Pseudo-Computed Tomography generation from Noisy Magnetic Resonance Imaging with Deep Learning Algorithm
Abstract
Background: Magnetic Resonance Imaging (MRI) applications offer superior soft tissue contrast compared with computed tomography (CT) for accurate radiotherapy planning. Although, MRI images suffer from poor image quality and lack electron density for radiation dose calculation. The present study aims to use the deep learning (DL) approach to 1) enhance the quality of MRI images and 2) generate synthetic CT images using MRI images for more accurate radiotherapy planning.
Methods: In this paper, the pix2pix Generative Adversarial Network was utilized to synthesize CT images from noisy MRI images of 20 arbitrarily patients with brain disease. The standard statistical measurements investigated the accuracy comparison of the modeled Hounsfield unit (HU) value from MRI images and referenced CT of each patient. The famous quality metrics that were used to compare synthetic CTs and referenced CTs were the mean absolute error (MAE), the structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR(.
Results: The higher quality measurements between the synthetic pseudo-CT and the referenced CT images as PSNR, and SSIM, should correlate to the lower MAE value. For the overall brain among blind test data, the measured peak signal-to-noise ratio, mean absolute error, and structural similarity index values were about 16.5, 28.13, and 93.46, respectively.
Conclusion: The proposed method provides an acceptable level of statistical measurements computed on the Pseudo-CT and referenced CT, and it could be concluded that the p-CT can be implemented in radiotherapy treatment planning with acceptable accuracy.
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2. Yousefi Moteghaed N, Tabatabaeefar M, Mostaar AJJoBP, Engineering. Biomedical Image Denoising Based on Hybrid Optimization Algorithm and Sequential Filters. 2020;10(1):83.
3. Bharathi D, Govindan SM. A new hybrid approach for denoising medical images. Advances in Computing and Information Technology: Springer; 2013. p. 905-14.
4. Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol P-AJJomlr. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. 2010;11(Dec):3371-408.
5. Xie J, Xu L, Chen E, editors. Image denoising and inpainting with deep neural networks. Advances in neural information processing systems; 2012.
6. Zhang K, Zuo W, Zhang LJIToIP. FFDNet: Toward a fast and flexible solution for CNN-based image denoising. 2018;27(9):4608-22.
7. Zhang K, Zuo W, Zhang L, editors. Learning a single convolutional super-resolution network for multiple degradations. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2018.
8. Li H, Mueller K, editors. Low-dose ct streak artifacts removal using deep residual neural network. Proceedings of Fully 3D conference; 2017.
9. Tian C, Fei L, Zheng W, Xu Y, Zuo W, Lin C-WJapa. Deep Learning on Image Denoising: An overview. 2019.
10. Tian C, Xu Y, Fei L, Wang J, Wen J, Luo NJCToIT. Enhanced CNN for image denoising. 2019;4(1):17-23.
11. Lucas A, Iliadis M, Molina R, Katsaggelos AKJISPM. Using deep neural networks for inverse problems in imaging: beyond analytical methods. 2018;35(1):20-36.
12. Tian C FL, Zheng W, Xu Y, Zuo W, Lin CW. . Deep learning on image denoising: An overview. arXiv preprint arXiv:191213171. 2019.
13. Yang Q, Yan P, Zhang Y, Yu H, Shi Y, Mou X, et al. Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss. 2018;37(6):1348-57.
14. Kadimesetty VS, Gutta S, Ganapathy S, Yalavarthy PKJIToR, Sciences PM. Convolutional neural network-based robust denoising of low-dose computed tomography perfusion maps. 2018;3(2):137-52.
15. Abbasi A, Monadjemi A, Fang L, Rabbani H, Zhang YJCib, medicine. Three-dimensional optical coherence tomography image denoising through multi-input fully-convolutional networks. 2019;108:1-8.
16. Wang D, Zhou X, Xu Z, Cheng T, Wang X, Miao H, editors. Ameliorated Deep Learning based on Improved Denoising Autoencoder and GACNN. 2018 37th Chinese Control Conference (CCC); 2018: IEEE.
17. Demol B, Boydev C, Korhonen J, Reynaert N. Dosimetric characterization of MRI‐only treatment planning for brain tumors in atlas‐based pseudo‐CT images generated from standard T1‐weighted MR images. Medical physics. 2016;43(12):6557-68.
18. Degen J, Heinrich MP, editors. Multi-atlas based pseudo-ct synthesis using multimodal image registration and local atlas fusion strategies. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops; 2016.
19. Sjölund J, Forsberg D, Andersson M, Knutsson H. Generating patient specific pseudo-CT of the head from MR using atlas-based regression. Physics in Medicine & Biology. 2015;60(2):825.
20. Dowling J, Lambert J, Parker J, Greer PB, Fripp J, Denham J, et al., editors. Automatic MRI atlas-based external beam radiation therapy treatment planning for prostate cancer. International Workshop on Prostate Cancer Imaging; 2010: Springer.
21. Uh J, Merchant TE, Li Y, Li X, Hua C. MRI‐based treatment planning with pseudo CT generated through atlas registration. Medical physics. 2014;41(5):051711.
22. Guerreiro F, Burgos N, Dunlop A, Wong K, Petkar I, Nutting C, et al. Evaluation of a multi-atlas CT synthesis approach for MRI-only radiotherapy treatment planning. Physica Medica. 2017;35:7-17.
23. Morris ED, Price RG, Kim J, Schultz L, Siddiqui MS, Chetty I, et al. Using synthetic CT for partial brain radiation therapy: Impact on image guidance. Practical radiation oncology. 2018;8(5):342-50.
24. Hu Y, Zhao W, Du D, Wooten HO, Olsen JR, Gay HA, et al. Magnetic resonance imaging-based treatment planning for prostate cancer: Use of population average tissue densities within the irradiated volume to improve plan accuracy. Practical radiation oncology. 2015;5(4):248-56.
25. Largent A, Barateau A, Nunes J-C, Mylona E, Castelli J, Lafond C, et al. Comparison of deep learning-based and patch-based methods for pseudo-CT generation in MRI-based prostate dose planning. International Journal of Radiation Oncology* Biology* Physics. 2019;105(5):1137-50.
26. Moteghaed NY, Mostaar A, Maghooli K, Houshyari M, Ameri A. Estimation and evaluation of pseudo-CT images using linear regression models and texture feature extraction from MRI images in the brain region to design external radiotherapy planning. Reports of Practical Oncology and Radiotherapy. 2020;25(5):738-45.
27. Yousefi Moteghaed N, Yaghobi Joybari A, Mostaar A. Pseudo-CT Generation for External Radiotherapy Planning from Magnetic Resonance Imaging Data on Brain Regions. Iranian Journal of Radiology. 2019;16(3).
28. Yousefi Moteghaed N, Mostaar A, Azadeh P. Generating pseudo‐computerized tomography (P‐CT) scan images from magnetic resonance imaging (MRI) images using machine learning algorithms based on fuzzy theory for radiotherapy treatment planning. Medical Physics. 2021;48(11):7016-27.
29. Largent A, Barateau A, Nunes J, Lafond C, Greer P, Dowling J, et al. A comparison of pseudo-CT generation methods for prostate MRI-based dose planning: deep learning, patch-based, atlas-based and bulk-density methods. Physica Medica: European Journal of Medical Physics. 2019;68:28.
30. Spadea MF, Pileggi G, Zaffino P, Salome P, Catana C, Izquierdo-Garcia D, et al. Deep convolution neural network (DCNN) multiplane approach to synthetic CT generation from MR images—application in brain proton therapy. International Journal of Radiation Oncology* Biology* Physics. 2019;105(3):495-503.
31. Dinkla AM, Wolterink JM, Maspero M, Savenije MH, Verhoeff JJ, Seravalli E, et al. MR-only brain radiation therapy: dosimetric evaluation of synthetic CTs generated by a dilated convolutional neural network. International Journal of Radiation Oncology* Biology* Physics. 2018;102(4):801-12.
32. Chen S, Qin A, Zhou D, Yan D. U‐net‐generated synthetic CT images for magnetic resonance imaging‐only prostate intensity‐modulated radiation therapy treatment planning. Medical physics. 2018;45(12):5659-65.
33. Gupta D, Kim M, Vineberg KA, Balter JM. Generation of synthetic CT images from MRI for treatment planning and patient positioning using a 3-channel U-net trained on sagittal images. Frontiers in oncology. 2019;9:964.
34. Wang Y, Liu C, Zhang X, Deng W. Synthetic CT generation based on T2 weighted MRI of nasopharyngeal carcinoma (NPC) using a deep convolutional neural network (DCNN). Frontiers in oncology. 2019;9:1333.
35. Li Y, Zhu J, Liu Z, Teng J, Xie Q, Zhang L, et al. A preliminary study of using a deep convolution neural network to generate synthesized CT images based on CBCT for adaptive radiotherapy of nasopharyngeal carcinoma. Physics in Medicine & Biology. 2019;64(14):145010.
36. Nie D, Trullo R, Lian J, Petitjean C, Ruan S, Wang Q, et al., editors. Medical image synthesis with context-aware generative adversarial networks. International conference on medical image computing and computer-assisted intervention; 2017: Springer.
37. Shokraei Fard A, Reutens DC, Vegh V. CNNs and GANs in MRI-based cross-modality medical image estimation. arXiv e-prints. 2021:arXiv: 2106.02198.
38. Bourbonne V, Jaouen V, Hognon C, Boussion N, Lucia F, Pradier O, et al. Dosimetric Validation of a GAN-Based Pseudo-CT Generation for MRI-Only Stereotactic Brain Radiotherapy. Cancers 2021, 13, 1082. s Note: MDPI stays neutral with regard to jurisdictional claims in published …; 2021.
39. Kazemifar S, Barragán Montero AM, Souris K, Rivas ST, Timmerman R, Park YK, et al. Dosimetric evaluation of synthetic CT generated with GANs for MRI‐only proton therapy treatment planning of brain tumors. Journal of applied clinical medical physics. 2020;21(5):76-86.
40. Tie X, Lam SK, Zhang Y, Lee KH, Au KH, Cai J. Pseudo‐CT generation from multi‐parametric MRI using a novel multi‐channel multi‐path conditional generative adversarial network for nasopharyngeal carcinoma patients. Medical physics. 2020;47(4):1750-62.
41. Zhang Y, Yue N, Su MY, Liu B, Ding Y, Zhou Y, et al. Improving CBCT quality to CT level using deep learning with generative adversarial network. Medical physics. 2021;48(6):2816-26.
42. Xu K, Cao J, Xia K, Yang H, Zhu J, Wu C, et al. Multichannel residual conditional GAN-leveraged abdominal pseudo-CT generation via dixon MR images. IEEE Access. 2019;7:163823-30.
43. Yang H, Sun J, Carass A, Zhao C, Lee J, Xu Z, et al. Unpaired brain mr-to-ct synthesis using a structure-constrained cyclegan. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: Springer; 2018. p. 174-82.
44. Lei Y, Harms J, Wang T, Liu Y, Shu HK, Jani AB, et al. MRI‐only based synthetic CT generation using dense cycle consistent generative adversarial networks. Medical physics. 2019;46(8):3565-81.
45. Liu Y, Lei Y, Wang T, Zhou J, Lin L, Liu T, et al., editors. Liver synthetic CT generation based on a dense-CycleGAN for MRI-only treatment planning. Medical Imaging 2020: Image Processing; 2020: International Society for Optics and Photonics.
46. Sun H, Fan R, Li C, Lu Z, Xie K, Ni X, et al. Imaging Study of Pseudo-CT Synthesized From Cone-Beam CT Based on 3D CycleGAN in Radiotherapy. Frontiers in Oncology. 2021;11:436.
47. Liang X, Chen L, Nguyen D, Zhou Z, Gu X, Yang M, et al. Generating synthesized computed tomography (CT) from cone-beam computed tomography (CBCT) using CycleGAN for adaptive radiation therapy. Physics in Medicine & Biology. 2019;64(12):125002.
48. Li W, Kazemifar S, Bai T, Nguyen D, Weng Y, Li Y, et al. Synthesizing CT images from MR images with deep learning: model generalization for different datasets through transfer learning. Biomedical Physics & Engineering Express. 2021;7(2):025020.
49. Abdelmotaal H, Abdou AA, Omar AF, El-Sebaity DM, Abdelazeem K. Pix2pix Conditional Generative Adversarial Networks for Scheimpflug Camera Color-Coded Corneal Tomography Image Generation. Translational Vision Science & Technology. 2021;10(7):21-.
2. Yousefi Moteghaed N, Tabatabaeefar M, Mostaar AJJoBP, Engineering. Biomedical Image Denoising Based on Hybrid Optimization Algorithm and Sequential Filters. 2020;10(1):83.
3. Bharathi D, Govindan SM. A new hybrid approach for denoising medical images. Advances in Computing and Information Technology: Springer; 2013. p. 905-14.
4. Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol P-AJJomlr. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. 2010;11(Dec):3371-408.
5. Xie J, Xu L, Chen E, editors. Image denoising and inpainting with deep neural networks. Advances in neural information processing systems; 2012.
6. Zhang K, Zuo W, Zhang LJIToIP. FFDNet: Toward a fast and flexible solution for CNN-based image denoising. 2018;27(9):4608-22.
7. Zhang K, Zuo W, Zhang L, editors. Learning a single convolutional super-resolution network for multiple degradations. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2018.
8. Li H, Mueller K, editors. Low-dose ct streak artifacts removal using deep residual neural network. Proceedings of Fully 3D conference; 2017.
9. Tian C, Fei L, Zheng W, Xu Y, Zuo W, Lin C-WJapa. Deep Learning on Image Denoising: An overview. 2019.
10. Tian C, Xu Y, Fei L, Wang J, Wen J, Luo NJCToIT. Enhanced CNN for image denoising. 2019;4(1):17-23.
11. Lucas A, Iliadis M, Molina R, Katsaggelos AKJISPM. Using deep neural networks for inverse problems in imaging: beyond analytical methods. 2018;35(1):20-36.
12. Tian C FL, Zheng W, Xu Y, Zuo W, Lin CW. . Deep learning on image denoising: An overview. arXiv preprint arXiv:191213171. 2019.
13. Yang Q, Yan P, Zhang Y, Yu H, Shi Y, Mou X, et al. Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss. 2018;37(6):1348-57.
14. Kadimesetty VS, Gutta S, Ganapathy S, Yalavarthy PKJIToR, Sciences PM. Convolutional neural network-based robust denoising of low-dose computed tomography perfusion maps. 2018;3(2):137-52.
15. Abbasi A, Monadjemi A, Fang L, Rabbani H, Zhang YJCib, medicine. Three-dimensional optical coherence tomography image denoising through multi-input fully-convolutional networks. 2019;108:1-8.
16. Wang D, Zhou X, Xu Z, Cheng T, Wang X, Miao H, editors. Ameliorated Deep Learning based on Improved Denoising Autoencoder and GACNN. 2018 37th Chinese Control Conference (CCC); 2018: IEEE.
17. Demol B, Boydev C, Korhonen J, Reynaert N. Dosimetric characterization of MRI‐only treatment planning for brain tumors in atlas‐based pseudo‐CT images generated from standard T1‐weighted MR images. Medical physics. 2016;43(12):6557-68.
18. Degen J, Heinrich MP, editors. Multi-atlas based pseudo-ct synthesis using multimodal image registration and local atlas fusion strategies. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops; 2016.
19. Sjölund J, Forsberg D, Andersson M, Knutsson H. Generating patient specific pseudo-CT of the head from MR using atlas-based regression. Physics in Medicine & Biology. 2015;60(2):825.
20. Dowling J, Lambert J, Parker J, Greer PB, Fripp J, Denham J, et al., editors. Automatic MRI atlas-based external beam radiation therapy treatment planning for prostate cancer. International Workshop on Prostate Cancer Imaging; 2010: Springer.
21. Uh J, Merchant TE, Li Y, Li X, Hua C. MRI‐based treatment planning with pseudo CT generated through atlas registration. Medical physics. 2014;41(5):051711.
22. Guerreiro F, Burgos N, Dunlop A, Wong K, Petkar I, Nutting C, et al. Evaluation of a multi-atlas CT synthesis approach for MRI-only radiotherapy treatment planning. Physica Medica. 2017;35:7-17.
23. Morris ED, Price RG, Kim J, Schultz L, Siddiqui MS, Chetty I, et al. Using synthetic CT for partial brain radiation therapy: Impact on image guidance. Practical radiation oncology. 2018;8(5):342-50.
24. Hu Y, Zhao W, Du D, Wooten HO, Olsen JR, Gay HA, et al. Magnetic resonance imaging-based treatment planning for prostate cancer: Use of population average tissue densities within the irradiated volume to improve plan accuracy. Practical radiation oncology. 2015;5(4):248-56.
25. Largent A, Barateau A, Nunes J-C, Mylona E, Castelli J, Lafond C, et al. Comparison of deep learning-based and patch-based methods for pseudo-CT generation in MRI-based prostate dose planning. International Journal of Radiation Oncology* Biology* Physics. 2019;105(5):1137-50.
26. Moteghaed NY, Mostaar A, Maghooli K, Houshyari M, Ameri A. Estimation and evaluation of pseudo-CT images using linear regression models and texture feature extraction from MRI images in the brain region to design external radiotherapy planning. Reports of Practical Oncology and Radiotherapy. 2020;25(5):738-45.
27. Yousefi Moteghaed N, Yaghobi Joybari A, Mostaar A. Pseudo-CT Generation for External Radiotherapy Planning from Magnetic Resonance Imaging Data on Brain Regions. Iranian Journal of Radiology. 2019;16(3).
28. Yousefi Moteghaed N, Mostaar A, Azadeh P. Generating pseudo‐computerized tomography (P‐CT) scan images from magnetic resonance imaging (MRI) images using machine learning algorithms based on fuzzy theory for radiotherapy treatment planning. Medical Physics. 2021;48(11):7016-27.
29. Largent A, Barateau A, Nunes J, Lafond C, Greer P, Dowling J, et al. A comparison of pseudo-CT generation methods for prostate MRI-based dose planning: deep learning, patch-based, atlas-based and bulk-density methods. Physica Medica: European Journal of Medical Physics. 2019;68:28.
30. Spadea MF, Pileggi G, Zaffino P, Salome P, Catana C, Izquierdo-Garcia D, et al. Deep convolution neural network (DCNN) multiplane approach to synthetic CT generation from MR images—application in brain proton therapy. International Journal of Radiation Oncology* Biology* Physics. 2019;105(3):495-503.
31. Dinkla AM, Wolterink JM, Maspero M, Savenije MH, Verhoeff JJ, Seravalli E, et al. MR-only brain radiation therapy: dosimetric evaluation of synthetic CTs generated by a dilated convolutional neural network. International Journal of Radiation Oncology* Biology* Physics. 2018;102(4):801-12.
32. Chen S, Qin A, Zhou D, Yan D. U‐net‐generated synthetic CT images for magnetic resonance imaging‐only prostate intensity‐modulated radiation therapy treatment planning. Medical physics. 2018;45(12):5659-65.
33. Gupta D, Kim M, Vineberg KA, Balter JM. Generation of synthetic CT images from MRI for treatment planning and patient positioning using a 3-channel U-net trained on sagittal images. Frontiers in oncology. 2019;9:964.
34. Wang Y, Liu C, Zhang X, Deng W. Synthetic CT generation based on T2 weighted MRI of nasopharyngeal carcinoma (NPC) using a deep convolutional neural network (DCNN). Frontiers in oncology. 2019;9:1333.
35. Li Y, Zhu J, Liu Z, Teng J, Xie Q, Zhang L, et al. A preliminary study of using a deep convolution neural network to generate synthesized CT images based on CBCT for adaptive radiotherapy of nasopharyngeal carcinoma. Physics in Medicine & Biology. 2019;64(14):145010.
36. Nie D, Trullo R, Lian J, Petitjean C, Ruan S, Wang Q, et al., editors. Medical image synthesis with context-aware generative adversarial networks. International conference on medical image computing and computer-assisted intervention; 2017: Springer.
37. Shokraei Fard A, Reutens DC, Vegh V. CNNs and GANs in MRI-based cross-modality medical image estimation. arXiv e-prints. 2021:arXiv: 2106.02198.
38. Bourbonne V, Jaouen V, Hognon C, Boussion N, Lucia F, Pradier O, et al. Dosimetric Validation of a GAN-Based Pseudo-CT Generation for MRI-Only Stereotactic Brain Radiotherapy. Cancers 2021, 13, 1082. s Note: MDPI stays neutral with regard to jurisdictional claims in published …; 2021.
39. Kazemifar S, Barragán Montero AM, Souris K, Rivas ST, Timmerman R, Park YK, et al. Dosimetric evaluation of synthetic CT generated with GANs for MRI‐only proton therapy treatment planning of brain tumors. Journal of applied clinical medical physics. 2020;21(5):76-86.
40. Tie X, Lam SK, Zhang Y, Lee KH, Au KH, Cai J. Pseudo‐CT generation from multi‐parametric MRI using a novel multi‐channel multi‐path conditional generative adversarial network for nasopharyngeal carcinoma patients. Medical physics. 2020;47(4):1750-62.
41. Zhang Y, Yue N, Su MY, Liu B, Ding Y, Zhou Y, et al. Improving CBCT quality to CT level using deep learning with generative adversarial network. Medical physics. 2021;48(6):2816-26.
42. Xu K, Cao J, Xia K, Yang H, Zhu J, Wu C, et al. Multichannel residual conditional GAN-leveraged abdominal pseudo-CT generation via dixon MR images. IEEE Access. 2019;7:163823-30.
43. Yang H, Sun J, Carass A, Zhao C, Lee J, Xu Z, et al. Unpaired brain mr-to-ct synthesis using a structure-constrained cyclegan. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: Springer; 2018. p. 174-82.
44. Lei Y, Harms J, Wang T, Liu Y, Shu HK, Jani AB, et al. MRI‐only based synthetic CT generation using dense cycle consistent generative adversarial networks. Medical physics. 2019;46(8):3565-81.
45. Liu Y, Lei Y, Wang T, Zhou J, Lin L, Liu T, et al., editors. Liver synthetic CT generation based on a dense-CycleGAN for MRI-only treatment planning. Medical Imaging 2020: Image Processing; 2020: International Society for Optics and Photonics.
46. Sun H, Fan R, Li C, Lu Z, Xie K, Ni X, et al. Imaging Study of Pseudo-CT Synthesized From Cone-Beam CT Based on 3D CycleGAN in Radiotherapy. Frontiers in Oncology. 2021;11:436.
47. Liang X, Chen L, Nguyen D, Zhou Z, Gu X, Yang M, et al. Generating synthesized computed tomography (CT) from cone-beam computed tomography (CBCT) using CycleGAN for adaptive radiation therapy. Physics in Medicine & Biology. 2019;64(12):125002.
48. Li W, Kazemifar S, Bai T, Nguyen D, Weng Y, Li Y, et al. Synthesizing CT images from MR images with deep learning: model generalization for different datasets through transfer learning. Biomedical Physics & Engineering Express. 2021;7(2):025020.
49. Abdelmotaal H, Abdou AA, Omar AF, El-Sebaity DM, Abdelazeem K. Pix2pix Conditional Generative Adversarial Networks for Scheimpflug Camera Color-Coded Corneal Tomography Image Generation. Translational Vision Science & Technology. 2021;10(7):21-.
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| Issue | Vol 11 No 1 (2024) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/fbt.v11i1.14518 | |
| Keywords | ||
| pseudo-CT Generative Adversarial Network deep learning | ||
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How to Cite
1.
Yousefi MoteghaedN, FatemiA, Mostaar A. Pseudo-Computed Tomography generation from Noisy Magnetic Resonance Imaging with Deep Learning Algorithm . Frontiers Biomed Technol. 2023;11(1):113-121.
