Opto-Electronic Engineering, Volume. 52, Issue 5, 250016(2025)
Integrating hierarchical semantic networks with physical models for MRI reconstruction
[1] Zhang Z J, Wang T Y, Xu X et al. Effect of polarized laser illumination on imaging contrast of multilayer thin film structure[J]. Opto-Electron Eng, 50, 230089(2023).
[2] Zhang Z J, Xu X, Wang J X et al. Review of the development of light sheet fluorescence microscopy[J]. Opto-Electron Eng, 50, 220045(2023).
[3] Abraham E, Zhou J X, Liu Z W. Speckle structured illumination endoscopy with enhanced resolution at wide field of view and depth of field[J]. Opto-Electron Adv, 6, 220163(2023).
[4] Yan F H. The clinical application and development prospect of deep learning MRI reconstruction algorithm[J]. Chin J Magn Reson Imaging, 14, 8-10(2023).
[5] Lustig M, Donoho D, Pauly J M. Sparse MRI: the application of compressed sensing for rapid MR imaging[J]. Magn Reson Med, 58, 1182-1195(2007).
[6] Liu J S, Qin C, Yaghoobi M. High-fidelity MRI reconstruction using adaptive spatial attention selection and deep data consistency prior[J]. IEEE Trans Comput Imaging, 9, 298-313(2023).
[7] Sandino C M, Cheng J Y, Chen F Y et al. Compressed sensing: from research to clinical practice with deep neural networks: shortening scan times for magnetic resonance imaging[J]. IEEE Signal Process Mag, 37, 117-127(2020).
[8] Goujon A, Neumayer S, Bohra P et al. A neural-network-based convex regularizer for inverse problems[J]. IEEE Trans Comput Imaging, 9, 781-795(2023).
[9] Hou R Z, Li F, Zhang G X. Truncated residual based plug-and-play ADMM algorithm for MRI reconstruction[J]. IEEE Trans Comput Imaging, 8, 96-108(2022).
[10] Xie J F, Zhang J, Zhang Y B et al. PUERT: probabilistic under-sampling and explicable reconstruction network for CS-MRI[J]. IEEE J Sel Top Signal Process, 16, 737-749(2022).
[11] Li P, Chen W G, Ng M K. Compressive total variation for image reconstruction and restoration[J]. Comput Math Appl, 80, 874-893(2020).
[12] Xu H H, Jiang J W, Feng Y C et al. Tensor completion via hybrid shallow-and-deep priors[J]. Appl Intell, 53, 17093-17114(2023).
[14] Zhang X M, Ma J W, Zhang H. Curvature-regularized manifold for seismic data interpolation[J]. Geophysics, 88, WA37-WA53(2023).
[15] Jiang J W, He Z H, Quan Y Q et al. PGIUN: physics-guided implicit unrolling network for accelerated MRI[J]. IEEE Trans Comput Imaging, 10, 1055-1068(2024).
[16] Pramanik A, Aggarwal H K, Jacob M. Deep generalization of structured low-rank algorithms (Deep-SLR)[J]. IEEE Trans Med Imaging, 39, 4186-4197(2020).
[17] Chen Y W, He Y, Ye H et al. Unified deep learning model for predicting fundus fluorescein angiography image from fundus structure image[J]. J Innov Opt Health Sci, 17, 2450003(2024).
[18] Geng C H, Jiang M F, Fang X et al. HFIST-Net: high-throughput fast iterative shrinkage thresholding network for accelerating MR image reconstruction[J]. Comput Methods Programs Biomed, 232, 107440(2023).
[19] Zhang J, Zhang Z Y, Xie J F et al. High-throughput deep unfolding network for compressive sensing MRI[J]. IEEE J Sel Top Signal Process, 16, 750-761(2022).
[20] Arabi H, Zeng G D, Zheng G Y et al. Novel adversarial semantic structure deep learning for MRI-guided attenuation correction in brain PET/MRI[J]. Eur J Nucl Med Mol Imaging, 46, 2746-2759(2019).
[21] Panić M, Aelterman J, Crnojević V et al. Sparse recovery in magnetic resonance imaging with a Markov random field prior[J]. IEEE Trans Med Imaging, 36, 2104-2115(2017).
[22] Ke Z W, Huang W Q, Cui Z X et al. Learned low-rank priors in dynamic MR imaging[J]. IEEE Trans Med Imaging, 40, 3698-3710(2021).
[24] Wang J, Zong Y, He Y et al. Domain adaptation-based automated detection of retinal diseases from optical coherence tomography images[J]. Curr Eye Res, 48, 836-842(2023).
[26] Lee D, Yoo J, Tak S et al. Deep residual learning for accelerated MRI using magnitude and phase networks[J]. IEEE Trans Biomed Eng, 65, 1985-1995(2018).
[27] Wang Z X, Wang Z W, Zhang Z Z et al. PFONet: a progressive focus-oriented dual-domain reconstruction network for accelerated MRI[J]. J Sichuan Univ (Nat Sci Ed), 61, 053004(2024).
[29] Aggarwal H K, Mani M P, Jacob M. MoDL: model-based deep learning architecture for inverse problems[J]. IEEE Trans Med Imaging, 38, 394-405(2019).
[30] Yang G, Zhang L, Zhou M et al. Model-guided multi-contrast deep unfolding network for MRI super-resolution reconstruction[C], 3974-3982(2022).
[31] Zhang X H, Lian Q S, Yang Y C et al. A deep unrolling network inspired by total variation for compressed sensing MRI[J]. Digital Signal Process, 107, 102856(2020).
[35] Xin B Y, Phan T, Axel L et al. Learned half-quadratic splitting network for MR image reconstruction[C], 1403-1412(2022).
[36] Beck A, Teboulle M. A fast iterative shrinkage-thresholding algorithm for linear inverse problems[J]. SIAM J Imaging Sci, 2, 183-202(2009).
[38] Li Y, Gupta A. Beyond grids: learning graph representations for visual recognition[C], 9245-9255(2018).
[41] Uecker M, Lai P, Murphy M J et al. ESPIRiT—an eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA[J]. Magn Reson Med, 71, 990-1001(2014).
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Xiaomin Zhang, Lingxin Bao. Integrating hierarchical semantic networks with physical models for MRI reconstruction[J]. Opto-Electronic Engineering, 2025, 52(5): 250016
Category: Article
Received: Jan. 20, 2025
Accepted: Mar. 12, 2025
Published Online: Jul. 18, 2025
The Author Email: Lingxin Bao (鲍玲鑫)