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頁籤選單縮合
題 名 | An Investigation of Block-Sequential Algorithms in Statistical PET Image Reconstruction=探討區塊序列演算法在統計式正子斷層影像重建之效能 |
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作 者 | 許靖涵; | 書刊名 | Journal of Medical and Biological Engineering |
卷 期 | 24:2 2004.06[民93.06] |
頁 次 | 頁77-83 |
分類號 | 410.1644 |
關鍵詞 | 區塊序列演算法; ML影像評估; MAP影像評估; 統計式影像重建法; 正子斷層掃描; Block-sequential algorithm; OSEM; BSREM; ML estimation; MAP estimation; Statistical image reconstruction; PET; |
語 文 | 英文(English) |
中文摘要 | 統計影像重建法已經在臨床正子斷層掃描被廣泛地使用。然而重建法的效能仍受重建目標函數的選擇與演算法影響。在本研究中,我們將探討ML和MAP兩類PET影像評估法,並各自搭配OSEM與BSREM等區塊序列(Block-Sequential)快速演算法,進行影像重建計算。以對比回復係數值(CRC)作為效能指標,我們針對ML-OSEM與MAP-BSREM重建影像之正確陸進行評比。對一0.2cc小型lesion,並在相似背景值的條件下,MAP-BSREM的CRC可以高於ML-OSEM的CRC達20-50%。結果證明MAP與BSREM可以增進較小lesion的可偵測性。 |
英文摘要 | Statistical iterative reconstructions have been widely applied for clinical PET imaging. However; performance of reconstruction methods greatly depends on the choice of objective functions and the corresponding reconstruction algorithms. In this research, we investigate two important PET image estimations: maximum likelihood (ML) and maximum a posteriori (MAP). And the corresponding image estimation problems are solved by ordered subset expectation maximization (OSEM) and block sequential regularized expectation maximization (BSREM), respectively. These two fast iterative algorithms are categorized as the block-sequential algorithms. Using the contrast recovery coefficient (CRC) as a figure of merit, we compare how image estimation methods affect reconstruction accuracy. For a small 0.2 cc lesion, the CRCs of MAP-BSREM are 20-50% higher than the CRCs of ML-OSEM under matched background variations. The results demonstrate that the MAP-BSREM estimation can improve the detectability of small hot lesions markedly. |
本系統中英文摘要資訊取自各篇刊載內容。