Wolfram A Bosbach, Kim Carolin Merdes, Bernd Jung, Elham Montazeri, Suzanne Anderson, Milena Mitrakovic, Keivan Daneshvar
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Deep Learning Reconstruction of Accelerated MRI: False-Positive Cartilage Delamination Inserted in MRI Arthrography Under Traction.
Objectives: 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.
Materials and methods: Our case shows images from magnetic resonance arthrography under traction of the right hip joint from a 30-year-old, otherwise healthy, male patient.
Results: The undersampled image data when reconstructed by a deep learning tool can contain false-positive cartilage delamination and false-positive diffuse cartilage defects.
Conclusions: 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.
期刊介绍:
Topics in Magnetic Resonance Imaging is a leading information resource for professionals in the MRI community. This publication supplies authoritative, up-to-the-minute coverage of technical advances in this evolving field as well as practical, hands-on guidance from leading experts. Six times a year, TMRI focuses on a single timely topic of interest to radiologists. These topical issues present a variety of perspectives from top radiological authorities to provide an in-depth understanding of how MRI is being used in each area.