In order to generate high-quality single-photon emission computed tomography (SPECT) images under low-dose acquisition mode, a sinogram denoising method was studied for suppressing random oscillation and enhancing contrast in the projection domain. A conditional generative adversarial network with cross-domain regularization (CGAN-CDR) is proposed for low-dose SPECT sinogram restoration. The generator stepwise extracts multiscale sinusoidal features from a low-dose sinogram, which are then rebuilt into a restored sinogram. Long skip connections are introduced into the generator, so that the low-level features can be better shared and reused, and the spatial and angular sinogram information can be better recovered. A patch discriminator is employed to capture detailed sinusoidal features within sinogram patches; thereby, detailed features in local receptive fields can be effectively characterized. Meanwhile, a cross-domain regularization is developed in both the projection and image domains. Projection-domain regularization directly constrains the generator via penalizing the difference between generated and label sinograms. Image-domain regularization imposes a similarity constraint on the reconstructed images, which can ameliorate the issue of ill-posedness and serves as an indirect constraint on the generator. By adversarial learning, the CGAN-CDR model can achieve high-quality sinogram restoration. Finally, the preconditioned alternating projection algorithm with total variation regularization is adopted for image reconstruction. Extensive numerical experiments show that the proposed model exhibits good performance in low-dose sinogram restoration. From visual analysis, CGAN-CDR performs well in terms of noise and artifact suppression, contrast enhancement and structure preservation, particularly in low-contrast regions. From quantitative analysis, CGAN-CDR has obtained superior results in both global and local image quality metrics. From robustness analysis, CGAN-CDR can better recover the detailed bone structure of the reconstructed image for a higher-noise sinogram. This work demonstrates the feasibility and effectiveness of CGAN-CDR in low-dose SPECT sinogram restoration. CGAN-CDR can yield significant quality improvement in both projection and image domains, which enables potential applications of the proposed method in real low-dose study.
Citation: Si Li, Limei Peng, Fenghuan Li, Zengguo Liang. Low-dose sinogram restoration enabled by conditional GAN with cross-domain regularization in SPECT imaging[J]. Mathematical Biosciences and Engineering, 2023, 20(6): 9728-9758. doi: 10.3934/mbe.2023427
| [1] | Si Li, Wenquan Ye, Fenghuan Li . LU-Net: combining LSTM and U-Net for sinogram synthesis in sparse-view SPECT reconstruction. Mathematical Biosciences and Engineering, 2022, 19(4): 4320-4340. doi: 10.3934/mbe.2022200 |
| [2] | Benxin Zhang, Xiaolong Wang, Yi Li, Zhibin Zhu . A new difference of anisotropic and isotropic total variation regularization method for image restoration. Mathematical Biosciences and Engineering, 2023, 20(8): 14777-14792. doi: 10.3934/mbe.2023661 |
| [3] | Yafei Liu, Linqiang Yang, Hongmei Ma, Shuli Mei . Adaptive filter method in Bendlet domain for biological slice images. Mathematical Biosciences and Engineering, 2023, 20(6): 11116-11138. doi: 10.3934/mbe.2023492 |
| [4] | Hui Yao, Yuhan Wu, Shuo Liu, Yanhao Liu, Hua Xie . A pavement crack synthesis method based on conditional generative adversarial networks. Mathematical Biosciences and Engineering, 2024, 21(1): 903-923. doi: 10.3934/mbe.2024038 |
| [5] | Xiaolei Gu, Wei Xue, Yanhong Sun, Xuan Qi, Xiao Luo, Yongsheng He . Magnetic resonance image restoration via least absolute deviations measure with isotropic total variation constraint. Mathematical Biosciences and Engineering, 2023, 20(6): 10590-10609. doi: 10.3934/mbe.2023468 |
| [6] | Sanjaykumar Kinge, B. Sheela Rani, Mukul Sutaone . Restored texture segmentation using Markov random fields. Mathematical Biosciences and Engineering, 2023, 20(6): 10063-10089. doi: 10.3934/mbe.2023442 |
| [7] | Jiajia Jiao, Xiao Xiao, Zhiyu Li . dm-GAN: Distributed multi-latent code inversion enhanced GAN for fast and accurate breast X-ray image automatic generation. Mathematical Biosciences and Engineering, 2023, 20(11): 19485-19503. doi: 10.3934/mbe.2023863 |
| [8] | Shuaiyu Bu, Yuanyuan Li, Wenting Ren, Guoqiang Liu . ARU-DGAN: A dual generative adversarial network based on attention residual U-Net for magneto-acousto-electrical image denoising. Mathematical Biosciences and Engineering, 2023, 20(11): 19661-19685. doi: 10.3934/mbe.2023871 |
| [9] | Urszula Ledzewicz, Behrooz Amini, Heinz Schättler . Dynamics and control of a mathematical model for metronomic chemotherapy. Mathematical Biosciences and Engineering, 2015, 12(6): 1257-1275. doi: 10.3934/mbe.2015.12.1257 |
| [10] | Mingju Chen, Hongyang Li, Hongming Peng, Xingzhong Xiong, Ning Long . HPCDNet: Hybrid position coding and dual-frquency domain transform network for low-light image enhancement. Mathematical Biosciences and Engineering, 2024, 21(2): 1917-1937. doi: 10.3934/mbe.2024085 |
In order to generate high-quality single-photon emission computed tomography (SPECT) images under low-dose acquisition mode, a sinogram denoising method was studied for suppressing random oscillation and enhancing contrast in the projection domain. A conditional generative adversarial network with cross-domain regularization (CGAN-CDR) is proposed for low-dose SPECT sinogram restoration. The generator stepwise extracts multiscale sinusoidal features from a low-dose sinogram, which are then rebuilt into a restored sinogram. Long skip connections are introduced into the generator, so that the low-level features can be better shared and reused, and the spatial and angular sinogram information can be better recovered. A patch discriminator is employed to capture detailed sinusoidal features within sinogram patches; thereby, detailed features in local receptive fields can be effectively characterized. Meanwhile, a cross-domain regularization is developed in both the projection and image domains. Projection-domain regularization directly constrains the generator via penalizing the difference between generated and label sinograms. Image-domain regularization imposes a similarity constraint on the reconstructed images, which can ameliorate the issue of ill-posedness and serves as an indirect constraint on the generator. By adversarial learning, the CGAN-CDR model can achieve high-quality sinogram restoration. Finally, the preconditioned alternating projection algorithm with total variation regularization is adopted for image reconstruction. Extensive numerical experiments show that the proposed model exhibits good performance in low-dose sinogram restoration. From visual analysis, CGAN-CDR performs well in terms of noise and artifact suppression, contrast enhancement and structure preservation, particularly in low-contrast regions. From quantitative analysis, CGAN-CDR has obtained superior results in both global and local image quality metrics. From robustness analysis, CGAN-CDR can better recover the detailed bone structure of the reconstructed image for a higher-noise sinogram. This work demonstrates the feasibility and effectiveness of CGAN-CDR in low-dose SPECT sinogram restoration. CGAN-CDR can yield significant quality improvement in both projection and image domains, which enables potential applications of the proposed method in real low-dose study.
Environmental degradation and climate change exacerbate the challenges humanity faces. Coping with climate change, controlling environmental pollution and promoting harmonious coexistence between man and nature have become the core issues of sustainable development of the global economy and society. The Sustainable Development Goals and the Paris Agreement on Climate Change indicate a fundamental re-organization of both the financial system and the economy it serves and also imply an intensive study of the current economic and financial development model of every country. Hence, aligning finance with sustainability is subject to much research, experimentation and practice.
Green Finance (GF) is an international, interdisciplinary Open Access journal devoted to publishing peer-reviewed, high quality original papers in the field of green finance, environment, and sustainability research and practice. Green Finance interprets low-carbon topics from an economic perspective, and conducts quantitative research on green finance through carbon finance, carbon information disclosure and other aspects, so as to achieve win-win between economic value and social value. GF dedicates to providing speedy review to accelerate publication process. GF will focus primarily on original research articles, but will also publish reviews, editorials, letters, and conference reports.
Green Finance builds a much-needed platform for publishing original contributions and comprehensive technical review articles with a scope that spans all areas of green finance, green economics, and environmental and sustainable issues. A particular emphasis is placed on three aspects: Financial support for sustainable development, green financial products especially energy finance, and finance and R & D innovation.
Firstly, green finance research is an important means to promote the sustainable development of society, economy and ecology. Green finance is defined as comprising "all forms of investment or lending that consider environmental effect and enhance environmental sustainability" (Volz et al., 2015: 2). Sustainable investment and lending are significant aspects of green finance, considering sustainable standards, and environmental and climate risks. Besides, to align the financial system and policies with sustainable goals are also crucial for achieving a green transformation. Sustainability is without doubt a very important area of research, evidenced by its unprecedented growth in the last decade. In the field of academic research, sustainability-themed papers are becoming more and more prominent with rising awareness of challenges such as global climate change and resources shortage. With the development of the economy, the financial system and mechanism are continuously adjusted to mobilize and allocate financial resources reasonably and effectively and improve financial efficiency, so as to realize the effective operation and steady development of the economy and finance in the long run. The quantitative research of green finance can greatly promote the sustainable development of domestic and global economy and finance. The UNEP Inquiry (2015) identified five areas of emerging practice in embedding sustainable development into the financial system which are all worth studying in the near future: (ⅰ) enhancing market practice: Disclosure, analysis, risk management; (ⅱ) upgrading governance architectures: Internalizing sustainable development into financial decision-making of financial regulators and central bank; (ⅲ) encouraging cultural transformation: capacity building, behaviour, market structure; (ⅳ) harnessing the public balance sheets: Fiscal incentives, public financial institutions and central banks; (ⅴ) directing finance through policy: Requirements and prohibitions, enhanced liability.
Secondly, the GF tries to carry out a mission to identify, explore and promote the development and adoption of best environment-friendly and sustainable products and services at all levels of financial institution operations. Specifically, GF places unique emphasis on channeling finance to activities that promote energy efficiency. Nowadays, there are many energy efficiency financing opportunities in markets worth the attention of not only clients and institutions but also the researchers. A large number of studies focus on issues such as the relationship between energy price shocks and financial markets, financing and investment decisions made by energy firms, and carbon finance. These studies can be generalized into a common research theme—energy finance, which is also a subject of interest of this journal. The GF determines to publish innovative articles on the following five broad themes based on a brief review of the energy finance relevant literature: (ⅰ) Energy and financial markets; (ⅱ) Energy corporate finance; (ⅲ) Energy products pricing mechanism; (ⅳ) Energy derivative markets; (ⅴ) Energy risk management.
Thirdly, the GF also feels responsible to promote R & D and innovation finance. Nowadays, investments in R & D and innovation are at the heart of enterprises' competitiveness and on the top of countries' development strategies, so it is crucial and urgent to have a better understanding of the current and emerging drivers and barriers for R & D and innovation financing. Research on finance and R & D innovation has grown enormously in recent decades, producing a great number of results, and shedding new lights on many unresolved issues as well. It is well known that financial constraints and information asymmetries are important obstacles to R & D and innovation, but the great heterogeneity of companies calls for much more systematic and comprehensive empirical evidence based on proper indicators. The related issue of the gap between the external and internal costs of R & D investments also requires further research. What's more, the following topics are also in need of closer look and additional insights. (ⅰ) The dynamic relationship between financial behavior and enterprises' R & D innovation; (ⅱ) The magnitude and relative importance of the various external barriers to innovative activities; (ⅲ) Measurement of the squeezing effect of enterprise R & D innovation financing; (ⅳ) Accurately designed direct and indirect policy measures to ease access to investment in R & D innovation; (ⅴ) Comparative analyses on more countries or clusters of homogenous regions on R & D financing related issues.
The editors of GF hope that the journal will be educational, provocative and practically useful to all sustainable research professionals and institutions. The editorial team and AIMS press are determined to see the journal become a leading platform for the exchange of scientific communication in sustainability, green economics and finance, and related areas. With an outstanding Editorial Board, we are determined to make the journal among the best in the field. We look forward to receiving your submissions, general feedback as to how the journal is progressing and suggestions for how it can be improved.
We, on behalf of the Editorial Board, extend an invitation to sustainable research professionals for contributions and continued patronage to the journal of Green Finance.
| [1] | H. A. Ziessman, J. P. O'Malley, J. H. Thrall, Nuclear Medicine: The Requisites, Elsevier, 2014. |
| [2] |
P. Ritt, H. Vija, J. Hornegger, T. Kuwert, Absolute quantification in SPECT, Eur. J. Nucl. Med. Mol. Imaging, 38 (2011), 69–77. https://doi.org/10.1007/s00259-011-1770-8 doi: 10.1007/s00259-011-1770-8
|
| [3] |
X. Niu, Y. Yang, M. Jin, M. N. Wernick, M. A. King, Effects of motion, attenuation, and scatter corrections on gated cardiac SPECT reconstruction, Med. Phys., 38 (2011), 6571–6584. https://doi.org/10.1118/1.3660328 doi: 10.1118/1.3660328
|
| [4] |
R. G. Wells, Dose reduction is good but it is image quality that matters, J. Nucl. Cardiol., 1 (2018), 1–3. https://doi.org/10.1007/s12350-018-1378-5 doi: 10.1007/s12350-018-1378-5
|
| [5] |
J. Zhang, S. Li, A. Krol, C. R. Schmidtlein, E. Lipson, D. Feiglin, et al., Infimal convolution-based regularization for SPECT reconstruction, Med. Phys., 45 (2018), 5397–5410. https://doi.org/10.1002/mp.13226 doi: 10.1002/mp.13226
|
| [6] |
A. Krol, S. Li, L. Shen, Y. Xu, Preconditioned alternating projection algorithms for maximum a posteriori ECT reconstruction, Inverse Probl., 28 (2012), 115005. https://doi.org/10.1088/0266-5611/28/11/115005 doi: 10.1088/0266-5611/28/11/115005
|
| [7] |
Y. Luo, M. Wei, S. Li, J. Ling, G. Xie, S. Yao, An effective co-support guided analysis model for multi-contrast MRI reconstruction, IEEE J. Biomed. Health, (2023). https://doi.org/10.1109/JBHI.2023.3244669 doi: 10.1109/JBHI.2023.3244669
|
| [8] |
H. Zhang, B. Dong, A review on deep learning in medical image reconstruction, J. Oper. Res. Soc. China, 8 (2020), 311–340. https://doi.org/10.1007/s40305-019-00287-4 doi: 10.1007/s40305-019-00287-4
|
| [9] |
I. Häggström, C. R. Schmidtlein, G. Campanella, T. J. Fuchs, DeepPET: A deep encoder-decoder network for directly solving the PET reconstruction inverse problem, Med. Image Anal., 54 (2019), 253–262. https://doi.org/10.1016/j.media.2019.03.013 doi: 10.1016/j.media.2019.03.013
|
| [10] |
B. Zhu, J. Z. Liu, S. F. Cauley, B. R. Rosen, M. S. Rosen, Image reconstruction by domain-transform manifold learning, Nature, 555 (2018), 487–492. https://doi.org/10.1038/nature25988 doi: 10.1038/nature25988
|
| [11] | H. Zhang, B. Dong, B. Liu, JSR-Net: A deep network for Joint Spatial-Radon domain CT reconstruction from incomplete data, in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (2019), 3657–3661. https://doi.org/10.1109/ICASSP.2019.8682178 |
| [12] | Y. Yang, H. Li, Z. Xu, J. Sun, Deep ADMM-Net for compressive sensing MRI, Adv. Neural Inf. Process. Syst., 29 (2016), 10–18. |
| [13] |
Y. Yang, H. Li, Z. Xu, ADMM-CSNet: A deep learning approach for image compressive sensing, IEEE Trans. Pattern Anal. Mach. Intell., 42 (2018), 521–538. https://doi.org/10.1109/TPAMI.2018.2883941 doi: 10.1109/TPAMI.2018.2883941
|
| [14] |
J. Adler, O. Öktem, Learned primal-dual reconstruction, IEEE Trans. Med. Imaging, 37 (2018), 1322–1332. https://doi.org/10.1109/TMI.2018.2799231 doi: 10.1109/TMI.2018.2799231
|
| [15] |
B. Zhou, X. Chen, S. K. Zhou, J. S. Duncan, C. Liu. DuDoDR-Net: Dual-domain data consistent recurrent network for simultaneous sparse view and metal artifact reduction in computed tomography, Med. Image Anal., 75 (2022), 102289. https://doi.org/10.1016/j.media.2021.102289 doi: 10.1016/j.media.2021.102289
|
| [16] |
M. Li, W. Hsu, X. Xie, J. Cong, W. Gao. SACNN: Self-attention convolutional neural network for low-dose CT denoising with self-supervised perceptual loss network, IEEE Trans. Med. Imaging, 39 (2020), 2289–2301. https://doi.org/10.1109/TMI.2020.2968472 doi: 10.1109/TMI.2020.2968472
|
| [17] | W. Bae, J. J. Yoo, J. C. Ye, Beyond deep residual learning for image restoration: persistent homology-guided manifold simplification, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, (2017), 145–153. https://doi.org/10.1109/CVPRW.2017.152 |
| [18] |
H. Chen, Y. Zhang, M. K. Kalra, F. Lin, Y. Chen, P. Liao, et al., Low-dose CT with a residual encoder-decoder convolutional neural network, IEEE Trans. Med. Imaging, 36 (2017), 2524–2535. https://doi.org/10.1109/TMI.2017.2715284 doi: 10.1109/TMI.2017.2715284
|
| [19] |
Z. Zhang, X. Liang, X. Dong, Y. Xie, G. Cao, A sparse-view CT reconstruction method based on combination of DenseNet and deconvolution, IEEE Trans. Med. Imaging, 37 (2018), 1407–1417. https://doi.org/10.1109/TMI.2018.2823338 doi: 10.1109/TMI.2018.2823338
|
| [20] |
J. C. Ye, Y. Han, E. Cha, Deep convolutional framelets: A general deep learning framework for inverse problems, SIAM J. Imaging Sci., 11 (2018), 991–1048. https://doi.org/10.1137/17M1141771 doi: 10.1137/17M1141771
|
| [21] |
Y. Han, J. C. Ye, Framing U-Net via deep convolutional framelets: Application to sparse-view CT, IEEE Trans. Med. Imaging, 37 (2018), 1418–1429. https://doi.org/10.1109/TMI.2018.2823768 doi: 10.1109/TMI.2018.2823768
|
| [22] | C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Reed, et al., Going deeper with convolutions, in IEEE Conference on Computer Vision and Pattern Recognition, (2015), 1–9. https://doi.org/10.1109/CVPR.2015.7298594 |
| [23] |
S. Xie, X. Zheng, Y. Chen, L. Xie, J. liu, Y. Zhang, et al., Artifact removal using improved GoogLeNet for sparse-view CT reconstruction, Sci. Rep., 8 (2018), 1–9. https://doi.org/10.1038/s41598-018-25153-w doi: 10.1038/s41598-018-25153-w
|
| [24] |
H. Lee, J. Lee, H. Kim, B. Cho, S. Cho, Deep-neural-network-based sinogram synthesis for sparse-view CT image reconstruction, IEEE Trans. Radiat. Plasma Med. Sci., 3 (2018), 109–119. https://doi.org/10.1109/TRPMS.2018.2867611 doi: 10.1109/TRPMS.2018.2867611
|
| [25] |
B. Pan, N. Qi, Q. Meng, J. Wang, S. Peng, C. Qi, et al., Ultra high speed SPECT bone imaging enabled by a deep learning enhancement method: A proof of concept, EJNMMI Phys., 9 (2022), 1–15. https://doi.org/10.1186/s40658-022-00472-0 doi: 10.1186/s40658-022-00472-0
|
| [26] | H. Yuan, J. Jia, Z. Zhu, Sipid: A deep learning framework for sinogram interpolation and image denoising in low-dose CT reconstruction, in IEEE 15th International Symposium on Biomedical Imaging (ISBI), (2018), 1521–1524. https://doi.org/10.1109/ISBI.2018.8363862 |
| [27] | X. Dong, S. Vekhande, G. Cao, Sinogram interpolation for sparse-view micro-CT with deep learning neural network, in SPIE Medical Imaging 2019: Physics of Medical Imaging, (2019), 109482O. https://doi.org/10.1117/12.2512979 |
| [28] | C. Chrysostomou, L. Koutsantonis, C. Lemesios, C. N. Papanicolas, SPECT angle interpolation based on deep learning methodologies, in IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), (2020), 1–4. https://doi.org/10.1109/NSS/MIC42677.2020.9507966 |
| [29] | I. Shiri, P. Sheikhzadeh, M. R. Ay, Deep-fill: Deep learning based sinogram domain gap filling in positron emission tomography, preprint, arXiv: 1906.07168. https://doi.org/10.48550/arXiv.1906.07168 |
| [30] |
S. Li, W. Ye, F. Li, LU-Net: Combining LSTM and U-Net for sinogram synthesis in sparse-view SPECT reconstruction, Math. Biosci. Eng., 19 (2022), 4320–4340. https://doi.org/10.3934/mbe.2022200 doi: 10.3934/mbe.2022200
|
| [31] |
E. Xie, P. Ni, R. Zhang, X. Li. Limited-angle CT Reconstruction with generative adversarial network sinogram inpainting and unsupervised artifact removal, Appl. Sci., 12 (2022), 6268. https://doi.org/10.3390/app12126268 doi: 10.3390/app12126268
|
| [32] |
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde‐Farley, S. Ozair, et al., Generative adversarial nets, Commun. ACM, 63 (2020), 139–144. https://doi.org/10.1145/3422622 doi: 10.1145/3422622
|
| [33] |
C. Tang, W. Zhang, L. Wang, A. Cai, N. Liang, L. Li, et al., Generative adversarial network-based sinogram super-resolution for computed tomography imaging, Phys. Med. Biol., 65 (2020), 235006. https://doi.org/10.1088/1361-6560/abc12f doi: 10.1088/1361-6560/abc12f
|
| [34] | Z. Li, W. Zhang, L. Wang, A. Cai, N. Liang, B. Yan, et al., A sinogram inpainting method based on generative adversarial network for limited-angle computed tomography, in 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, (2019), 345–349. https://doi.org/10.1117/12.2533757 |
| [35] |
Y. Wang, W. Zhang, A. Cai, L. Wang, C. Tang, Z. Feng, et al., An effective sinogram inpainting for complementary limited-angle dual-energy computed tomography imaging using generative adversarial networks, J. X-Ray Sci. Technol., 29 (2021), 37–61. https://doi.org/10.3233/XST-200736 doi: 10.3233/XST-200736
|
| [36] | M. Mirza, S. Osindero, Conditional generative adversarial nets, preprint, arXiv: 1411.1784. https://doi.org/10.48550/arXiv.1411.1784 |
| [37] |
G. E. Hinton, R. R. Salakhutdinov, Reducing the dimensionality of data with neural networks, Science, 313 (2006), 504–507. https://doi.org/10.1126/science.1127647 doi: 10.1126/science.1127647
|
| [38] | P. Isola, J. Y. Zhu, T. Zhou, A. A. Efros, Image-to-image translation with conditional adversarial networks, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2017), 1125–1134. https://doi.org/10.1109/CVPR.2017.632 |
| [39] | K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, preprint, arXiv: 1409.1556. https://doi.org/10.48550/arXiv.1409.1556 |
| [40] | A. Odena, C. Odena, J. Odena, Conditional image synthesis with auxiliary classifier GANs, in The 34th International Conference on Machine Learning, 70 (2017), 2642–2651. |
| [41] |
C. A. Micchelli, L. Shen, Y. Xu, Proximity algorithms for image models: denoising, Inverse Probl., 27 (2011), 045009. https://doi.org/10.1088/0266-5611/27/4/045009 doi: 10.1088/0266-5611/27/4/045009
|
| [42] | M. Ljungberg, S. E. Strand, M. A. King, Monte Carlo Calculations in Nuclear Medicine: Applications In Diagnostic Imaging, CRC Press, 2012. https://doi.org/10.1201/b13073 |
| [43] |
M. Morphis, J. Staden, H. D. Raan, M. Ljungberg, Modelling of energy-dependent spectral resolution for SPECT Monte Carlo simulations using SIMIND, Heliyon, 7 (2021), e06097. https://doi.org/10.1016/j.heliyon.2021.e06097 doi: 10.1016/j.heliyon.2021.e06097
|
| [44] | S. Peltonen, U. Tuna, E. Sanchez-Monge, U. Ruotsalainen, PET sinogram denoising by block-matching and 3D filtering, in 2011 IEEE Nuclear Science Symposium Conference Record, (2011), 3125–3129. https://doi.org/10.1109/NSSMIC.2011.6152568 |
| 1. | Vitor Miguel Ribeiro, Pioneering paradigms: unraveling niche opportunities in green finance through bibliometric analysis of nation brands and brand culture, 2024, 6, 2643-1092, 287, 10.3934/GF.2024012 |
Figures(18) / Tables(4)
Si Li, Limei Peng, Fenghuan Li, Zengguo Liang. Low-dose sinogram restoration enabled by conditional GAN with cross-domain regularization in SPECT imaging[J]. Mathematical Biosciences and Engineering, 2023, 20(6): 9728-9758. doi: 10.3934/mbe.2023427
DownLoad: