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| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 黃宣銘(Hsuan-Ming Huang) | |
| dc.contributor.author | Cheng-Hsun Yang | en |
| dc.contributor.author | 楊承勳 | zh_TW |
| dc.date.accessioned | 2022-11-25T07:46:55Z | - |
| dc.date.available | 2023-07-31 | |
| dc.date.copyright | 2021-07-20 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-07-10 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/82566 | - |
| dc.description.abstract | 從動態正子斷層造影（PET）數據的藥物代謝動力學模型分析獲得的參數圖像是定量示踪劑動力學的有用工具。然而，單一像素的時間活性曲線具有較多的雜訊，這導致參數圖像的影像品質較差，從而影響後續的定量分析。為了解決這一項問題，本論文提出了一種基於深度圖像先驗（DIP）架構的圖像去雜訊方法。在原始的深度圖像先驗方法中，只能對單一影像進行去雜訊；與之不同，本研究提出的深度圖像先驗方法可以同時對所有動態正子斷層造影圖像進行去雜訊。為了進一步提高動態正子斷層造影圖像的品質，本研究提出了一種具有兩種深度圖像先驗架構的雙深度圖像先驗方法。在這種方法中，第一個深度圖像先驗架構用於生成第二個深度圖像先驗架構所需的高質量輸入影像。第二個深度圖像先驗架構將較高質量平均影像作為輸入，並執行類似於第一個深度圖像先驗架構的去雜訊方法，以提供更高質量的所有動態圖像。為了確保所提出方法的性能，本研究利用電腦模擬產生之數據進行評估，並改變不同的影像擷取參數，如：計數(counts) 以及持續期間 (duration)，來評估提出方法的一般化程度。本研究將結果與其他傳統的和基於深度學習的去雜訊方法進行比較。與非局部方法、高約束反投影法和原始深度圖像先驗法相比，本研究提出的雙深度圖像先驗方法可以提供更高質量的動態正子斷層造影圖像和參數圖像。不僅如此，本論文所提出的雙深度圖像先驗方法可以比其他去雜訊方法進一步降低均方根誤差，並提高參數圖像的質量。本研究的結果表明，提出的雙深度圖像先驗方法是一種新穎而有效的動態正子斷層造影圖像去雜訊方法。該方法不僅可以同時對所有動態正子斷層造影圖像進行去雜訊，而且可以提高參數圖像的質量。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-25T07:46:55Z (GMT). No. of bitstreams: 1 U0001-0807202114424600.pdf: 4855434 bytes, checksum: 5055dc5008e42b2286917d5d813db25f (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 誌謝 i 中文摘要 ii ABSTRACT iii CONTENTS v LIST OF FIGURES vi Chapter 1 Introduction 1 1.1 Dynamic positron emission tomography 1 1.2 Kinetic modeling 2 1.3 Traditional denoising method 3 1.4 DL-based denoising method 4 Chapter 2 Methodology 7 2.1 Deep image prior (DIP) 7 2.2 Simulations of dynamic PET data 13 2.2.1 Computer simulations 13 2.2.2 Additional computer simulations 17 2.3 Data analysis 19 Chapter 3 Results 24 Chapter 4 Discussion 39 Chapter 5 Conclusion 46 REFERENCE 47 | |
| dc.language.iso | en | |
| dc.title | 基於deep image prior (DIP) 之動態正子斷層成像同時去雜訊演算法 | zh_TW |
| dc.title | Simultaneous denoising of dynamic PET imaging based on deep image prior (DIP) | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 張瀚(Hsin-Tsai Liu),蕭穎聰(Chih-Yang Tseng) | |
| dc.subject.keyword | 深度圖像先驗,動態正子斷層造影,參數影像,非監督式學習, | zh_TW |
| dc.subject.keyword | deep image prior,dynamic PET,parametric imaging,unsupervised learning, | en |
| dc.relation.page | 51 | |
| dc.identifier.doi | 10.6342/NTU202101344 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-07-12 | |
| dc.contributor.author-college | 醫學院 | zh_TW |
| dc.contributor.author-dept | 醫療器材與醫學影像研究所 | zh_TW |
| dc.date.embargo-lift | 2023-07-31 | - |
| Appears in Collections: | 醫療器材與醫學影像研究所 | |
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| U0001-0807202114424600.pdf Access limited in NTU ip range | 4.74 MB | Adobe PDF | View/Open |
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