Feature Fusion for Multi-Coil Compressed MR Image Reconstruction

Griswold M A, Jakob P M, Nittka M, et al. Partially parallel imaging with localized sensitivities (PILS). Magnetic Resonance in Medicine, 2000; 44(4): 602–9. https://doi.org/10.1002/1522-2594(200010)44:4<602::aid-mrm14>3.0.co;2-5.

Article  CAS  PubMed  Google Scholar 

Donoho D L. Compressed sensing. IEEE Transactions on information theory, 2006; 52(4): 1289–306. https://doi.org/10.1109/TIT.2006.871582.

Article  MathSciNet  Google Scholar 

Lustig M, Donoho D L, Santos J M, et al. Compressed sensing MRI. IEEE signal Processing Magazine, 2008; 25(2): 72–82. https://doi.org/10.1109/MSP.2007.914728.

Article  ADS  Google Scholar 

Sodickson DK, Manning WJ. Simultaneous acquisition of spatial harmonics (SMASH): fast imaging with radiofrequency coil arrays. Magnetic Resonance in Medicine, 1997; 38(4): 591–603. https://doi.org/10.1002/mrm.1910380414.

Article  CAS  PubMed  Google Scholar 

Pruessmann KP, Weiger M, Scheidegger MB, et al. SENSE: sensitivity encoding for fast MRI. Magnetic Resonance in Medicine, 1999; 42(5): 952–62.

Article  CAS  PubMed  Google Scholar 

Griswold M, Jakob P, Heidemann R, et al. Generalized autocalibrating partially parallel acquisitions (GRAPPA). Magnetic Resonance in Medicine, 2014; 71(3): 990–1001. https://doi.org/10.1002/mrm.10171.

Article  Google Scholar 

Uecker M, Lai P, Murphy MJ, et al. ESPIRiT-an eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA. Magnetic Resonance in Medicine, 2014; 71. https://doi.org/10.1002/mrm.24751.

Wang S, Su Z, Ying L, et al. Accelerating magnetic resonance imaging via deep learning. 2016 IEEE 13th international symposium on biomedical imaging (ISBI). IEEE, 2016: 514–517. https://doi.org/10.1109/ISBI.2016.7493320.

Article  Google Scholar 

Schlemper J, Caballero J, Hajnal Jv, et al. A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction [J]. IEEE Trans Med Imaging. 2018, 37(2):491–503. https://doi.org/10.1109/TMI.2017.2760978

Gan W, Sun Y, Eldeniz C, et al. Deep image reconstruction using unregistered measurements without groundtruth. 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI). IEEE, 2021: 1531–1534. https://doi.org/10.1109/ISBI48211.2021.9434079

Article  Google Scholar 

Duan C, Deng H, Xiao S, et al. Accelerate gas diffusion-weighted MRI for lung morphometry with deep learning. European Radiology. 2022, 32(1):702–713. https://doi.org/10.1007/s00330-021-08126-y.

Article  PubMed  Google Scholar 

Guo P, Valanarasu J M J, Wang P, et al. Over-and-under complete convolutional RNN for MRI reconstruction. Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part VI 24. Springer International Publishing, 2021: 13–23. https://arxiv.org/abs/2106.08886

Chen E Z, Wang P, Chen X, et al. Pyramid convolutional RNN for MRI image reconstruction. IEEE Transactions on Medical Imaging, 2022, 41(8): 2033–2047. https://doi.org/10.1109/TMI.2022.3153849.

Article  PubMed  Google Scholar 

Yang G, Yu S, Dong H, et al. DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction. IEEE Trans Med Imaging. 2018, 37(6):1310–1321. https://doi.org/10.1109/TMI.2017.2785879.

Belov A, Stadelmann J, Kastryulin S, et al. Towards ultrafast MRI via extreme k-space undersampling and superresolution. International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2021: 254–264. https://arxiv.org/abs/2103.02940

Li G, Lv J, Wang C. A modified generative adversarial network using spatial and channel-wise attention for CS-MRI reconstruction. IEEE Access, 2021, 9: 83185–83198. https://doi.org/10.1109/access.2021.3086839

Article  Google Scholar 

Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, 2015: 234–241. https://doi.org/10.1016/j.compbiomed.2021.104699

Hyun CM, Kim HP, Lee SM, et al. Deep learning for undersampled MRI reconstruction. Physics in Medicine & Biology. 2018, 63(13):135007. https://doi.org/10.1088/1361-6560/aac71a

Article  ADS  Google Scholar 

Yang J, Küstner T, Hu P, et al. End-to-End Deep Learning of Non-rigid Groupwise Registration and Reconstruction of Dynamic MRI. Frontiers in Cardiovascular Medicine, 2022, 9. https://doi.org/10.3389/fcvm.2022.880186.

Hammernik K, Klatzer T, Kobler E, et al. Learning a variational network for reconstruction of accelerated MRI data. Magnetic Resonance in Medicine, 2018; 79(6): 3055–71. https://doi.org/10.1002/mrm.26977.

Article  PubMed  Google Scholar 

Yiasemis G, Sonke J J, Sánchez C, et al. Recurrent variational network: a deep learning inverse problem Solver applied to the task of accelerated MRI reconstruction. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022: 732–41. https://arxiv.org/abs/2111.09639.

Zhang Z X, Du H W, Qiu B S. FFVN: An explicit feature fusion-based variational network for accelerated multi-coil MRI reconstruction. Magnetic Resonance Imaging, 2023, 97: 31–45. https://doi.org/10.1016/j.mri.2022.12.018.

Article  ADS  PubMed  Google Scholar 

Duan J, Schlemper J, Qin C, et al. VS-Net: Variable splitting network for accelerated parallel MRI reconstruction. Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part IV 22. Springer International Publishing, 2019: 713–722. https://arxiv.org/abs/1907.10033.

Murugesan B, Ramanarayanan S, Vijayarangan S, et al. A deep cascade of ensemble of dual domain networks with gradient-based T1 assistance and perceptual refinement for fast MRI reconstruction. Computerized Medical Imaging and Graphics, 2021, 91: 101942. https://doi.org/10.1016/j.compmedimag.2021.101942.

Article  PubMed  Google Scholar 

Küstner T, Fuin N, Hammernik K, et al. CINENet: deep learning-based 3D cardiac CINE MRI reconstruction with multi-coil complex-valued 4D spatio-temporal convolutions. Scientific reports, 2020, 10(1): 13710. https://doi.org/10.1038/s41598-020-70551-8.

Article  CAS  PubMed  PubMed Central  Google Scholar 

WANG Y D, SONG Y, XIE H B, et al. Reconstruction of under-sampled magnetic resonance image based on convolution neural network. Chin J Magn Reson Imaging, 2018, 9 (06): 453–459. https://doi.org/10.12015/issn.1674-8034.2018.06.010

Liu Y, Niu H, Ren P, et al. Generation of quantification maps and weighted images from synthetic magnetic resonance imaging using deep learning network. Physics in Medicine & Biology, 2022, 67(2): 025002. https://doi.org/10.1088/1361-6560/ac46dd

Article  ADS  Google Scholar 

Sriram A, Zbontar J, Murrell T, et al. End-to-end variational networks for accelerated MRI reconstruction. International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2020: 64–73. https://doi.org/10.1016/j.media.2017.06.012

Ramzi Z., Ciuciu P., and Starck J.-L., “XPDNet for MRI reconstruction: An application to the 2020 fastMRI challenge,” in Proc. ISMRM, 2020, pp. 1–4. Available: https://arxiv.org/abs/2010.07290

Ramzi Z, Chaithya G R, Starck J L, et al. NC-PDNet: A density-compensated unrolled network [[41(7): 1625–1638. https://doi.org/10.1109/tmi.2022.3144619

Sun L, Fan Z, Fu X, et al. A deep information sharing network for multi-contrast compressed sensing MRI reconstruction. IEEE Transactions on Image Processing, 2019, 28(12): 6141–6153. https://doi.org/10.1109/tip.2019.2925288

Article  ADS  MathSciNet  PubMed  Google Scholar 

Xiang L, Chen Y, Chang W, et al. Deep-learning-based multi-modal fusion for fast MR reconstruction. IEEE Transactions on Biomedical Engineering, 2018, 66(7): 2105–2114. https://doi.org/10.1109/tbme.2018.2883958

Article  Google Scholar 

Zbontar J, Knoll F, Sriram A, et al. fastMRI: An open dataset and benchmarks for accelerated MRI. https://arxiv.org/abs/1811.08839

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[J]. NMR in Biomedicine, 2022, 35(4): e4131. https://doi.org/10.1002/nbm.4131

Article  PubMed  Google Scholar 

Trabelsi C, Bilaniuk O, Serdyuk D, et al. Deep complex networks. 2018, https://arxiv.org/abs/1705.09792

留言 (0)

沒有登入
gif