Transfer learning enhanced generative adversarial networks for multi-channel MRI reconstruction

Computers in Biology and Medicine - Tập 134 - Trang 104504 - 2021
Jun Lv1, Guangyuan Li1, Xiangrong Tong1, Weibo Chen2, Jiahao Huang3, Chengyan Wang4, Guang Yang5,6
1School of Computer and Control Engineering, Yantai University, Yantai, China
2Philips Healthcare, Shanghai, China
3School of Optics and Photonics, Beijing Institute of Technology, Beijing, China
4Human Phenome Institute, Fudan University, Shanghai, China
5Cardiovascular Research Centre, Royal Brompton Hospital, London, SW3 6NP, UK
6National Heart and Lung Institute, Imperial College London, London SW7 2AZ, UK

Tài liệu tham khảo

Deshmane, 2012, Parallel MR imaging, J. Magn. Reson. Imag., 36, 55, 10.1002/jmri.23639 Candès, 2006, Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information, IEEE Trans. Inf. Theor., 52, 489, 10.1109/TIT.2005.862083 Pruessmann, 1999, SENSE: sensitivity encoding for fast MRI, Magn. Reson. Med., 42, 952, 10.1002/(SICI)1522-2594(199911)42:5<952::AID-MRM16>3.0.CO;2-S Griswold, 2002, Generalized autocalibrating partially parallel acquisitions (GRAPPA), Magn. Reson. Med., 47, 1202, 10.1002/mrm.10171 Lai, 2016, Image reconstruction of compressed sensing MRI using graph-based redundant wavelet transform, Med. Image Anal., 27, 93, 10.1016/j.media.2015.05.012 Tourbier, 2015, An efficient total variation algorithm for super-resolution in fetal brain MRI with adaptive regularization, Neuroimage, 118, 584, 10.1016/j.neuroimage.2015.06.018 Shin, 2014, Calibrationless parallel imaging reconstruction based on structured low‐rank matrix completion, Magn. Reson. Med., 72, 959, 10.1002/mrm.24997 Lustig, 2007, Sparse MRI: the application of compressed sensing for rapid MR imaging, Magn. Reson. Med., 58, 1182, 10.1002/mrm.21391 Ravishankar, 2010, MR image reconstruction from highly undersampled k-space data by dictionary learning, IEEE Trans. Med. Imag., 30, 1028, 10.1109/TMI.2010.2090538 Quan, 2018, Compressed sensing MRI reconstruction using a generative adversarial network with a cyclic loss, IEEE Trans. Med. Imag., 37, 1488, 10.1109/TMI.2018.2820120 Cole, 2021, Analysis of deep complex‐valued convolutional neural networks for MRI reconstruction and phase‐focused applications, Magn. Reson. Med., 86, 1093, 10.1002/mrm.28733 Wang, 2016, Accelerating magnetic resonance imaging via deep learning, 514 Sun, 2016, Deep ADMM-Net for compressive sensing MRI, Adv. Neural Inf. Process. Syst., 29, 10 Schlemper, 2017, A deep cascade of convolutional neural networks for dynamic MR image reconstruction, IEEE Trans. Med. Imag., 37, 491, 10.1109/TMI.2017.2760978 Yang, 2017, DAGAN: deep de-aliasing generative adversarial networks for fast compressed sensing MRI reconstruction, IEEE Trans. Med. Imag., 37, 1310, 10.1109/TMI.2017.2785879 Mardani, 2018, Deep generative adversarial neural networks for compressive sensing MRI, IEEE Trans. Med. Imag., 38, 167, 10.1109/TMI.2018.2858752 Shaul, 2020, Subsampled brain MRI reconstruction by generative adversarial neural networks, Med. Image Anal., 101747, 10.1016/j.media.2020.101747 Wu, 2019, Self-attention convolutional neural network for improved MR image reconstruction, Inf. Sci., 490, 317, 10.1016/j.ins.2019.03.080 Yuan, 2020, SARA-GAN: self-attention and relative average discriminator based generative adversarial networks for fast compressed sensing MRI reconstruction, Front. Neuroinf., 14, 611666, 10.3389/fninf.2020.611666 Hammernik, 2018, Learning a variational network for reconstruction of accelerated MRI data, Magn. Reson. Med., 79, 3055, 10.1002/mrm.26977 Aggarwal, 2018, MoDL: model-based deep learning architecture for inverse problems, IEEE Trans. Med. Imag., 38, 394, 10.1109/TMI.2018.2865356 Zhou, 2019, Parallel imaging and convolutional neural network combined fast MR image reconstruction: applications in low‐latency accelerated real‐time imaging, Med. Phys., 46, 3399, 10.1002/mp.13628 Wang, 2020, DeepcomplexMRI: exploiting deep residual network for fast parallel MR imaging with complex convolution, Magn. Reson. Imag., 68, 136, 10.1016/j.mri.2020.02.002 Liu, 2019, SANTIS: sampling‐augmented neural network with incoherent structure for MR image reconstruction, Magn. Reson. Med., 82, 1890, 10.1002/mrm.27827 Duan, 2019, Variable splitting network for accelerated parallel MRI reconstruction, 713 Lv, 2020, Parallel imaging with a combination of sensitivity encoding and generative adversarial networks, Quant. Imag. Med. Surg., 10, 2260, 10.21037/qims-20-518 Sriram, 2020, GrappaNet: combining parallel imaging with deep learning for multi-coil MRI reconstruction, 14315 Souza, 2020, Dual-domain cascade of U-nets for multi-channel magnetic resonance image reconstruction, Magn. Reson. Imag., 71, 140, 10.1016/j.mri.2020.06.002 Han, 2018, Deep learning with domain adaptation for accelerated projection‐reconstruction MR, Magn. Reson. Med., 80, 1189, 10.1002/mrm.27106 Knoll, 2019, Assessment of the generalization of learned image reconstruction and the potential for transfer learning, Magn. Reson. Med., 81, 116, 10.1002/mrm.27355 Dar, 2020, A transfer‐learning approach for accelerated MRI using deep neural networks, Magn. Reson. Med., 84, 663, 10.1002/mrm.28148 Arshad, 2020, Transfer learning in deep neural network based under-sampled MR image reconstruction, Magn. Reson. Imag., 76, 96, 10.1016/j.mri.2020.09.018 Lim, 2017, Enhanced deep residual networks for single image super-resolution, 136 Qu, 2020, Synthesized 7T MRI from 3T MRI via deep learning in spatial and wavelet domains, Med. Image Anal., 62, 101663, 10.1016/j.media.2020.101663 Kingma, 2015 Uecker, 2014, ESPIRiT—an eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA, Magn. Reson. Med., 71, 990, 10.1002/mrm.24751 Tamir, 2016 Subhas, 2020, Diagnostic interchangeability of deep convolutional neural networks reconstructed knee MR images: preliminary experience, Quant. Imag. Med. Surg., 10, 1748, 10.21037/qims-20-664 Zardavas, 2015, Clinical management of breast cancer heterogeneity, Nat. Rev. Clin. Oncol., 12, 381, 10.1038/nrclinonc.2015.73 Despotović, 2015, MRI segmentation of the human brain: challenges, methods, and applications, Computational and mathematical methods in medicine, 2015 Oh, 2020, Unsupervised learning for compressed sensing MRI using cycleGAN, 1082 Cole, 2020 Ke, 2020, An unsupervised deep learning method for multi-coil cine MRI, Phys. Med. Biol., 65, 235041, 10.1088/1361-6560/abaffa Sheng, 2019, Improved parallel MR imaging with accurate coil sensitivity estimation using iterative adaptive support, Biomed. Signal Process Contr., 51, 73, 10.1016/j.bspc.2019.02.001 Islam, 2021, Compressed sensing regularized calibrationless parallel magnetic resonance imaging via deep learning, Biomed. Signal Process Contr., 66, 102399, 10.1016/j.bspc.2020.102399