研究生: |
李國紳 Kuo-Shen Lee |
---|---|
論文名稱: |
影像事前機率在正子斷層掃描之研究 An Investigation of Image Priors in Positron Emission Tomography |
指導教授: |
許靖涵
Ching-Han Hsu |
口試委員: | |
學位類別: |
碩士 Master |
系所名稱: |
原子科學院 - 生醫工程與環境科學系 Department of Biomedical Engineering and Environmental Sciences |
論文出版年: | 2004 |
畢業學年度: | 92 |
語文別: | 中文 |
論文頁數: | 81 |
中文關鍵詞: | 正子斷層掃描 、影像事前機率 |
外文關鍵詞: | Positron Emission Tomography (PET), Image prior |
相關次數: | 點閱:2 下載:0 |
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最大相似度與期望值最大化演算法(Maximum Likelihood Expectation Maximization,MLEM)在正子斷層掃描(Positron Emission Tomography,PET)上用來進行影像重建,會因為PET本身具有不健全性(ill-condition)的問題存在,使得微量的雜訊便可能造成重建影像值的變動性大幅增加。因此使用MLEM演算法會有隨著疊代次數增加,而使得重建影像有著變異性(variance)增大以及數值發散的問題存在。為了避免此一問題,我們改採用最大事後機率評估法(Maximum a Posteriori,MAP),來進行影像重建。此方法中影像事前機率(Image prior)為一極為重要的部份,不同的事前機率會對重建影像產生不同的影響,例如過度平滑(over-smoothing)使得重建影像中邊緣的部份消失。在本研究中,我們提出了Approximate ratio(AR)事前機率,將注重在保留重建影像中邊緣的部份,並兼具能將相同比例但絕對差異不同的區域維持在一相近比例的情況下。我們並進行各類假體的蒙地卡羅模擬(Monte Carlo study)來比較優劣,最後並使用Hoffman假體模擬真實情況,進行驗證。
In Positron emission tomography (PET), image reconstruction using maximum likelihood expectation maximization (MLEM) suffers a problem which small noise could make reconstruction image exhibit high variance due to ill-condition. This motivates the development of some practical solutions to produce acceptable image. In this work, we consider the maximum a posteriori (MAP) estimation which combines the likelihood function with image prior under the paradigm of Bayesian statistics. The One-Step-Late (OSL) algorithm is used for the corresponding image reconstruction. The selection of proper image prior is the emphasis of this work. Different prior has different affect on reconstruction image. For example, quadratic prior has a well-known over-smoothing problem, which leads to the blurring of edges. Non-quadratic prior has better performance in maintaining edge information, but it is difficult in optimization. In this research, we proposed an approximate (AP) prior which can preserve edge regions by introducing the same penalty to those regions with same contrast. We also compare AR prior with other image priors such as: Quadratic, L1-Norm, Huber, Geman and McClure, Relative difference and Thin Plate. We use several point and line sources, and Hoffman phantom to evaluate the performance of various image priors. The proposed AR prior exhibits the best performance in terms of resolution and contrast recovery.
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