Investigative Magnetic Resonance Imaging

  • 제24권4호
  • /
  • Pages.214-222
  • /
  • 2020
  • /
  • 2384-1095(pISSN)
  • /
  • 2384-1109(eISSN)
대한자기공명의과학회 (Korean Society of Magnetic Resonance in Medicine)
권호 표지
DOI QR코드

DOI QR Code

Cascaded Residual Dense Networks for Dynamic MR Imaging with Edge-Enhanced Loss Constraint

  • Ke, Ziwen (Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences) ;
  • Zhu, Yanjie (Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences) ;
  • Liang, Dong (Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences)
  • 투고 : 2020.06.04
  • 심사 : 2020.11.10
  • 발행 : 2020.12.31
⟨ 이전 논문 다음 논문 ⟩

초록

Dynamic magnetic resonance (MR) imaging has generated great research interest, because it can provide both spatial and temporal information for clinical diagnosis. However, slow imaging speed or long scanning time is still a challenge for dynamic MR imaging. Most existing methods reconstruct dynamic MR images from incomplete k-space data under the guidance of compressed sensing (CS) or low-rank theory, which suffer from long iterative reconstruction time. Recently, deep learning has shown great potential in accelerating dynamic MR. Our previous work proposed a dynamic MR imaging method with both k-space and spatial prior knowledge integrated via multi-supervised network training. Nevertheless, there was still some smoothing needed in the reconstructed images at high acceleration. In this work, we propose cascaded residual dense networks for dynamic MR imaging with edge-enhanced loss constraint, dubbed cascaded residual dense networks (CRDN). Specifically, the cascaded residual dense networks fully exploit the hierarchical features from all the convolutional layers with both local and global feature fusion. We further use the higher-degree total variation loss function, which has the edge enhancement properties, for training the networks.

키워드

참고문헌

  1. Kaiser WA, Zeitler E. MR imaging of the breast: fast imaging sequences with and without Gd-DTPA. Preliminary observations. Radiology 1989;170:681-686 https://doi.org/10.1148/radiology.170.3.2916021
  2. Sodickson DK, Manning WJ. Simultaneous acquisition of spatial harmonics (SMASH): fast imaging with radiofrequency coil arrays. Magn Reson Med 1997;38:591-603 https://doi.org/10.1002/mrm.1910380414
  3. Donoho DL. Compressed sensing. IEEE Trans Inf Theory 2006;52:1289-1306 https://doi.org/10.1109/TIT.2006.871582
  4. Lustig M, Donoho D, Pauly JM. Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn Reson Med 2007;58:1182-1195 https://doi.org/10.1002/mrm.21391
  5. Jung H, Ye JC, Kim EY. Improved k-t BLAST and k-t SENSE using FOCUSS. Phys Med Biol 2007;52:3201-3226 https://doi.org/10.1088/0031-9155/52/11/018
  6. Tsao J, Boesiger P, Pruessmann KP. k-t BLAST and k-t SENSE: dynamic MRI with high frame rate exploiting spatiotemporal correlations. Magn Reson Med 2003;50:1031-1042 https://doi.org/10.1002/mrm.10611
  7. Liang D, DiBella EV, Chen RR, Ying L. k-t ISD: dynamic cardiac MR imaging using compressed sensing with iterative support detection. Magn Reson Med 2012;68:41-53 https://doi.org/10.1002/mrm.23197
  8. Caballero J, Price AN, Rueckert D, Hajnal JV. Dictionary learning and time sparsity for dynamic MR data reconstruction. IEEE Trans Med Imaging 2014;33:979-994 https://doi.org/10.1109/TMI.2014.2301271
  9. Wang Y, Ying L. Compressed sensing dynamic cardiac cine MRI using learned spatiotemporal dictionary. IEEE Trans Biomed Eng 2014;61:1109-1120 https://doi.org/10.1109/TBME.2013.2294939
  10. Otazo R, Candes E, Sodickson DK. Low-rank plus sparse matrix decomposition for accelerated dynamic MRI with separation of background and dynamic components. Magn Reson Med 2015;73:1125-1136 https://doi.org/10.1002/mrm.25240
  11. Lingala SG, Hu Y, DiBella E, Jacob M. Accelerated dynamic MRI exploiting sparsity and low-rank structure: k-t SLR. IEEE Trans Med Imaging 2011;30:1042-1054 https://doi.org/10.1109/TMI.2010.2100850
  12. Yang Y, Sun J, Li H, Xu Z. Deep ADMM-net for compressive sensing MRI. Advances in Neural Information Processing Systems (NIPS), 2016
  13. Hammernik K, Klatzer T, Kobler E, et al. Learning a variational network for reconstruction of accelerated MRI data. Magn Reson Med 2018;79:3055-3071 https://doi.org/10.1002/mrm.26977
  14. Knoll F, Hammernik K, Kobler E, Pock T, Recht MP, Sodickson DK. Assessment of the generalization of learned image reconstruction and the potential for transfer learning. Magn Reson Med 2019;81:116-128 https://doi.org/10.1002/mrm.27355
  15. Wang S, Su Z, Ying L, et al. Accelerating magnetic resonance imaging via deep learning. Proc IEEE Int Symp Biomed Imaging 2016:514-517
  16. Kwon K, Kim D, Park H. A parallel MR imaging method using multilayer perceptron. Med Phys 2017;44:6209-6224 https://doi.org/10.1002/mp.12600
  17. Han Y, Yoo J, Kim HH, Shin HJ, Sung K, Ye JC. Deep learning with domain adaptation for accelerated projectionreconstruction MR. Magn Reson Med 2018;80:1189-1205 https://doi.org/10.1002/mrm.27106
  18. Zhu B, Liu JZ, Cauley SF, Rosen BR, Rosen MS. Image reconstruction by domain-transform manifold learning. Nature 2018;555:487-492 https://doi.org/10.1038/nature25988
  19. Eo T, Jun Y, Kim T, Jang J, Lee HJ, Hwang D. KIKI-net: crossdomain convolutional neural networks for reconstructing undersampled magnetic resonance images. Magn Reson Med 2018;80:2188-2201 https://doi.org/10.1002/mrm.27201
  20. Sun L, Fan Z, Huang Y, Ding X, Paisley J. Compressed sensing MRI using a recursive dilated network. AAAI 2018;2444-2451
  21. Quan TM, Nguyen-Duc T, Jeong WK. Compressed sensing MRI reconstruction using a generative adversarial network with a cyclic loss. IEEE Trans Med Imaging 2018;37:1488-1497 https://doi.org/10.1109/TMI.2018.2820120
  22. Schlemper J, Caballero J, Hajnal JV, Price AN, Rueckert D. A deep cascade of convolutional neural networks for dynamic MR image reconstruction. IEEE Trans Med Imaging 2018;37:491-503 https://doi.org/10.1109/tmi.2017.2760978
  23. Wang S, Ke Z, Cheng H, et al. DIMENSION: dynamic MR imaging with both k-space and spatial prior knowledge obtained via multi-supervised network training. NMR Biomed 2019;4:e4131
  24. Qin C, Schlemper J, Caballero J, Price AN, Hajnal JV, Rueckert D. Convolutional recurrent neural networks for dynamic MR image reconstruction. IEEE Trans Med Imaging 2019;38:280-290 https://doi.org/10.1109/TMI.2018.2863670
  25. Hu Y, Jacob M. Higher degree total variation (HDTV) regularization for image recovery. IEEE Trans Image Process 2012;21:2559-2571 https://doi.org/10.1109/TIP.2012.2183143
  26. Walsh DO, Gmitro AF, Marcellin MW. Adaptive reconstruction of phased array MR imagery. Magn Reson Med 2000;43:682-690 https://doi.org/10.1002/(SICI)1522-2594(200005)43:5<682::AID-MRM10>3.0.CO;2-G
  27. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. Computer Vision and Pattern Recognition (CVPR), IEEE 2016:770-778
  28. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521:436-444 https://doi.org/10.1038/nature14539
  29. He K, Zhang X, Ren S, Sun J. Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. International Conference on Computer Vision (ICCV), IEEE 2015:1026-1034
  30. Glorot X, Bordes A, Bengio Y. Deep sparse rectifier neural networks. AISTATS 2011;315-323
  31. Zeiler MD. ADADELTA: an adaptive learning rate method. arXiv preprint arXiv:1212.5701, 2012
  32. Kingma DP, Ba JL. Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014
  33. Abadi M, Barham P, Chen J, et al. Tensorflow: a system for large-scale machine learning. OSDI 2016;16:265-283
  34. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 2004;13:600-612 https://doi.org/10.1109/TIP.2003.819861