Peer-Reviewed Research
Highly accelerated knee magnetic resonance imaging using deep neural network (DNN)–based reconstruction: prospective, multi-reader, multi-vendor study
Our summary
This study evaluated the performance of SwiftMR in accelerating the knee imaging protocol. 45 participants with knee pain were scanned on three MRI scanners from three different vendors, where the findings suggested that the scan time for the standard-of-care protocol could be reduced by 41% without compromising image quality or diagnostic accuracy.
Why this matters
The findings showcase the potential of SwiftMR in optimizing MRI exams across multiple MR scanner vendors, offering faster imaging without sacrificing diagnostic quality. This advancement could enhance patient comfort and MRI efficiency, marking a significant step forward in medical imaging technology.
Explore the full study for a deeper understanding of SwiftMR’s impact on MRI efficiency and its implications for future clinical practices in the linked article.
Lee, J., Jung, M., Park, J., Kim, S., Im, Y., Lee, N., et al. (2023). Highly accelerated knee magnetic resonance imaging using deep neural network (DNN)–based reconstruction: prospective, multi-reader, multi-vendor study. Scientific Reports, 13(1), 17264
