Deep Learning Reconstruction of Accelerated MRI: False-Positive Cartilage Delamination Inserted in MRI Arthrography Under Traction.
Journal
Topics in magnetic resonance imaging : TMRI
ISSN: 1536-1004
Titre abrégé: Top Magn Reson Imaging
Pays: United States
ID NLM: 8913523
Informations de publication
Date de publication:
01 Aug 2024
01 Aug 2024
Historique:
received:
12
04
2024
accepted:
28
05
2024
medline:
17
7
2024
pubmed:
17
7
2024
entrez:
17
7
2024
Statut:
epublish
Résumé
The radiological imaging industry is developing and starting to offer a range of novel artificial intelligence software solutions for clinical radiology. Deep learning reconstruction of magnetic resonance imaging data seems to allow for the acceleration and undersampling of imaging data. Resulting reduced acquisition times would lead to greater machine utility and to greater cost-efficiency of machine operations. Our case shows images from magnetic resonance arthrography under traction of the right hip joint from a 30-year-old, otherwise healthy, male patient. The undersampled image data when reconstructed by a deep learning tool can contain false-positive cartilage delamination and false-positive diffuse cartilage defects. In the future, precision of this novel technology will have to be put to thorough testing. Bias of systems, in particular created by the choice of training data, will have to be part of those assessments.
Identifiants
pubmed: 39016321
doi: 10.1097/RMR.0000000000000313
pii: 00002142-202408000-00001
doi:
Types de publication
Journal Article
Case Reports
Langues
eng
Sous-ensembles de citation
IM
Pagination
e0313Informations de copyright
Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.
Références
Barat M, Soyer P, Dohan A. Appropriateness of Recommendations Provided by ChatGPT to Interventional Radiologists. Can Assoc Radiol J. 2023;74(4):758–763.
Winkel DJ, Heye T, Weikert TJ, et al. Evaluation of an AI-based detection software for acute findings in abdominal computed tomography scans: toward an automated work list prioritization of routine CT examinations. Invest Radiol. 2019;54(1):55–59.
Ramedani S, Ramedani M, Von Tengg-Kobligk H, et al. A deep learning-based fully automated approach for body composition analysis in 3D whole body dixon MRI. In: 2023 IEEE 19th International Conference on Intelligent Computer Communication and Processing (ICCP); 2023:287–292. doi: 10.1109/ICCP60212.2023.10398648.
Bosbach WA, Senge JF, Nemeth B, et al. Ability of ChatGPT to generate competent radiology reports for distal radius fracture by use of RSNA template items and integrated AO classifier. Curr Probl Diagn Radiol. 2024;53(1):102–110.
Senge JF., Mc Murray MT, Haupt F, et al. ChatGPT may free time needed by the interventional radiologist for administration/documentation: A study on the RSNA PICC line reporting template. Swiss J Radiol Nucl Med. 2024;7(2):1–14.
Deshmane A, Gulani V, Griswold MA, et al. Parallel MR imaging. J Magn Reson Imaging. 2012;36(1):55–72.
Hammernik K, Klatzer T, Kobler E, et al. Learning a variational network for reconstruction of accelerated MRI data. Magn Reson Med. 2018;79(6):3055–3071.
Bosbach WA, Heinrich M, Kolisch R, et al. Maximization of open hospital capacity under shortage of SARS-CoV-2 vaccines-an open access, stochastic simulation tool. Vaccines. 2021;9(6):546.
Johnson PM, Lin DJ, Zbontar J, et al. Deep learning reconstruction enables prospectively accelerated clinical knee MRI. Radiology. 2023;307(2):e220425.
Siemens Healthcare GmbH. In: Deep Resolve; 2023. Available at: https://www.siemens-healthineers.com/magnetic-resonance-imaging/technologies-and-innovations/deep-resolve. Accessed September 18, 2023.
Lin DJ, Walter SS, Fritz J. Artificial intelligence-driven ultra-fast superresolution MRI: 10-fold accelerated musculoskeletal turbo spin echo MRI within reach. Invest Radiol. 2023;58(1):28–42.
Perarnau G, Alvarez JM. Invertible conditional GANs for image editing. arXiv Prepr. 2016;1611:06355. doi: 10.48550/arXiv.1611.06355.
doi: 10.48550/arXiv.1611.06355
Garni SN, Mertineit N, Nöldge G, et al., Regulatory needs for radiation protection devices based upon artificial intelligence—state task or leave unregulated? Swiss J Radiol Nucl Med. 2024;5:5. doi: 10.59667/sjoranm.v5i1.11.
doi: 10.59667/sjoranm.v5i1.11