[1] Shikhaliev P M, Fritz S G. Photon counting spectral CT versus conventional CT: comparative evaluation for breast imaging application[J]. Physics in Medicine and Biology, 56, 1905-1930(2011).

[2] Withers P J, Bouman C, Carmignato S et al. X-ray computed tomography[J]. Nature Reviews Methods Primers, 1, 18(2021).

[3] McCollough C H, Boedeker K, Cody D et al. Principles and applications of multienergy CT: report of AAPM task group 291[J]. Medical Physics, 47, e881-e912(2020).

[4] Rajiah P, Parakh A, Kay F et al. Update on multienergy CT: physics, principles, and applications[J]. RadioGraphics, 40, 1284-1308(2020).

[5] Taguchi K, Ballabriga R, Campbell M et al. Photon counting detector computed tomography[J]. IEEE Transactions on Radiation and Plasma Medical Sciences, 6, 1-4(2022).

[6] Wang S K, Li X, Chen P. ADMM-SVNet: an ADMM-based sparse-view CT reconstruction network[J]. Photonics, 9, 186(2022).

[7] 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).

[8] Hansen D C, Sørensen T S, Rit S. Fast reconstruction of low dose proton CT by sinogram interpolation[J]. Physics in Medicine and Biology, 61, 5868-5882(2016).

[9] Zang G M, Aly M, Idoughi R, Ferrari V, Hebert M, Sminchisescu C et al. Super-resolution and sparse view CT reconstruction[M]. Computer vision-ECCV 2018. Lecture notes in computer science, 11220, 145-161(2018).

[10] Zhang Y, Wang Y, Zhang W H et al. Statistical iterative reconstruction using adaptive fractional order regularization[J]. Biomedical Optics Express, 7, 1015-1029(2016).

[11] Wu W W, Hu D L, Niu C et al. Deep learning based spectral CT imaging[J]. Neural Networks, 144, 342-358(2021).

[12] Di J L, Lin J C, Zhong L Y et al. Review of sparse-view or limited-angle CT reconstruction based on deep learning[J]. Laser & Optoelectronics Progress, 60, 0811002(2023).

[13] Shi Z F, Cheng M, Ouyang S X et al. Denoising method of low dose energy spectrum CT image based on DCPAN[J]. Journal of Tianjin University (Science and Technology), 56, 184-192(2023).

[14] Zhang L, Chen Q, Jiang B B et al. Preliminary study on motion artifacts removal of coronary CT angiography using generative adversarial network[J]. Journal of Shanghai Jiao Tong University (Medical Science), 40, 1230-1235, 1229(2020).

[15] Deng C Y, Lu J Y, Huang B T. Super-resolution reconstruction algorithm of CBCT image based on neural network learning[J]. Chinese Journal of Medical Physics, 37, 878-882(2020).

[16] Lü X J, Ren X Z, He P et al. Image denoising and ring artifacts removal for spectral CT via deep neural network[J]. IEEE Access, 8, 225594-225601(2020).

[17] 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).

[18] 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).

[19] 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

[20] Lee M, Kim H, Cho H M et al. Ultra-low-dose spectral CT based on a multi-level wavelet convolutional neural network[J]. Journal of Digital Imaging, 34, 1359-1375(2021).

[21] Zhang Y L, Li K P, Li K, Ferrari V, Hebert M, Sminchisescu C et al. Image super-resolution using very deep residual channel attention networks[M]. Computer vision-ECCV 2018. Lecture notes in computer science, 11211, 294-310(2018).

[22] Li G Q, Fang Q, Zha L L et al. HAM: hybrid attention module in deep convolutional neural networks for image classification[J]. Pattern Recognition, 129, 108785(2022).

[23] Liang T F, Jin Y, Li Y D et al. EDCNN: edge enhancement-based densely connected network with compound loss for low-dose CT denoising[C], 193-198(2021).

[24] Lehmann T M, Gonner C, Spitzer K. Addendum: B-spline interpolation in medical image processing[J]. IEEE Transactions on Medical Imaging, 20, 660-665(2001).

[25] Feldkamp L A, Davis L C, Kress J W. Practical cone-beam algorithm[J]. Journal of the Optical Society of America A, 1, 612-619(1984).

[26] Segars W P, Sturgeon G, Mendonca S et al. 4D XCAT phantom for multimodality imaging research[J]. Medical Physics, 37, 4902-4915(2010).

[27] Taguchi K, Stierstorfer K, Polster C et al. Spatio-energetic cross-talk in photon counting detectors: numerical detector model (PcTK) and workflow for CT image quality assessment[J]. Proceedings of SPIE, 10573, 1057310(2018).

[28] Zhao H, Gallo O, Frosio I et al. Loss functions for image restoration with neural networks[J]. IEEE Transactions on Computational Imaging, 3, 47-57(2017).