Image Quality and Diagnostic Performance of Accelerated 2D Hip MRI with Deep Learning Reconstruction Based on a Deep Iterative Hierarchical Network

被引:1
|
作者
Herrmann, Judith [1 ]
Afat, Saif [1 ]
Gassenmaier, Sebastian [1 ]
Koerzdoerfer, Gregor [2 ]
Lingg, Andreas [1 ]
Almansour, Haidara [1 ]
Nickel, Dominik [2 ]
Werner, Sebastian [1 ]
机构
[1] Eberhard Karls Univ Tuebingen, Dept Diagnost & Intervent Radiol, Hoppe Seyler Str 3, D-72076 Tubingen, Germany
[2] Siemens Healthcare GmbH, MR Applicat Predev, Allee Roethelheimpk 2, D-91052 Erlangen, Germany
关键词
magnetic resonance imaging; deep learning reconstruction; image processing; diagnostic imaging; hip; CONVOLUTIONAL NEURAL-NETWORKS; SPIN-ECHO SEQUENCE; KNEE; PROTOCOL; SPACE;
D O I
10.3390/diagnostics13203241
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Objectives: Hip MRI using standard multiplanar sequences requires long scan times. Accelerating MRI is accompanied by reduced image quality. This study aimed to compare standard two-dimensional (2D) turbo spin echo (TSE) sequences with accelerated 2D TSE sequences with deep learning (DL) reconstruction (TSEDL) for routine clinical hip MRI at 1.5 and 3 T in terms of feasibility, image quality, and diagnostic performance.Material and Methods: In this prospective, monocentric study, TSEDL was implemented clinically and evaluated in 14 prospectively enrolled patients undergoing a clinically indicated hip MRI at 1.5 and 3T between October 2020 and May 2021. Each patient underwent two examinations: For the first exam, we used standard sequences with generalized autocalibrating partial parallel acquisition reconstruction (TSES). For the second exam, we implemented prospectively undersampled TSE sequences with DL reconstruction (TSEDL). Two radiologists assessed the TSEDL and TSES regarding image quality, artifacts, noise, edge sharpness, diagnostic confidence, and delineation of anatomical structures using an ordinal five-point Likert scale (1 = non-diagnostic; 2 = poor; 3 = moderate; 4 = good; 5 = excellent). Both sequences were compared regarding the detection of common pathologies of the hip. Comparative analyses were conducted to assess the differences between TSEDL and TSES.Results: Compared with TSES, TSEDL was rated to be significantly superior in terms of image quality (p <= 0.020) with significantly reduced noise (p <= 0.001) and significantly improved edge sharpness (p = 0.003). No difference was found between TSES and TSEDL concerning the extent of artifacts, diagnostic confidence, or the delineation of anatomical structures (p > 0.05). Example acquisition time reductions for the TSE sequences of 52% at 3 Tesla and 70% at 1.5 Tesla were achieved.Conclusion: TSEDL of the hip is clinically feasible, showing excellent image quality and equivalent diagnostic performance compared with TSES, reducing the acquisition time significantly.
引用
收藏
页数:11
相关论文
共 50 条
  • [31] Improving Diagnostic Performance of MRI for Temporal Lobe Epilepsy With Deep Learning-Based Image Reconstruction in Patients With Suspected Focal Epilepsy
    Suh, Pae Sun
    Park, Ji Eun
    Roh, Yun Hwa
    Kim, Seonok
    Jung, Mina
    Koo, Yong Seo
    Lee, Sang-Ahm
    Choi, Yangsean
    Kim, Ho Sung
    KOREAN JOURNAL OF RADIOLOGY, 2024, 25 (04) : 374 - 383
  • [32] Deep learning reconstruction in pediatric brain MRI: comparison of image quality with conventional T2-weighted MRI
    Kim, Soo-Hyun
    Choi, Young Hun
    Lee, Joon Sung
    Lee, Seul Bi
    Cho, Yeon Jin
    Lee, Seung Hyun
    Shin, Su-Mi
    Cheon, Jung-Eun
    NEURORADIOLOGY, 2023, 65 (01) : 207 - 214
  • [33] Deep learning reconstruction in pediatric brain MRI: comparison of image quality with conventional T2-weighted MRI
    Soo-Hyun Kim
    Young Hun Choi
    Joon Sung Lee
    Seul Bi Lee
    Yeon Jin Cho
    Seung Hyun Lee
    Su-Mi Shin
    Jung-Eun Cheon
    Neuroradiology, 2023, 65 : 207 - 214
  • [34] Deep learning denoising reconstruction for improved image quality in fetal cardiac cine MRI
    Vollbrecht, Thomas M.
    Hart, Christopher
    Zhang, Shuo
    Katemann, Christoph
    Sprinkart, Alois M.
    Isaak, Alexander
    Attenberger, Ulrike
    Pieper, Claus C.
    Kuetting, Daniel
    Geipel, Annegret
    Strizek, Brigitte
    Luetkens, Julian A.
    FRONTIERS IN CARDIOVASCULAR MEDICINE, 2024, 11
  • [35] Diagnostic performance and image quality of deep learning image reconstruction (DLIR) on unenhanced low-dose abdominal CT for urolithiasis
    Delabie, Aurelien
    Bouzerar, Roger
    Pichois, Raphael
    Desdoit, Xavier
    Vial, Jeremie
    Renard, Cedric
    ACTA RADIOLOGICA, 2022, 63 (09) : 1283 - 1292
  • [36] Image Quality and Lesion Detection of Multiplanar Reconstruction Images Using Deep Learning: Comparison with Hybrid Iterative Reconstruction
    Yunaga, Hiroto
    Miyoshi, Hidenao
    Ochiai, Ryoya
    Gonda, Takuro
    Sakoh, Toshio
    Noma, Hisashi
    Fujii, Shinya
    YONAGO ACTA MEDICA, 2024, 67 (02) : 100 - 107
  • [37] DEEP LEARNING FOR MRI RECONSTRUCTION USING A NOVEL PROJECTION BASED CASCADED NETWORK
    Kocanaogullari, Deniz
    Eksioglu, Ender M.
    2019 IEEE 29TH INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING (MLSP), 2019,
  • [38] Thin-Slice Pituitary MRI with Deep Learning-based Reconstruction: Diagnostic Performance in a Postoperative Setting
    Kim, Minjae
    Kim, Ho Sung
    Kim, Hyun Jin
    Park, Ji Eun
    Park, Seo Young
    Kim, Young-Hoon
    Kim, Sang Joon
    Lee, Joonsung
    Lebel, Marc R.
    RADIOLOGY, 2021, 298 (01) : 114 - 122
  • [39] Digital rock image reconstruction based on deep learning and its reconstruction performance evaluation
    Wang F.
    Zhao J.
    Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology), 2022, 53 (11): : 4412 - 4424
  • [40] 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