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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 徐宏民(Winston H. Hsu) | |
dc.contributor.author | Jhih-Yuan Lin | en |
dc.contributor.author | 林智遠 | zh_TW |
dc.date.accessioned | 2021-05-20T00:52:40Z | - |
dc.date.available | 2021-08-06 | |
dc.date.available | 2021-05-20T00:52:40Z | - |
dc.date.copyright | 2020-08-24 | |
dc.date.issued | 2020 | |
dc.date.submitted | 2020-08-06 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/8357 | - |
dc.description.abstract | 心臟核磁共振的技術被廣泛用於檢測心臟功能,受惠於其非侵入式的方式,檢查過程中不會對病人造成傷害。然而,取得高解析度的心臟核磁共振影像是一個耗時且昂貴的程序。為此,我們提出一個新穎的端到端 (end-to-end) 可訓練的神經網路來解決心臟核磁共振超解析度成像的問題,無須調整既有的硬體設備與掃描協定。我們妥善運用心臟的領域知識 (即心跳週期相位),以週期性的函數來描述心跳週期,以對應心臟核磁共振所特有的循環特性。此外,我們提出殘差中的殘差學習機制 (residual of residual learning),讓網路可以循序漸進地學習低解析度到高解析度的映射,透過這樣的機制,我們的方法能夠彈性地因應不同難度的問題。在大規模數據集上的定量與定性分析結果顯示我們的方法優於現有最佳的方法。 | zh_TW |
dc.description.abstract | Cardiac Magnetic Resonance Imaging (CMR) is widely used since it can illustrate the structure and function of the heart in a non-invasive and painless way. However, it is time-consuming and high-cost to acquire high-quality scans due to the hardware limitation. To this end, we propose a novel end-to-end trainable network to solve CMR video super-resolution problem without the hardware upgrade and the scanning protocol modifications. We incorporate the cardiac knowledge into our model to assist in utilizing the temporal information. Specifically, we formulate the cardiac knowledge as the periodic function, which is tailored to meet the cyclic characteristic of CMR. Besides, the proposed residual of residual learning scheme facilitates the network to learn the LR-HR mapping in a progressive refinement fashion. This mechanism enables the network to have the adaptive capability by adjusting refinement iterations depending on the difficulty of the task. Extensive experimental results on large-scale datasets demonstrate the superiority of the proposed method compared with numerous state-of-the-art methods. | en |
dc.description.provenance | Made available in DSpace on 2021-05-20T00:52:40Z (GMT). No. of bitstreams: 1 U0001-3007202013591400.pdf: 1290518 bytes, checksum: cd8dc9b5b9b0bfa545a974e4245ee7e1 (MD5) Previous issue date: 2020 | en |
dc.description.tableofcontents | 誌謝ii 摘要iii Abstract iv 1 Introduction 1 2 Proposed approach 3 2.1 Overall architecture 3 2.2 Phase fusion module 4 2.3 Residual of residual learning 5 2.4 Loss function 6 3 Experiment 8 3.1 Data preparation 8 3.2 Evaluation metrics 8 3.3 Training details 9 3.4 Experimental results 9 3.5 Ablation study 10 4 Conclusion 12 Bibliography 13 | |
dc.language.iso | en | |
dc.title | 基於相位之心臟核磁共振超解析度成像 | zh_TW |
dc.title | Efficient and Phase-aware Video Super-resolution for Cardiac MRI | en |
dc.type | Thesis | |
dc.date.schoolyear | 108-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 陳文進(Wen-Chin Chen),葉梅珍(Mei-Chen Yeh),李志國(Chih-Kuo Lee),蘇東弘(Tung-Hung Su) | |
dc.subject.keyword | 心臟核磁共振,超解析度成像,深度學習, | zh_TW |
dc.subject.keyword | Cardiac MRI,Video Super-resolution,Deep Learning, | en |
dc.relation.page | 16 | |
dc.identifier.doi | 10.6342/NTU202002101 | |
dc.rights.note | 同意授權(全球公開) | |
dc.date.accepted | 2020-08-07 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
顯示於系所單位: | 資訊工程學系 |
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U0001-3007202013591400.pdf | 1.26 MB | Adobe PDF | 檢視/開啟 |
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