[1] Chen H, Zhang Y, Zhang W H et al. Low-dose CT via convolutional neural network[J]. Biomedical Optics Express, 8, 679-694(2017).

[2] Kang E, Min J H, Ye J C. A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction[J]. Medical Physics, 44, e360-e375(2017).

[3] Khodajou-Chokami H, Hosseini S A, Ay M R. A deep learning method for high-quality ultra-fast CT image reconstruction from sparsely sampled projections[J]. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 1029, 166428(2022).

[4] Ben Y H, Cardoen B, Hamarneh G. Deep learning for biomedical image reconstruction: A survey[J]. Artificial Intelligence Review, 54, 215-251(2021).

[5] Zhang H M, Dong B. A review on deep learning in medical image reconstruction[J]. Journal of the Operations Research Society of China, 8, 311-340(2020).

[6] Yang Y S, Liang H W, Yao Y et al. Fast reconstruction method of photon tomography under metal shielding condition[J]. Chinese Journal of Quantum Electronics, 39, 558-565(2022).

[7] Zhang Z H, Yang M H, Li H J et al. An innovative low-dose CT inpainting algorithm based on limited-angle imaging inpainting model[J]. Journal of X-Ray Science and Technology, 31, 131-152(2023).

[8] Du T Y, Gao K, Wu Z. Method for removing ring artifacts of absorption signal in grating-based phase contrast CT[J]. Chinese Journal of Quantum Electronics, 40, 40-47(2023).

[9] Vontobel P, Lehmann E, Carlson W D. Comparison of X-ray and neutron tomography investigations of geological materials[J]. IEEE Transactions on Nuclear Science, 52, 338-341(2005).

[10] Banhart J, Borbély A, Dzieciol K et al. X-ray and neutron imaging-Complementary techniques for materials science and engineering[J]. International Journal of Materials Research, 101, 1069-1079(2010).

[11] Soliman S R, Zayed H H, Selim M M et al. High quality reconstruction for neutron computerized tomography images[J]. Alexandria Engineering Journal, 60, 2041-2064(2021).

[12] Anderson I S, McGreevy R L, Bilheux H Z[M]. Neutron Imaging and Applications(2009).

[13] Pfeiffer F, Grünzweig C, Bunk O et al. Neutron phase imaging and tomography[J]. Physical Review Letters, 96, 215505(2006).

[14] Brown J M C, Garbe U, Pelliccia D. Statistical image reconstruction for high-throughput thermal neutron computed tomography[J]. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 942, 162396(2019).

[15] Kim H T, Tiana Razakamandimby R D F, Szilágyi V et al. Reconstruction of concrete microstructure using complementarity of X-ray and neutron tomography[J]. Cement and Concrete Research, 148, 106540(2021).

[16] Magnier L, Lecarme L, Alloin F et al. Tomography imaging of lithium electrodeposits using neutron, synchrotron X-ray, and laboratory X-ray sources: A comparison[J]. Frontiers in Energy Research, 9, 657712(2021).

[17] Venkatakrishnan S, Ziabari A, Hinkle J et al. Convolutional neural network based non-iterative reconstruction for accelerating neutron tomography[J]. Machine Learning: Science and Technology, 2, 025031(2021).

[18] Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 52, 1289-1306(2006).

[19] Yu H Y, Wang G. Compressed sensing based interior tomography[J]. Physics in Medicine and Biology, 54, 2791-2805(2009).

[20] Lee M J, Han Y, Ward J P et al. Interior tomography using 1D generalized total variation. part II: Multiscale implementation[J]. SIAM Journal on Imaging Sciences, 8, 2452-2486(2015).

[21] Ward J P, Lee M J, Ye J C et al. Interior tomography using 1D generalized total variation. part I: Mathematical foundation[J]. SIAM Journal on Imaging Sciences, 8, 226-247(2015).

[22] Jakubek J. Data processing and image reconstruction methods for pixel detectors[J]. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 576, 223-234(2007).

[23] Han Y, Wu D F, Kim K et al. End-to-end deep learning for interior tomography with low-dose X-ray CT[J]. Physics in Medicine & Biology, 67, 115001(2022).

[24] Zhang S, Salari E. Image denoising using a neural network based non-linear filter in wavelet domain[C](2005).

[25] Jain V, Seung H S. Natural image denoising with convolutional networks[C], 769-776(2008).

[26] Vincent P, Larochelle H, Lajoie I et al. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion[J]. Journal of Machine Learning Research, 11, 3371-3408(2010).

[27] Nasri M, Nezamabadi-pour H. Image denoising in the wavelet domain using a new adaptive thresholding function[J]. Neurocomputing, 72, 1012-1025(2009).

[28] Mao X J, Shen C H, Yang Y B. Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections[C], 2810-2818(2016).

[29] Wolterink J M, Leiner T, Viergever M A et al. Generative adversarial networks for noise reduction in low-dose CT[J]. IEEE Transactions on Medical Imaging, 36, 2536-2545(2017).

[30] Wang G. A perspective on deep imaging[J]. IEEE Access, 4, 8914-8924(2016).

[31] Pan S J, Yang Q. A survey on transfer learning[J]. IEEE Transactions on Knowledge and Data Engineering, 22, 1345-1359(2010).

[32] Fessler J A, Sonka M, Fitzpatrick J M. Statistical image reconstruction methods for transmission tomography[J]. Handbook of Medical Imaging, 2, 1-70(2000).

[33] Liu C H, Xiong D F, Dong Y et al. Optimization of temperature measurement of infrared radiation via theory of radiant heat transfer angle coefficient[J]. Chinese Journal of Quantum Electronics, 36, 490-494(2019).

[34] Liang K C, Yang H K, Xing Y X. Comparison of projection domain, image domain, and comprehensive deep learning for sparse-view X-ray CT image reconstruction[OL], 1804.04289(2018). http: //arxiv.org/abs/1804.04289.pdf

[35] Schillinger B, Grazzi F. Artefacts in neutron CT - their effects and how to reduce some of them[J]. Physics Procedia, 69, 244-251(2015).

[36] Ikeda Y, Yokoi M, Oda M et al. Correction of scattering neutron effects on neutron CT[J]. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 377, 85-89(1996).

[37] Ghifary M, Kleijn W B, Zhang M J. Domain adaptive neural networks for object recognition[C], 898-904(2014).

[38] Pan S J, Tsang I W, Kwok J T et al. Domain adaptation via transfer component analysis[J]. IEEE Transactions on Neural Networks, 22, 199-210(2011).

[39] Ghifary M, Kleijn W B, Zhang M J et al. Deep reconstruction-classification networks for unsupervised domain adaptation[C], 597-613(2016).

[40] Haris M, Shakhnarovich G, Ukita N. Deep back-projection networks for super-resolution[C], 1664-1673(2018).

[41] Kingma D P, Ba J. Adam: A method for stochastic optimization[OL], 1412.6980(2014). http: //arxiv.org/abs/1412.6980.pdf

[42] Calzada E, Gruenauer F, Mühlbauer M et al. New design for the ANTARES-II facility for neutron imaging at FRM II[J]. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 605, 50-53(2009).

[43] McCollough C H, Bartley A C, Carter R E et al. Low-dose CT for the detection and classification of metastatic liver lesions: Results of the 2016 Low Dose CT Grand Challenge[J]. Medical Physics, 44, e339-e352(2017).

[44] Jin K H, McCann M T, Froustey E et al. Deep convolutional neural network for inverse problems in imaging[J]. IEEE Transactions on Image Processing, 26, 4509-4522(2017).

[45] Chen H, Zhang Y, Kalra M K et al. Low-dose CT with a residual encoder-decoder convolutional neural network[J]. IEEE Transactions on Medical Imaging, 36, 2524-2535(2017).

[46] Zhang K, Zuo W M, Chen Y J et al. Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising[J]. IEEE Transactions on Image Processing, 26, 3142-3155(2017).