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Comparative analysis of image quality and interchangeability between standard and deep learning-reconstructed T2-weighted spine MRI.
Lee, Seungeun; Jung, Joon-Yong; Chung, Heeyoung; Lee, Hyun-Soo; Nickel, Dominik; Lee, Jooyeon; Lee, So-Yeon.
Affiliation
  • Lee S; Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea.
  • Jung JY; Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea. Electronic address: jy.jung@songeui.ac.kr.
  • Chung H; Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea.
  • Lee HS; Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea; Siemens Healthineers, Seoul 06620, Republic of Korea. Electronic address: hyunsoo.lee@siemens-healthineers.com.
  • Nickel D; Siemens Healthcare GmbH, Allee am Roethelheimpark, Erlangen 91052, Germany. Electronic address: marcel.nickel@siemens-healthineers.com.
  • Lee J; Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea; Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, School of Public Health, Houston, TX 77030, USA. Electron
  • Lee SY; Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea.
Magn Reson Imaging ; 109: 211-220, 2024 Jun.
Article in En | MEDLINE | ID: mdl-38513791
ABSTRACT
RATIONALE AND

OBJECTIVES:

MRI reconstruction of undersampled data using a deep learning (DL) network has been recently performed as part of accelerated imaging. Herein, we compared DL-reconstructed T2-weighted image (T2-WI) to conventional T2-WI regarding image quality and degenerative lesion detection. MATERIALS AND

METHODS:

Sixty-two patients underwent C-spine (n = 27) or L-spine (n = 35) MRIs, including conventional and DL-reconstructed T2-WI. Image quality was assessed with non-uniformity measurement and 4-scale grading of structural visibility. Three readers (R1, R2, R3) independently assessed the presence and types of degenerative lesions. Student t-test was used to compare non-uniformity measurements. Interprotocol and interobserver agreement of structural visibility was analyzed with Wilcoxon signed-rank test and weighted-κ values, respectively. The diagnostic equivalence of degenerative lesion detection between two protocols was assessed with interchangeability test.

RESULTS:

The acquisition time of DL-reconstructed images was reduced to about 21-58% compared to conventional images. Non-uniformity measurement was insignificantly different between the two images (p-value = 0.17). All readers rated DL-reconstructed images as showing the same or superior structural visibility compared to conventional images. Significantly improved visibility was observed at disk margin of C-spine (R1, p < 0.001; R2, p = 0.04) and dorsal root ganglia (R1, p = 0.03; R3, p = 0.02) and facet joint (R1, p = 0.04; R2, p < 0.001; R3, p = 0.03) of L-spine. Interobserver agreements of image quality were variable in each structure. Clinical interchangeability between two protocols for degenerative lesion detection was verified showing <5% in the upper bounds of 95% confidence intervals of agreement rate differences.

CONCLUSIONS:

DL-reconstructed T2-WI demonstrates comparable image quality and diagnostic performance with conventional T2-WI in spine imaging, with reduced acquisition time.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Deep Learning Limits: Humans Language: En Journal: Magn Reson Imaging Year: 2024 Document type: Article Country of publication: Netherlands

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Deep Learning Limits: Humans Language: En Journal: Magn Reson Imaging Year: 2024 Document type: Article Country of publication: Netherlands