Transfer learning in deep neural network based under-sampled MR image reconstruction |
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| Institution: | 1. Spinoza Centre for Neuroimaging, Amsterdam 1105 BK, the Netherlands;2. Informatics Institute at the University of Amsterdam, Amsterdam 1098 XH, the Netherlands;3. Netherlands Cancer Institute, Amsterdam 1066 CX, the Netherlands;4. AMLab, Amsterdam, 1098 XH, the Netherlands;5. Amsterdam UMC, Biomedical Engineering and Physics, University of Amsterdam, Amsterdam 1105 AZ, the Netherlands;1. Erasmus MC University Medical Center, Department of Radiology and Nuclear Medicine, Rotterdam, the Netherlands;2. Amsterdam UMC, Biomedical Engineering and Physics, University of Amsterdam, Amsterdam, the Netherlands;3. imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium;4. µ NEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium;5. Delft University of Technology, Delft, the Netherlands;1. Utah Center for Advanced Imaging Research (UCAIR), Department of Radiology and Imaging Sciences, University of Utah Salt Lake City, UT, USA;2. Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA;3. Department of Physics and Astronomy, University of Utah, Salt Lake City, UT, USA;4. Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA;5. Department of Cardiology, University of Utah, Salt Lake City, UT, USA;1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China;2. School of Microelectronics, Tianjin University, Tianjin 300072, China;3. School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China;1. Department of Radiology, Fujita Health University, School of Medicine, Japan;2. Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University, School of Medicine, Japan;3. Canon Medical Systems Corporation, Japan |
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| Abstract: | In Magnetic Resonance Imaging (MRI), the success of deep learning-based under-sampled MR image reconstruction depends on: (i) size of the training dataset, (ii) generalization capabilities of the trained neural network. Whenever there is a mismatch between the training and testing data, there is a need to retrain the neural network from scratch with thousands of MR images obtained using the same protocol. This may not be possible in MRI as it is costly and time consuming to acquire data. In this research, a transfer learning approach i.e. end-to-end fine tuning is proposed for U-Net to address the data scarcity and generalization problems of deep learning-based MR image reconstruction. First the generalization capabilities of a pre-trained U-Net (initially trained on the human brain images of 1.5 T scanner) are assessed for: (a) MR images acquired from MRI scanners of different magnetic field strengths, (b) MR images of different anatomies and (c) MR images under-sampled by different acceleration factors. Later, end-to-end fine tuning of the pre-trained U-Net is proposed for the reconstruction of the above-mentioned MR images (i.e. (a), (b) and (c)). The results show successful reconstructions obtained from the proposed method as reflected by the Structural SIMilarity index, Root Mean Square Error, Peak Signal-to-Noise Ratio and central line profile of the reconstructed images. |
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