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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/90329完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 黃宣銘 | zh_TW |
| dc.contributor.advisor | Hsuan-Ming Huang | en |
| dc.contributor.author | 張惠瑜 | zh_TW |
| dc.contributor.author | Hui-Yu Chang | en |
| dc.date.accessioned | 2023-09-26T16:17:36Z | - |
| dc.date.available | 2025-07-24 | - |
| dc.date.copyright | 2023-09-26 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-07-25 | - |
| dc.identifier.citation | Ngam, P.I., et al., Computed tomography coronary angiography - past, present and future. Singapore Med J, 2020. 61(3): p. 109-115.
Tremewan, R.N., Computed tomography in radiotherapy treatment planning. Australas Radiol, 1988. 32(1): p. 50-6. Adam, E.J., et al., Evaluation of the role of computed tomography in radiotherapy treatment planning. Clin Radiol, 1984. 35(2): p. 147-50. Rothwell, R.I., D.V. Ash, and W.G. Jones, Radiation treatment planning for bladder cancer: a comparison of cystogram localisation with computed tomography. Clin Radiol, 1983. 34(1): p. 103-11. Meyer, J.E., et al., Impact of thoracic computed tomography on radiation therapy planning in Hodgkin disease. J Comput Assist Tomogr, 1984. 8(5): p. 892-4. Goldman, L.W., Principles of CT and CT technology. J Nucl Med Technol, 2007. 35(3): p. 115-28; quiz 129-30. Willemink, M.J. and P.B. Noël, The evolution of image reconstruction for CT-from filtered back projection to artificial intelligence. Eur Radiol, 2019. 29(5): p. 2185- 2195. GN, H., Computerized transverse axial scanning (tomography): Part I. Description of system. Br J Radiol. , 1973. 46(552): p. 1016-1022. Kelcz, F., P.M. Joseph, and S.K. Hilal, Noise considerations in dual energy CT scanning. Med Phys, 1979. 6(5): p. 418-25. Lehmann LA, A.R., Macovski A, et al., Generalized image combinations in dual KVP digital radiography. . Medical Physics, 1981. 8(5): p. 659-667. Flohr, T.G., et al., First performance evaluation of a dual-source CT (DSCT) system. Eur Radiol, 2006. 16(2): p. 256-68. Patino, M., Prochowski, A., Agrawal, M. D., Simeone, F. J., Gupta, R., Hahn, P. F., & Sahani, D. V., Material Separation Using Dual-Energy CT: Current and Emerging Applications. RadioGraphics, 2016. 36(4): p. 1087-1105. Goo, H.W. and J.M. Goo, Dual-Energy CT: New Horizon in Medical Imaging. Korean J Radiol, 2017. 18(4): p. 555-569. Schmidt, B. and T. Flohr, Principles and applications of dual source CT. Phys Med, 2020. 79: p. 36-46. Rassouli, N., et al., Detector-based spectral CT with a novel dual-layer technology: principles and applications. Insights Imaging, 2017. 8(6): p. 589-598. Goodsitt, M.M., E.G. Christodoulou, and S.C. Larson, Accuracies of the synthesized monochromatic CT numbers and effective atomic numbers obtained with a rapid kVp switching dual energy CT scanner. Med Phys, 2011. 38(4): p. 2222-32. Godreau, J.P., et al., Introducing and Building a Dual-Energy CT Business. Semin Ultrasound CT MR, 2022. 43(4): p. 355-363. Schulz, B., et al., Automatic bone removal technique in whole-body dual-energy CT angiography: performance and image quality. AJR Am J Roentgenol, 2012. 199(5): p. W646-50. Yu, L., et al., Virtual monochromatic imaging in dual-source dual-energy CT: radiation dose and image quality. Med Phys, 2011. 38(12): p. 6371-9. 52 Jacobsen, M.C., et al., Dual-Energy CT: Lower Limits of Iodine Detection and Quantification. Radiology, 2019. 292(2): p. 414-419. McCollough CH, L.S., Yu L, Fletcher JG. , Dual- and Multi-Energy CT: Principles, Technical Approaches, and Clinical Applications. Radiology., 2015. 276(3): p. 637-53. Rutherford RA, P.B., Isherwood I. , Measurement of effective atomic number and electron density using an EMI scanner. . Neuroradiology, 1976. 11(1): p. 15-21. Schlomka, J.P., et al., Experimental feasibility of multi-energy photon-counting K- edge imaging in pre-clinical computed tomography. Phys Med Biol, 2008. 53(15): p. 4031-47. Rutherford RA, P.B., Isherwood I. , X-ray energies for effective atomic number determination. Neuroradiology, 1976. 11(1): p. 23-28. Alvarez, R.E. and A. Macovski, Energy-selective reconstructions in x-ray computerised tomography. Physics in Medicine & Biology, 1976. 21(5): p. 733. Siegel, M.J., S. Bhalla, and M. Cullinane, Dual-Energy CT Material Decomposition in Pediatric Thoracic Oncology. Radiol Imaging Cancer, 2021. 3(1): p. e200097. Gupta R, P.C., Leidecker C, Brady TJ, Hirsch JA, Nogueira RG, Yoo AJ. , Evaluation of dual-energy CT for differentiating intracerebral hemorrhage from iodinated contrast material staining. Radiology., 2010. 257(1): p. 205-11. Kalender, W.A., et al., Evaluation of a prototype dual-energy computed tomographic apparatus. I. Phantom studies. Med Phys, 1986. 13(3): p. 334-9. Long, Y. and J.A. Fessler, Multi-material decomposition using statistical image reconstruction for spectral CT. IEEE Trans Med Imaging, 2014. 33(8): p. 1614-26. Cong, W., B. De Man, and G. Wang, Projection decomposition via univariate optimization for dual-energy CT. J Xray Sci Technol, 2022. 30(4): p. 725-736. Lyu, Q., et al., Image-domain multi-material decomposition for dual-energy CT with non-convex sparsity regularization, in Medical Imaging 2019: Image Processing. 2019. Mendonca, P.R., P. Lamb, and D.V. Sahani, A Flexible Method for Multi-Material Decomposition of Dual-Energy CT Images. IEEE Trans Med Imaging, 2014.33(1): p. 99-116. Szczykutowicz, T.P. and G.H. Chen, Dual energy CT using slow kVp switching acquisition and prior image constrained compressed sensing. Phys Med Biol, 2010. 55(21): p. 6411-29. Niu, T., et al., Iterative image-domain decomposition for dual-energy CT. Med Phys, 2014. 41(4): p. 041901. Qiaoqiao Ding, T.N., Xiaoqun Zhang, Yong Long, Image‐domain multimaterial decomposition for dual‐energy CT based on prior information of material images.Medical Physics, 2018. Yu, L., S. Leng, and C.H. McCollough, Dual-energy CT-based monochromatic imaging. AJR Am J Roentgenol, 2012. 199(5 Suppl): p. S9-S15. Lenga, L., et al., Monoenergetic Dual-energy Computed Tomographic Imaging: Cardiothoracic Applications. J Thorac Imaging, 2017. 32(3): p. 151-158. D'Angelo, T., et al., Dual energy computed tomography virtual monoenergetic imaging: technique and clinical applications. Br J Radiol, 2019. 92(1098): p. 20180546. Albrecht, M.H., et al., Review of Clinical Applications for Virtual Monoenergetic Dual-Energy CT. Radiology, 2019. 293(2): p. 260-271. Bamberg, F., et al., Metal artifact reduction by dual energy computed tomography using monoenergetic extrapolation. Eur Radiol, 2011. 21(7): p. 1424-9. Shuman, W.P., et al., Dual-energy CT Aortography with 50% Reduced Iodine Dose Versus Single-energy CT Aortography with Standard Iodine Dose. Acad Radiol, 2016. 23(5): p. 611-8. Chuang, K.S. and H.K. Huang, A fast dual-energy computational method using isotransmission lines and table lookup. Med Phys, 1987. 14(2): p. 186-92. Shuai Leng, L.Y., Joel G. Fletcher, and Cynthia H. McCollough, Maximizing Iodine Contrast-to-Noise Ratios in Abdominal CT Imaging through Use of Energy Domain Noise Reduction and Virtual Monoenergetic Dual-Energy CT. Radiology, 2015. 276(2): p. 562-570. Schabel, C., et al., Assessment of the hepatic veins in poor contrast conditions using dual energy CT: evaluation of a novel monoenergetic extrapolation software algorithm. Rofo, 2014. 186(6): p. 591-7. Albrecht, M.H., et al., Advanced image-based virtual monoenergetic dual-energy CT angiography of the abdomen: optimization of kiloelectron volt settings to improve image contrast. Eur Radiol, 2016. 26(6): p. 1863-70. ALBAWI, S.M., Tareq Abed; AL-ZAWI, Saad. , Understanding of a convolutional neural network. , in international conference on engineering and technology (ICET). 2017, Ieee. p. 1-6. Yasaka, K., et al., Deep learning with convolutional neural network in radiology. Jpn J Radiol, 2018. 36(4): p. 257-272. Currie, G., et al., Machine Learning and Deep Learning in Medical Imaging: Intelligent Imaging. J Med Imaging Radiat Sci, 2019. 50(4): p. 477-487. Xu, Y., et al., Projection decomposition algorithm for dual-energy computed tomography via deep neural network. J Xray Sci Technol, 2018. 26(3): p. 361- 377. Zhang, W., et al., Image domain dual material decomposition for dual-energy CT using butterfly network. Med Phys, 2019. 46(5): p. 2037-2051. Su, T., et al., DIRECT-Net: A unified mutual-domain material decomposition network for quantitative dual-energy CT imaging. Med Phys, 2022. 49(2): p. 917-934. Cong, W. and G. Wang Monochromatic CT Image Reconstruction from Current- Integrating Data via Deep Learning. 2017. arXiv:1710.03784 DOI: 10.48550/arXiv.1710.03784. Feng, C., Kang, K., and Xing, Y. , Fully connected neural network for virtual monochromatic imaging in spectral computed tomography. . J. Med. Imaging 2019. 6: p. 0010006. Z. Shi, J.L., H. Li, Q. Hu and Q. Cao, A Virtual Monochromatic Imaging Method for Spectral CT Based on Wasserstein Generative Adversarial Network With a Hybrid Loss. IEEE Access, 2019. 7: p. 110992-111011. Koike, Y., et al., Pseudo low-energy monochromatic imaging of head and neck cancers: Deep learning image reconstruction with dual-energy CT. Int J Comput Assist Radiol Surg, 2022. 17(7): p. 1271-1279. D. Ulyanov, A.V., and V. Lempitsky. , Deep_image_prior. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2018. K. Schrader, T.A., J. Weickert , M. Ertel, CNN-based Euler’s Elastica Inpainting with Deep Energy and Deep Image Prior, in 10th European Workshop on Visual Information Processing (EUVIP). 2022, IEEE: Lisbon, Portugal. p. 1-6. Sun, H., et al., Dynamic PET Image Denoising Using Deep Image Prior Combined With Regularization by Denoising. IEEE Access, 2021. 9: p. 52378-52392. Ma, X., Y. Hong, and Y. Song, Super resolution land cover mapping of hyperspectral images using the deep image prior-based approach. International Journal of Remote Sensing, 2019. 41(7): p. 2818-2834. Baguer, D.O., Leuschner, J., & Schmidt, M., Computed tomography reconstruction using deep image prior and learned reconstruction methods. Inverse Problems, 2020. 36(9): p. 094004. Liu, C.-K. and H.-M. Huang, Unsupervised deep learning based image outpainting for dual-source, dual-energy computed tomography. Radiation Physics and Chemistry, 2021. 188. Heismann, B.J., J. Leppert, and K. Stierstorfer, Density and atomic number measurements with spectral x-ray attenuation method. Journal of Applied Physics, 2003. 94(3): p. 2073-2079. Liu, X., et al., Quantitative imaging of element composition and mass fraction using dual-energy CT: three-material decomposition. Med Phys, 2009. 36(5): p. 1602-9. Mendonça, P.R.S., et al., Multi-material decomposition of spectral CT images, in Medical Imaging 2010: Physics of Medical Imaging. 2010. Yu, L., et al., Image quality optimization and evaluation of linearly mixed images in dual-source, dual-energy CT. Med Phys, 2009. 36(3): p. 1019-24. Grant, K.L., Flohr, T. G., Krauss, B., Sedlmair, M., Thomas, C., & Schmidt, B. , Assessment of an Advanced Image-Based Technique to Calculate Virtual Monoenergetic Computed Tomographic Images From a Dual-Energy Examination to Improve Contrast-To-Noise Ratio in Examinations Using Iodinated Contrast Media. Investigative Radiology, 2014. 49(9): p. 586-592. Ronneberger, O., P. Fischer, and T. Brox U-Net: Convolutional Networks for Biomedical Image Segmentation. 2015. arXiv:1505.04597. Kingma, D.P. and J. Ba Adam: A Method for Stochastic Optimization. 2014. arXiv:1412.6980. Wilcoxon, F., Individual Comparisons by Ranking Methods. Biometrics Bulletin,, 1945. 1(6): p. 80-83. Samei, E., M.J. Flynn, and D.A. Reimann, A method for measuring the presampled MTF of digital radiographic systems using an edge test device. Med Phys, 1998. 25(1): p. 102-13. Wang, Z., et al., Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process, 2004. 13(4): p. 600-12. Magnusson, M., et al., Optimal Selection of Base Materials for Accurate Dual- Energy Computed Tomography: Comparison between the Alvarez-Macovski Method and Dira. Radiat Prot Dosimetry, 2021. 195(3-4): p. 218-224. Zhou, Q., et al. Towards the Automation of Deep Image Prior. 2019. arXiv:1911.07185. Wang, H., et al. Early Stopping for Deep Image Prior. 2021. arXiv:2112.06074. Albrecht, M.H., et al., Comprehensive Comparison of Virtual Monoenergetic and Linearly Blended Reconstruction Techniques in Third-Generation Dual-Source Dual-Energy Computed Tomography Angiography of the Thorax and Abdomen. Invest Radiol, 2016. 51(9): p. 582-90. Beeres, M., et al., Evaluation of different keV-settings in dual-energy CT angiography of the aorta using advanced image-based virtual monoenergetic imaging. Int J Cardiovasc Imaging, 2016. 32(1): p. 137-44. Frellesen, C., et al., Noise-optimized advanced image-based virtual monoenergetic imaging for improved visualization of lung cancer: Comparison with traditional virtual monoenergetic imaging. Eur J Radiol, 2016. 85(3): p. 665- 72. ZhangK, X., GorM, ChenY-T, ZhouY, MetzlerCA. , MetaDIP: Accelerating Deep Image Prior with Meta Learning 2022. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/90329 | - |
| dc.description.abstract | 雙能電腦斷層成像技術的問世，近年來在臨床上衍生了多樣的應用。例如:物質組成影像和虛擬單能電腦斷層影像。然而，重建影像雜訊過高一直是傳統影像後 處理方法產生的最大問題。為了提高影像品質，本文提出以非監督式深度學習「深度影像先驗」的概念建構模型，實現由雙能電腦斷層影像產生物質組成影像和虛擬 單能電腦斷層影像，同時改善影像品質。我們搜集了十位接受一般腦部電腦斷層掃描的影像用以測試深度學習模型的可行性。在物質組成影像方面，我們著重於以微調架構的深度學習模型實現雙物質分離(軟組織、骨頭)和三物質分離(軟組織、骨頭和脂肪)。在虛擬單能影像方面，我們使用調整後的深度學習模型評估了重建後虛擬低能量單能影像的影像品質。我們還提出一個簡單的技術用以一次性產生多張不同能量的虛擬單能影像。最後以影像品質指標訊雜比(Signal-to-Noise Ratio, SNR)、解析度指標調製傳遞函數(Modulated Transfer Function, MTF)、結構性相似指標(structural similarity index, SSIM index)為依據，對深度學習模型產生之重建影像和傳統方法之重建影像做評估和分析。初步分析結果顯示，與傳統生成方法所產生的影像相比，深度學習模型產生之物質組成影像和虛擬單能電腦斷層影像均可減少影像雜訊的產生，並維持相似的空間分辨率，可增加臨床價值。 | zh_TW |
| dc.description.abstract | Since its commercialization, dual energy computed tomography (DECT) has shown many important clinical applications. Material decomposition (MD) and virtual monochromatic imaging (VMI) are two common applications of DECT. However, synthesizing basis material images and VMI by traditional methods gave rise to severe noise amplification in the reconstructed images. We proposed an image domain unsupervised deep learning-based method to enhance the quality of basis material images and VMI, based on the concept of deep image prior (DIP). We retrospectively recruited ten patients who received non-contrast DECT brain scan. In the part of MD, we utilized a convolutional neural network (CNN) model with specific modifications to investigate the feasibility of generating two (soft tissue and bone) and three basis materials (soft tissue, bone, and fat) from DECT imaging. In the part of VMI, we used a modified CNN model to generate high-quality VMI from DECT imaging. We also proposed a simple technique to generate multi-energy VMIs from one trained CNN model. In the data analysis, we evaluated the DIP-based MD images and VMI in terms of SNR, MTF, and SSIM index, and compared with conventional DECT-based MD images and VMI. Our preliminary results showed that both the DIP-based MD images and VMI showed superior noise suppression. Moreover, the DIP-based MD images maintained the same spatial resolution compared with the DECT-based MD images. Similarly, the DIP-based VMIs had similar spatial resolution compared with DECT-based VMIs. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-09-26T16:17:36Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-09-26T16:17:36Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 誌謝 i
中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vi LIST OF TABLES viii LIST OF SYMBOLS ix LIST OF ABBREVIATION x Chapter 1 Introduction 1 Chapter 2 Methods 13 2.1 Principle of material decomposition 13 2.1.1 Traditional MD 13 2.1.2 Iterative MD 16 2.2 Principle of Virtual monochromatic Imaging 17 2.3 Deep image prior 18 2.4 DIP-based MD and VM 20 2.4.1 DIP-based MD 20 2.4.2 DIP-based VMI 25 2.5 DECT data acquisition and pre-processing 29 2.6 Data analysis 30 2.6.1 Mean value and Standard deviation 30 2.6.2 Modulated transfer function 30 2.6.3 Structural similarity index 31 Chapter 3 Result 33 3.1 Material decomposition 33 3.1.1 2MD 33 3.1.2 3MD 35 3.2 Virtual monochromatic Imaging 39 Chapter 4 Discussion 43 4.1 MD 43 4.2 VMI 47 Chapter 5 Conclusion 50 5.1 Conclusion – MD 50 5.2 Conclusion – VMI 50 REFERENCE 52 Appendix I 57 Appendix II 58 | - |
| dc.language.iso | en | - |
| dc.subject | 虛擬單能電腦斷層影像 | zh_TW |
| dc.subject | 雙能電腦斷層技術 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | Deep Image Prior | zh_TW |
| dc.subject | 物質組成影像 | zh_TW |
| dc.subject | Virtual monochromatic imaging | en |
| dc.subject | Dual energy computed tomography | en |
| dc.subject | Material decomposition | en |
| dc.subject | Deep Image Prior | en |
| dc.subject | Deep Learning | en |
| dc.title | 深度學習技術深度影像先驗在雙能電腦斷層影像上的應用 | zh_TW |
| dc.title | Applications of deep learning technique - Deep Image Prior in dual energy computed tomography images | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 蕭穎聰;詹美齡 | zh_TW |
| dc.contributor.oralexamcommittee | Ing-Tsung Hsiao;Meei-Ling Jan | en |
| dc.subject.keyword | 雙能電腦斷層技術,深度學習,Deep Image Prior,虛擬單能電腦斷層影像,物質組成影像, | zh_TW |
| dc.subject.keyword | Dual energy computed tomography,Deep Learning,Deep Image Prior,Virtual monochromatic imaging,Material decomposition, | en |
| dc.relation.page | 58 | - |
| dc.identifier.doi | 10.6342/NTU202301972 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2023-07-25 | - |
| dc.contributor.author-college | 醫學院 | - |
| dc.contributor.author-dept | 醫療器材與醫學影像研究所 | - |
| dc.date.embargo-lift | 2025-07-24 | - |
| 顯示於系所單位： | 醫療器材與醫學影像研究所 | |
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