
    Ye, Jong Chul
    IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING, 2020, 6 (06) : 1285 - 1296
  • [10] Accelerated EPR imaging using deep learning denoising
    Canavesi, Irene
    Viswakarma, Navin
    Epel, Boris
    Mcmillan, Alan
    Kotecha, Mrignayani
    MAGNETIC RESONANCE IN MEDICINE, 2025, : 436 - 446