Deep Learning Super-Resolution Reconstruction for Fast and Motion-Robust T2-weighted Prostate MRI

被引:39
|
作者
Bischoff, Leon M. [1 ,2 ]
Peeters, Johannes M. [5 ]
Weinhold, Leonie [3 ]
Krausewitz, Philipp [4 ]
Ellinger, Joerg [4 ]
Katemann, Christoph [6 ]
Isaak, Alexander [1 ,2 ]
Weber, Oliver M. [6 ]
Kuetting, Daniel [1 ,2 ]
Attenberger, Ulrike
Pieper, Claus C. [1 ]
Sprinkart, Alois M. [1 ,2 ]
Luetkens, Julian A. [1 ,2 ]
机构
[1] Univ Hosp Bonn, Dept Diagnost & Intervent Radiol, Venusberg Campus 1, D-53127 Bonn, Germany
[2] Univ Hosp Bonn, Quantitat Imaging Lab Bonn QILaB, Venusberg Campus 1, D-53127 Bonn, Germany
[3] Univ Hosp Bonn, Inst Med Biometry Informat & Epidemiol, Venusberg Campus 1, D-53127 Bonn, Germany
[4] Univ Hosp Bonn, Dept Urol, Venusberg Campus 1, D-53127 Bonn, Germany
[5] Philips MR Clin Sci, Best, Netherlands
[6] Philips Market DACH, Hamburg, Germany
关键词
CANCER;
D O I
10.1148/radiol.230427
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) reconstructions can enhance image quality while decreasing MRI acquisition time. However, DL reconstruction methods combined with compressed sensing for prostate MRI have not been well studied. Purpose: To use an industry-developed DL algorithm to reconstruct low-resolution T2-weighted turbo spin-echo (TSE) prostate MRI scans and compare these with standard sequences. Materials and Methods: In this prospective study, participants with suspected prostate cancer underwent prostate MRI with a Cartesian standard-resolution T2-weighted TSE sequence (T2 (C)) and non-Cartesian standard-resolution T2-weighted TSE sequence (T2 (NC)) between August and November 2022. Additionally, a low-resolution Cartesian DL-reconstructed T2-weighted TSE sequence (T2 (DL)) with compressed sensing DL denoising and resolution upscaling reconstruction was acquired. Image sharpness was assessed qualitatively by two readers using a five-point Likert scale (from 1 = nondiagnostic to 5 = excellent) and quantitatively by calculating edge rise distance. The Friedman test and one-way analysis of variance with post hoc Bonferroni and Tukey tests, respectively, were used for group comparisons. Prostate Imaging Reporting and Data System (PI-RADS) score agreement between sequences was compared by using Cohen kappa. Results: This study included 109 male participants (mean age, 68 years +/- 8 [SD]). Acquisition time of T2 (DL) was 36% and 29% lower compared with that of T2 (C) and T2 (NC) (mean duration, 164 seconds +/- 20 vs 257 seconds +/- 32 and 230 seconds +/- 28; P <.001 for both). T2 (DL) showed improved image sharpness compared with standard sequences using both qualitative (median score, 5 [IQR, 4-5] vs 4 [IQR, 3-4] for T2 (C) and 4 [IQR, 3-4] for T2 (NC); P <.001 for both) and quantitative (mean edge rise distance, 0.75 mm +/- 0.39 vs 1.15 mm +/- 0.68 for T2 (C) and 0.98 mm +/- 0.65 for T2 (NC); P <.001 and P =.01) methods. PI-RADS score agreement between T2 (NC) and T2 (DL) was excellent (. range, 0.92-0.94 [95% CI: 0.87, 0.98]). Conclusion: DL reconstruction of low-resolution T2-weighted TSE sequences enabled accelerated acquisition times and improved image quality compared with standard acquisitions while showing excellent agreement with conventional sequences for PI-RADS ratings.
引用
收藏
页数:9
相关论文
共 50 条
  • [31] Super-resolution reconstruction of propeller wake based on deep learning
    Li, Changming
    Liang, Bingchen
    Wan, Yingdi
    Yuan, Peng
    Zhang, Qin
    Liu, Yongkai
    Zhao, Ming
    PHYSICS OF FLUIDS, 2024, 36 (11)
  • [32] Deep learning for fast super-resolution ultrasound microvessel imaging
    Luan, Shunyao
    Yu, Xiangyang
    Lei, Shuang
    Ma, Chi
    Wang, Xiao
    Xue, Xudong
    Ding, Yi
    Ma, Teng
    Zhu, Benpeng
    PHYSICS IN MEDICINE AND BIOLOGY, 2023, 68 (24):
  • [33] An Intercenter Intensity Normalization for Prostate T2-Weighted MRI
    Gholizadeh, N.
    Greer, P.
    Lau, P.
    Ramadan, S.
    Simpson, J.
    ASIA-PACIFIC JOURNAL OF CLINICAL ONCOLOGY, 2017, 13 : 24 - 25
  • [34] Editorial for "Comparison of a Deep Learning-Accelerated Versus Conventional T2-Weighted Sequence in Biparametric MRI of the Prostate"
    Wu, Zhe
    Bhayana, Rajesh
    Uludag, Kamil
    JOURNAL OF MAGNETIC RESONANCE IMAGING, 2023, 58 (04) : 1065 - 1066
  • [35] Comparison of a Deep Learning-Accelerated vs. Conventional T2-Weighted Sequence in Biparametric MRI of the Prostate
    Tong, Angela
    Bagga, Barun
    Petrocelli, Robert
    Smereka, Paul
    Vij, Abhinav
    Qian, Kun
    Grimm, Robert
    Kamen, Ali
    Keerthivasan, Mahesh B.
    Nickel, Marcel Dominik
    von Busch, Heinrich
    Chandarana, Hersh
    JOURNAL OF MAGNETIC RESONANCE IMAGING, 2023, 58 (04) : 1055 - 1064
  • [36] DeepSWI: Using Deep Learning to Enhance Susceptibility Contrast on T2-Weighted MRI
    Genc, Ozan
    Morrison, Melanie A.
    Villanueva-Meyer, Javier E.
    Burns, Brian
    Hess, Christopher P.
    Banerjee, Suchandrima
    Lupo, Janine M.
    JOURNAL OF MAGNETIC RESONANCE IMAGING, 2023, 58 (04) : 1200 - 1210
  • [37] Advancing MRI Technology with Deep Learning Super Resolution Reconstruction
    Luetkens, Julian A.
    Kravchenko, Dmitrij
    ACADEMIC RADIOLOGY, 2024, 31 (10) : 4183 - 4184
  • [38] SUPER-RESOLUTION RECONSTRUCTION OF CARDIAC MRI USING COUPLED DICTIONARY LEARNING
    Bhatia, Kanwal K.
    Price, Anthony N.
    Shi, Wenzhe
    Hajnal, Jo V.
    Rueckert, Daniel
    2014 IEEE 11TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI), 2014, : 947 - 950
  • [39] Segmented Multislice Acquisition for Motion-Insensitive Super Resolution Multislice T2-Weighted Fast-Spin-Echo Imaging
    Kargar, S.
    Borisch, E.
    Froemming, A.
    Grimm, R.
    Kawashima, A.
    King, B.
    Stinson, E.
    Riederer, S.
    MEDICAL PHYSICS, 2020, 47 (06) : E400 - E400
  • [40] Accelerated 3D high-resolution T2-weighted breast MRI with deep learning constrained compressed sensing, comparison with conventional T2-weighted sequence on 3.0 T
    Yang, Fan
    Pan, Xuelin
    Zhu, Ke
    Xiao, Yitian
    Yue, Xun
    Peng, Pengfei
    Zhang, Xiaoyong
    Huang, Juan
    Chen, Jie
    Yuan, Yuan
    Sun, Jiayu
    EUROPEAN JOURNAL OF RADIOLOGY, 2022, 156