1. Using Deep Learning to Accelerate Knee MRI at 3 T: Results of an Interchangeability Study
- Author
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Matthew J. Muckley, Jure Zbontar, William R. Walter, Mohammad Samim, Tullie Murrell, Aaron Defazio, Nafissa Yakubova, Leon Rybak, Gina A. Ciavarra, C. Lawrence Zitnick, Zhengnan Huang, Dana J Lin, Florian Knoll, Mitchell J Kline, Erin F. Alaia, Michael G. Rabbat, Ruben Stern, Anuroop Sriram, Michael P. Recht, Yvonne W. Lui, Patricia M. Johnson, and Daniel K. Sodickson
- Subjects
Adult ,Male ,Adolescent ,Knee Joint ,Image quality ,Knee Injuries ,Iterative reconstruction ,Signal-To-Noise Ratio ,Interchangeability ,Article ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,Acceleration ,Deep Learning ,0302 clinical medicine ,Data acquisition ,Knee mri ,Image Processing, Computer-Assisted ,Humans ,Medicine ,Radiology, Nuclear Medicine and imaging ,Aged ,Aged, 80 and over ,business.industry ,Deep learning ,Pattern recognition ,General Medicine ,Middle Aged ,Magnetic Resonance Imaging ,Feature (computer vision) ,030220 oncology & carcinogenesis ,Female ,Artificial intelligence ,business - Abstract
OBJECTIVE: Deep learning (DL) image reconstruction has the potential to disrupt the current state of MRI by significantly decreasing the time required for MRI examinations. Our goal was to use DL to accelerate MRI to allow a 5-minute comprehensive examination of the knee without compromising image quality or diagnostic accuracy. MATERIALS AND METHODS. A DL model for image reconstruction using a variational network was optimized. The model was trained using dedicated multisequence training, in which a single reconstruction model was trained with data from multiple sequences with different contrast and orientations. After training, data from 108 patients were retrospectively undersampled in a manner that would correspond with a net 3.49-fold acceleration of fully sampled data acquisition and a 1.88-fold acceleration compared with our standard twofold accelerated parallel acquisition. An interchangeability study was performed, in which the ability of six readers to detect internal derangement of the knee was compared for clinical and DL-accelerated images. RESULTS. We found a high degree of interchangeability between standard and DL-accelerated images. In particular, results showed that interchanging the sequences would produce discordant clinical opinions no more than 4% of the time for any feature evaluated. Moreover, the accelerated sequence was judged by all six readers to have better quality than the clinical sequence. CONCLUSION. An optimized DL model allowed acceleration of knee images that performed interchangeably with standard images for detection of internal derangement of the knee. Importantly, readers preferred the quality of accelerated images to that of standard clinical images.
- Published
- 2020
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