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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/81914完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 李百祺(Pai-Chi Li) | |
| dc.contributor.author | Shi-Hao Li | en |
| dc.contributor.author | 黎世豪 | zh_TW |
| dc.date.accessioned | 2022-11-25T03:06:30Z | - |
| dc.date.available | 2026-09-30 | |
| dc.date.copyright | 2021-10-23 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-10-03 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/81914 | - |
| dc.description.abstract | 在三維超音波影像中，行列式二維陣列相較於全採樣二維陣列大幅地降低了所需要的硬體以及計算複雜度。然而，當可控制的傳感器元件從N^2減少至2N以及較長孔徑造成的邊界效應，將導致成像品質受到影響。因此本論文提出從行列式二維陣列的三維影像重建出全採樣二維陣列的全資料集的方法，藉由重建的全資料集不僅可以改善行列式二維陣列的成像品質，也可能適用於其他被全採樣陣列發展的成像方法，預期能達到更廣泛的應用。本論文所提出方法之核心為透過空間濾波來重建全採樣空間資料，為達到較佳之效果，本方法首先使用端對端的深度學習框架，將行列式二維陣列的三維影像增強至全採樣二維陣列的雙向動態聚焦影像，接著應用N^2組K空間濾波器至增強後的影像，以估計出全採樣二維陣列各個發射事件所獲得的低解析度影像，最後藉由波束和分解方法，計算各張低解析度影像對於原始通道資料中每一個取樣點的貢獻量，再將該貢獻量加總以重建出全採樣二維陣列的全資料集。在Field II模擬實驗中，使用128行與128列、11MHz、通道間隔為一倍波長的行列式二維陣列，將線仿體於-6dB與-20dB的側向波束寬度從0.42mm及0.64mm改善至0.35mm及0.57mm，旁瓣等級也從-15.92dB降低至-24.78dB。在囊腫仿體中，將對比與對比雜訊比從5.09dB及2.59dB改善至19.10dB與4.85dB，廣義對比雜訊比也從0.71提高至0.99。提出的方法預期也能拓展至其他不能存取全資料集的超音波系統上，且無關於陣列的發射方式。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-25T03:06:30Z (GMT). No. of bitstreams: 1 U0001-0310202115543800.pdf: 8937006 bytes, checksum: 88c645c6697aa4fb1e9ccd8c4eabf9f7 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 致謝 i 摘要 iii ABSTRACT iv 目錄 v 圖目錄 viii 表目錄 xv 第一章 緒論 1 1.1 三維超音波影像 1 1.1.1 一維陣列成像系統 1 1.1.2 二維陣列成像系統 2 1.2 行列式二維陣列 6 1.2.1 硬體架構 7 1.2.2 成像解析度 9 1.2.3 成像方法的限制 10 1.3 研究目標 11 1.4 論文架構 12 第二章 基於深度學習之影像增強方法 13 2.1 三維影像模擬方法 14 2.1.1 全採樣二維陣列的成像方法 15 2.1.2 行列式二維陣列的成像方法 16 2.1.3 點擴散函數分析 17 2.1.4 模擬時間分析 19 2.1.5 基於卷積之快速模擬方法 20 2.2 資料集 24 2.2.1 成像物件的設計 24 2.2.2 三維影像區塊切割方法 27 2.3 深度神經網路架構 28 2.3.1 訓練方式 30 2.3.2 評估指標 31 2.4 模擬結果與討論 32 第三章 K空間濾波方法 38 3.1 K空間介紹 39 3.2 反濾波器設計方法 40 3.2.1 K空間響應分析 41 3.2.2 濾波結果分析 42 3.3 維納濾波器設計方法 43 3.3.1 濾波結果分析 43 3.3.2 限制重建範圍 44 3.3 模擬結果與討論 46 第四章 從影像重建通道資料的方法 49 4.1 波束和分解方法 50 4.2 重建通道資料分析 51 4.2.1 相位分析 53 4.2.2 影像分析 57 4.3 模擬結果與討論 58 第五章 分析與討論 63 5.1 執行階段討論 63 5.2 使用神經網路增強影像之必要性討論 63 5.3 使用K空間濾波方法之必要性討論 66 第六章 結論與未來展望 68 6.1 結論 68 6.2 未來展望 68 6.2.1 一維陣列之線仿體實驗資料測試 69 6.2.2 一維陣列之臨床乳房資料測試 73 參考文獻 81 | |
| dc.language.iso | zh-TW | |
| dc.subject | K空間濾波方法 | zh_TW |
| dc.subject | 行列式二維陣列 | zh_TW |
| dc.subject | 深度神經網路 | zh_TW |
| dc.subject | row-column addressed 2-D array | en |
| dc.subject | deep neural network | en |
| dc.subject | k-space filtering | en |
| dc.title | 使用K空間濾波重建全採樣陣列資料的行列式陣列波束成型改善方法 | zh_TW |
| dc.title | Improved Row-Column Addressed Array Beamforming with Reconstruction of Fully Sampled Array Data Using K-Space Filtering | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | #VALUE! | |
| dc.subject.keyword | 行列式二維陣列,深度神經網路,K空間濾波方法, | zh_TW |
| dc.subject.keyword | row-column addressed 2-D array,deep neural network,k-space filtering, | en |
| dc.relation.page | 86 | |
| dc.identifier.doi | 10.6342/NTU202103515 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2021-10-05 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 生醫電子與資訊學研究所 | zh_TW |
| dc.date.embargo-lift | 2026-09-30 | - |
| 顯示於系所單位： | 生醫電子與資訊學研究所 | |
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| U0001-0310202115543800.pdf 此日期後於網路公開 2026-09-30 | 8.73 MB | Adobe PDF |
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