[1] Brenner D J, Hall E J. Computed tomography: an increasing source of radiation exposure[J]. The New England Journal of Medicine, 357, 2277-2284(2007).

[2] Ciompi F, Chung K, van Riel S J et al. Towards automatic pulmonary nodule management in lung cancer screening with deep learning[J]. Scientific Reports, 7, 1-11(2017).

[3] Hall E J, Brenner D J. Cancer risks from diagnostic radiology[J]. The British Journal of Radiology, 81, 362-378(2008).

[4] Smith B D. Image reconstruction from cone-beam projections: necessary and sufficient conditions and reconstruction methods[J]. IEEE Transactions on Medical Imaging, 4, 14-25(1985).

[5] Shepp L A, Logan B F. The Fourier reconstruction of a head section[J]. IEEE Transactions on Nuclear Science, 21, 21-43(1974).

[6] Li Y S, Chen Y, Ma J H et al. Metal artifact reduction in CT based on adaptive steering filter and nonlocal sinogram inpainting[J]. Chinese Journal of Biomedical Engineering, 30, 377-381(2011).

[7] Yuan Y, Zhang Y B, Yu H Y. Adaptive nonlocal means method for denoising basis material images from dual-energy computed tomography[J]. Journal of Computer Assisted Tomography, 42, 972-981(2018).

[8] Geraldo R J, Cura L M V, Cruvinel P E et al. Low dose CT filtering in the image domain using MAP algorithms[J]. IEEE Transactions on Radiation and Plasma Medical Sciences, 1, 56-67(2017).

[9] Zhang H, Sonke J J. Directional sinogram interpolation for sparse angular acquisition in cone-beam computed tomography[J]. Journal of X-Ray Science and Technology, 21, 481-496(2013).

[10] Kim G, Park C, Lee D et al. Analytic computed tomography reconstruction in sparse-angular sampling using a sinogram-normalization interpolation method[J]. Journal of the Korean Physical Society, 73, 361-367(2018).

[11] Zeng G L. Sparse-view tomography via displacement function interpolation[J]. Visual Computing for Industry, Biomedicine, and Art, 2, 13(2019).

[12] Gordon R. A tutorial on art (algebraic reconstruction techniques)[J]. IEEE Transactions on Nuclear Science, 21, 78-93(1974).

[13] Rudin L I, Osher S. Total variation based image restoration with free local constraints[C], 31-35(2002).

[14] Buades A, Coll B, Morel J M. A review of image denoising algorithms, with a new one[J]. Multiscale Modeling & Simulation, 4, 490-530(2005).

[15] LaRoque S J, Sidky E Y, Pan X C. Accurate image reconstruction from few-view and limited-angle data in diffraction tomography[J]. Journal of the Optical Society of America. A, Optics, Image Science, and Vision, 25, 1772-1782(2008).

[16] Chen Z J, Qi H L, Jin Y et al. Sparse-view computed tomography reconstruction using an improved non-local means[J]. Journal of Medical Imaging and Health Informatics, 5, 1910-1914(2015).

[17] Altaf F, Islam S M S, Akhtar N et al. Going deep in medical image analysis: concepts, methods, challenges, and future directions[J]. IEEE Access, 7, 99540-99572(2019).

[18] Li K P. Research on CT reconstruction algorithm based on convolutional neural network[D](2022).

[19] Gonzalez R C, Woods R E[M]. Digital image processing. Ruan Q Q, Ruan Y Z, Transl(2020).

[20] Kak A C, Slaney M, Wang G. Principles of computerized tomographic imaging[J]. Medical Physics, 29, 107(2002).

[21] Kulathilake K A S H, Abdullah N A, Sabri A Q M et al. A review on deep learning approaches for low-dose computed tomography restoration[J]. Complex & Intelligent Systems, 1-33(2021).

[22] Chauhan R, Ghanshala K K, Joshi R C. Convolutional neural network (CNN) for image detection and recognition[C], 278-282(2019).

[23] Chen J W, Chen J W, Chao H Y et al. Image blind denoising with generative adversarial network based noise modeling[C], 3155-3164(2018).

[24] Ren S Q, He K M, Girshick R et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149(2017).

[25] LeCun Y, Bottou L, Bengio Y et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 86, 2278-2324(1998).

[26] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 60, 84-90(2017).

[27] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[EB/OL]. https://arxiv.org/abs/1409.1556

[28] Szegedy C, Liu W, Jia Y Q et al. Going deeper with convolutions[C](2015).

[29] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).

[30] Huang G, Liu Z, van der Maaten L et al. Densely connected convolutional networks[C], 2261-2269(2017).

[31] Hu J, Shen L, Albanie S et al. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 2011-2023(2020).

[32] Woo S, Park J, Lee J Y et al. CBAM: convolutional block attention module[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018, 11211, 3-19(2018).

[33] Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 640-651(2017).

[34] Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation[M]. Navab N, Hornegger J, Wells W M, et al. Medical image computing and computer-assisted intervention-MICCAI 2015, 9351, 234-241(2015).

[35] Goodfellow I, Pouget-Abadie J, Mirza M et al. Generative adversarial networks[J]. Communications of the ACM, 63, 139-144(2020).

[36] Kazeminia S, Baur C, Kuijper A et al. GANs for medical image analysis[J]. Artificial Intelligence in Medicine, 109, 101938(2020).

[37] Dosovitskiy A, Beyer L, Kolesnikov A et al. An image is worth 16×16 words: transformers for image recognition at scale[EB/OL]. https://arxiv.org/abs/2010.11929

[38] Ho J, Jain A, Abbeel P. Denoising diffusion probabilistic models[C], 6840-6851(2020).

[39] Frikel J, Quinto E T. Characterization and reduction of artifacts in limited angle tomography[J]. Inverse Problems, 29, 125007(2013).

[40] 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).

[41] Gu J, Ye J C. Multi-scale wavelet domain residual learning for limited-angle CT reconstruction[EB/OL]. https://arxiv.org/abs/1703.01382

[42] Han Y, Ye J C. Framing U-net via deep convolutional framelets: application to sparse-view CT[J]. IEEE Transactions on Medical Imaging, 37, 1418-1429(2018).

[43] Lee M, Kim H, Kim H J. Sparse-view CT reconstruction based on multi-level wavelet convolution neural network[J]. Physica Medica, 80, 352-362(2020).

[44] Mustafa W, Kehl C, Olsen U L et al. Sparse-view spectral CT reconstruction using deep learning[EB/OL]. https://arxiv.org/abs/2011.14842

[45] Zhang Y J, Qiao Z W. High-precision sparse reconstruction of CT images based on multiply residual UNet[J]. Journal of Computer Applications, 41, 2964-2969(2021).

[46] Wang J X, Liang J, Cheng J Y et al. Deep learning based image reconstruction algorithm for limited-angle translational computed tomography[J]. PLoS One, 15, e0226963(2020).

[47] Jiang Z R, Chen Y X, Zhang Y W et al. Augmentation of CBCT reconstructed from under-sampled projections using deep learning[J]. IEEE Transactions on Medical Imaging, 38, 2705-2715(2019).

[48] Liao H F, Huo Z M, Sehnert W J et al. Adversarial sparse-view CBCT artifact reduction[M]. Frangi A F, Schnabel J A, Davatzikos C, et al. Medical image computing and computer assisted intervention-MICCAI 2018, 11070, 154-162(2018).

[49] Huang J W, Xiao W P, Zhu S T et al. Application of adversarial training-based U-Net neural network in the enhancement of CT images obtained by sparse projection[J]. Chinese Journal of Medical Physics, 37, 612-618(2020).

[50] Du C C, Qiao Z W, Zhang Y J et al. Sparse-view CT reconstruction based on an adversarial multi-residual deep neural network[J]. Chinese Journal of Stereology and Image Analysis, 26, 145-154(2021).

[51] Isola P, Zhu J Y, Zhou T H et al. Image-to-image translation with conditional adversarial networks[C], 5967-5976(2017).

[52] Hegazy M A A, Cho M H, Lee S Y. Half-scan artifact correction using generative adversarial network for dental CT[J]. Computers in Biology and Medicine, 132, 104313(2021).

[53] Xie H D, Shan H M, Wang G. Deep encoder-decoder adversarial reconstruction(DEAR) network for 3D CT from few-view data[J]. Bioengineering, 6, 111(2019).

[54] Yang L, Zhang Z L, Song Y et al. Diffusion models: a comprehensive survey of methods and applications[EB/OL]. https://arxiv.org/abs/2209.00796

[55] Liu J M, Anirudh R, Thiagarajan J J et al. DOLCE: a model-based probabilistic diffusion framework for limited-angle CT reconstruction[EB/OL]. https://arxiv.org/abs/2211.12340

[56] Song Y, Shen L Y, Xing L et al. Solving inverse problems in medical imaging with score-based generative models[EB/OL]. https://arxiv.org/abs/2111.08005

[57] Zhang Z C, Liang X K, Dong X et al. A sparse-view CT reconstruction method based on combination of DenseNet and deconvolution[J]. IEEE Transactions on Medical Imaging, 37, 1407-1417(2018).

[58] Xie S P, Zheng X Y, Chen Y et al. Artifact removal using improved GoogLeNet for sparse-view CT reconstruction[J]. Scientific Reports, 8, 1-9(2018).

[59] Shen T C, Li X, Zhong Z S et al. R2-Net: recurrent and recursive network for sparse-view CT artifacts removal[M]. Shen D G, Liu T M, Peters T M, et al. Medical image computing and computer assisted intervention-MICCAI 2019, 11769, 319-327(2019).

[60] Zhou H C, Zhu Y N, Wang Q et al. Multi-scale dilated convolution neural network for image artifact correction of limited-angle tomography[J]. IEEE Access, 8, 1567-1576(2019).

[61] Shen T C, Yang Y B, Lin Z C et al. Recurrent learning with clique structures for prostate sparse-view CT artifacts reduction[J]. IET Image Processing, 15, 648-655(2021).

[62] Yang Y B, Zhong Z S, Shen T C et al. Convolutional neural networks with alternately updated clique[C], 2413-2422(2018).

[63] Qian Y F, Xie S P, Zhuang W Q et al. Sparse-view CT reconstruction based on improved re-sidual network[M]. Okada H, Atluri S N. Computational and experimental simulations in engineering, 75, 1069-1080(2020).

[64] Fu Z Y, Tseng H W, Vedantham S et al. A residual dense network assisted sparse view reconstruction for breast computed tomography[J]. Scientific Reports, 10, 1-16(2020).

[65] Xie S P, Yang T. Artifact removal in sparse-angle CT based on feature fusion residual network[J]. IEEE Transactions on Radiation and Plasma Medical Sciences, 5, 261-271(2021).

[66] Zhang F Q, Zhang M H, Qin B J et al. REDAEP: robust and enhanced denoising autoencoding prior for sparse-view CT reconstruction[J]. IEEE Transactions on Radiation and Plasma Medical Sciences, 5, 108-119(2021).

[67] Jiang Z R, Zhang Z Y, Chang Y S et al. Prior image-guided cone-beam computed tomography augmentation from under-sampled projections using a convolutional neural network[J]. Quantitative Imaging in Medicine and Surgery, 11, 4767-4780(2021).

[68] Han K, Wang Y H, Chen H T et al. A survey on vision transformer[EB/OL]. https://arxiv.org/abs/2012.12556

[69] Zhang Y K, Hu D L, Yan Z H et al. TIME-Net: transformer-integrated multi-encoder network for limited-angle artifact removal in dual-energy CBCT[J]. Medical Image Analysis, 83, 102650(2023).

[70] Lee H, Lee J, Cho S. View-interpolation of sparsely sampled sinogram using convolutional neural network[J]. Proceedings of SPIE, 10133, 1013328(2017).

[71] Lee H, Lee J, Kim H et al. Deep-neural-network-based sinogram synthesis for sparse-view CT image reconstruction[J]. IEEE Transactions on Radiation and Plasma Medical Sciences, 3, 109-119(2018).

[72] Cao G H, Vekhande S, Dong X. Sinogram interpolation for sparse-view micro-CT with deep learning neural network[J]. Proceedings of SPIE, 10948, 109482O(2019).

[73] Wen J, Qiao Z W. Sparse CT reconstruction with sinogram intelligent interpolation[J]. Nuclear Electronics & Detection Technology, 41, 1125-1131(2021).

[74] Dong J B, Fu J, He Z. A deep learning reconstruction framework for X-ray computed tomography with incomplete data[J]. PLoS One, 14, e0224426(2019).

[75] Fu J, Dong J B, Zhao F. A deep learning reconstruction framework for differential phase-contrast computed tomography with incomplete data[J]. IEEE Transactions on Image Processing, 29, 2190-2202(2020).

[76] Anirudh R, Kim H, Thiagarajan J J et al. Lose the views: limited angle CT reconstruction via implicit sinogram completion[C], 6343-6352(2018).

[77] Bai J N, Dai X B, Wu Q J et al. Limited-view CT reconstruction based on autoencoder-like generative adversarial networks with joint loss[J]. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2018, 5570-5574(2018).

[78] Dai X B, Bai J N, Liu T L et al. Limited-view cone-beam CT reconstruction based on an adversarial autoencoder network with joint loss[J]. IEEE Access, 7, 7104-7116(2018).

[79] Li Z H, Zhang W K, Wang L Y et al. A sinogram inpainting method based on generative adversarial network for limited-angle computed tomography[J]. Proceedings of SPIE, 11072, 1107220(2019).

[80] Li Z H, Cai A L, Wang L Y et al. Promising generative adversarial network based sinogram inpainting method for ultra-limited-angle computed tomography imaging[J]. Sensors, 19, 3941(2019).

[81] Mirza M, Osindero S. Conditional generative adversarial nets[EB/OL]. https://arxiv.org/abs/1411.1784

[82] Liu J C, Li J W. Sparse-sampling CT sinogram completion using generative adversarial networks[C], 640-644(2020).

[83] Ghani M U, Karl W C. Deep learning-based sinogram completion for low-dose CT[C](2018).

[84] Yuan H Z, Jia J Z, Zhu Z X. SIPID: a deep learning framework for sinogram interpolation and image denoising in low-dose CT reconstruction[C], 1521-1524(2018).

[85] Lee D, Choi S, Kim H J. High quality imaging from sparsely sampled computed tomography data with deep learning and wavelet transform in various domains[J]. Medical Physics, 46, 104-115(2019).

[86] 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[EB/OL]. https://arxiv.org/abs/1804.04289

[87] Zhou B, Chen X C, Zhou S K et al. DuDoDR-Net: dual-domain data consistent recurrent network for simultaneous sparse view and metal artifact reduction in computed tomography[J]. Medical Image Analysis, 75, 102289(2022).

[88] Wang W, Xia X G, He C J et al. An end-to-end deep network for reconstructing CT images directly from sparse sinograms[J]. IEEE Transactions on Computational Imaging, 6, 1548-1560(2020).

[89] Jiao F Y, Gui Z G, Li K P et al. A dual-domain CNN-based network for CT reconstruction[J]. IEEE Access, 9, 71091-71103(2021).

[90] Zhang Y K, Lü T L, Ge R J et al. CD-net: comprehensive domain network with spectral complementary for DECT sparse-view reconstruction[J]. IEEE Transactions on Computational Imaging, 7, 436-447(2021).

[91] Amirrashedi M, Sarkar S, Ghadiri H et al. A deep neural network to recover missing data in small animal pet imaging: comparison between sinogram- and image-domain implementations[C], 1365-1368(2021).

[92] Hu D L, Liu J, Lü T L et al. Hybrid-domain neural network processing for sparse-view CT reconstruction[J]. IEEE Transactions on Radiation and Plasma Medical Sciences, 5, 88-98(2021).

[93] Çiçek Ö, Abdulkadir A, Lienkamp S S et al. 3D U-Net: learning dense volumetric segmentation from sparse annotation[M]. Ourselin S, Joskowicz L, Sabuncu M R, et al. Medical image computing and computer-assisted intervention-MICCAI 2016, 9901, 424-432(2016).

[94] Han Y, Kang J G, Ye J C. Deep learning reconstruction for 9-view dual energy CT baggage scanner[EB/OL]. https://arxiv.org/abs/1801.01258

[95] Zheng A, Gao H W, Zhang L et al. A dual-domain deep learning-based reconstruction method for fully 3D sparse data helical CT[J]. Physics in Medicine and Biology, 65, 245030(2020).

[96] Zhao Z W, Sun Y W, Cong P. Sparse-view CT reconstruction via generative adversarial networks[C](2019).

[97] Ketola J H J, Heino H, Juntunen M A K et al. Generative adversarial networks improve interior computed tomography angiography reconstruction[J]. Biomedical Physics & Engineering Express, 7, 065041(2021).

[98] Xie E, Ni P J, Zhang R F et al. Limited-angle CT reconstruction with generative adversarial network sinogram inpainting and unsupervised artifact removal[J]. Applied Sciences, 12, 6268(2022).

[99] Wang C, Shang K, Zhang H M et al. DuDoTrans: dual-domain transformer provides more attention for sinogram restoration in sparse-view CT reconstruction[EB/OL]. https://arxiv.org/abs/2111.10790

[100] Shi C R, Xiao Y S, Chen Z Q. Dual-domain sparse-view CT reconstruction with Transformers[J]. Physica Medica, 101, 1-7(2022).

[101] Li R R, Li Q, Wang H X et al. DDPTransformer: dual-domain with parallel transformer network for sparse view CT image reconstruction[J]. IEEE Transactions on Computational Imaging, 8, 1101-1116(2022).

[102] Pan J Y, Zhang H Y, Wu W F et al. Multi-domain integrative Swin Transformer network for sparse-view tomographic reconstruction[J]. Patterns, 3, 100498(2022).

[103] Chen H, Zhang Y, Chen Y J et al. LEARN: learned experts’ assessment-based reconstruction network for sparse-data CT[J]. IEEE Transactions on Medical Imaging, 37, 1333-1347(2018).

[104] Zhang Y, Chen H, Xia W J et al. LEARN: recurrent dual-domain reconstruction network for compressed sensing CT[J]. IEEE Transactions on Radiation and Plasma Medical Sciences, 7, 132-142(2023).

[105] Hammernik K, Würfl T, Pock T et al. A deep learning architecture for limited-angle computed tomography reconstruction[M]. Bildverarbeitung für die Medizin 2017, 92-97(2017).

[106] Cheng W L, Wang Y, Li H W et al. Learned full-sampling reconstruction from incomplete data[J]. IEEE Transactions on Computational Imaging, 6, 945-957(2020).

[107] Wang J X, Zeng L, Wang C X et al. ADMM-based deep reconstruction for limited-angle CT[J]. Physics in Medicine and Biology, 64, 115011(2019).

[108] Xia W J, Yang Z Y, Zhou Q Z et al. A transformer-based iterative reconstruction model for sparse-view CT reconstruction[M]. Wang L W, Dou Q, Fletcher P T, et al. Medical image computing and computer assisted intervention-MICCAI 2022, 13436, 790-800(2022).

[109] Huang Y X, Preuhs A, Manhart M et al. Data extrapolation from learned prior images for truncation correction in computed tomography[J]. IEEE Transactions on Medical Imaging, 40, 3042-3053(2021).

[110] Zhang H M, Dong B, Liu B D. JSR-net: a deep network for joint spatial-radon domain CT reconstruction from incomplete data[C], 3657-3661(2019).

[111] Chun I Y, Huang Z Y, Lim H et al. Momentum-Net: fast and convergent iterative neural network for inverse problems[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32750839(2020).

[112] Xiang J X, Dong Y G, Yang Y J. FISTA-net: learning a fast iterative shrinkage thresholding network for inverse problems in imaging[J]. IEEE Transactions on Medical Imaging, 40, 1329-1339(2021).

[113] Bubba T A, Kutyniok G, Lassas M et al. Learning the invisible: a hybrid deep learning-shearlet framework for limited angle computed tomography[J]. Inverse Problems, 35, 064002(2019).

[114] Genzel M, Macdonald J, MäRZ M et al. Near-exact recovery for tomographic inverse problems via deep learning[C], 162, 7368-7381(2022).

[115] Zhang H M, Liu B D, Yu H Y et al. MetaInv-net: meta inversion network for sparse view CT image reconstruction[J]. IEEE Transactions on Medical Imaging, 40, 621-634(2021).

[116] Wu W W, Hu D L, Niu C et al. DRONE: dual-domain residual-based optimization network for sparse-view CT reconstruction[J]. IEEE Transactions on Medical Imaging, 40, 3002-3014(2021).

[117] Zhou B, Zhou S K, Duncan J S et al. Limited view tomographic reconstruction using a cascaded residual dense spatial-channel attention network with projection data fidelity layer[J]. IEEE Transactions on Medical Imaging, 40, 1792-1804(2021).

[118] Chen G Y, Huang Q. An efficient CT reconstruction algorithm based on the fused analytical iterative reconstruction network for sparse projections[J]. Chinese Journal of Stereology and Image Analysis, 26, 163-172(2021).

[119] Wu D F, Kim K, El Fakhri G et al. Iterative low-dose CT reconstruction with priors trained by artificial neural network[J]. IEEE Transactions on Medical Imaging, 36, 2479-2486(2017).

[120] Zhang M H, Zhang F Q, Liu Q G et al. Sparse-view CT reconstruction via robust and multi-channels autoencoding priors[C], 55-59(2018).

[121] Chun I Y, Fessler J A. Convolutional analysis operator learning: application to sparse-view CT (Invited Paper)[C], 1631-1635(2019).

[122] Zhu B, Liu J Z, Cauley S F et al. Image reconstruction by domain-transform manifold learning[J]. Nature, 555, 487-492(2018).

[123] Zhang Q Y, Liang D. Visualization of fully connected layer weights in deep learning CT reconstruction[EB/OL]. https://arxiv.org/abs/2002.06788

[124] Yim D, Kim B, Lee S. Limited-angle CT reconstruction via data-driven deep neural network[J]. Proceedings of SPIE, 11595, 115952V(2021).

[125] Yim D, Lee S, Nam K et al. Deep learning-based image reconstruction for few-view computed tomography[J]. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 1011, 165594(2021).

[126] Ma G W, Zhu Y N, Zhao X. Learning image from projection: a full-automatic reconstruction (FAR) net for computed tomography[J]. IEEE Access, 8, 219400-219414(2020).

[127] Kalare K W, Bajpai M K. RecDNN: deep neural network for image reconstruction from limited view projection data[J]. Soft Computing, 24, 17205-17220(2020).

[128] Mizusawa S, Sei Y, Orihara R et al. Computed tomography image reconstruction using stacked U-Net[J]. Computerized Medical Imaging and Graphics, 90, 101920(2021).

[129] Wurfl T, Hoffmann M, Christlein V et al. Deep learning computed tomography: learning projection-domain weights from image domain in limited angle problems[J]. IEEE Transactions on Medical Imaging, 37, 1454-1463(2018).

[130] He J, Wang Y B, Ma J H. Radon inversion via deep learning[J]. IEEE Transactions on Medical Imaging, 39, 2076-2087(2020).

[131] He J, Chen S L, Zhang H et al. Downsampled imaging geometric modeling for accurate CT reconstruction via deep learning[J]. IEEE Transactions on Medical Imaging, 40, 2976-2985(2021).

[132] Li Y S, Li K, Zhang C Z et al. Learning to reconstruct computed tomography images directly from sinogram data under a variety of data acquisition conditions[J]. IEEE Transactions on Medical Imaging, 38, 2469-2481(2019).

[133] Xie H D, Shan H M, Cong W X et al. Deep efficient end-to-end reconstruction (DEER) network for few-view breast CT image reconstruction[J]. IEEE Access, 8, 196633-196646(2020).

[134] Zhou B, Lin X Y, Eck B. Limited angle tomography reconstruction: synthetic reconstruction via unsupervised sinogram adaptation[M]. Chung A C S, Gee J C, Yushkevich P A, et al. Information processing in medical imaging, 11492, 141-152(2019).

[135] Liu X Q, Sajda P. Unsupervised sparse-view backprojection via convolutional and spatial transformer networks[EB/OL]. https://arxiv.org/abs/2006.01658

[136] Ye D H, Buzzard G T, Ruby M et al. Deep back projection for sparse-view CT reconstruction[C](2019).

[137] Zang G M, Idoughi R, Li R et al. IntraTomo: self-supervised learning-based tomography via sinogram synthesis and prediction[C], 1940-1950(2022).

[138] Kim B, Shim H, Baek J. A streak artifact reduction algorithm in sparse-view CT using a self-supervised neural representation[J]. Medical Physics, 49, 7497-7515(2022).

[139] Shu Z Y, Entezari A. Sparse-view and limited-angle CT reconstruction with untrained networks and deep image prior[J]. Computer Methods and Programs in Biomedicine, 226, 107167(2022).