本文選題：因子分析 + α散度　； 參考：《南方醫(yī)科大學學報》2017年12期

【摘要】：目的建立一種基于動力學聚類與α散度測度的因子分析方法,分析從動態(tài)心肌PET圖像中無創(chuàng)地提取血輸入函數(shù)及局部組織的時間活度曲線。方法通過最小化動態(tài)圖像與因子模型間的α散度將動態(tài)PET圖像做初步的分解,得到初始因子與因子圖像,然后聯(lián)合PET像素動力學聚類的先驗信息解決因子分析模型中解的非唯一性問題,將初始因子與因子圖像通過空間變換生成具有生理意義的組織活度曲線及組織空間分布。結果與傳統(tǒng)的最小二乘法測度和最小化因子圖像間重疊程度約束模型相比,本模型對噪聲的敏感性較低,提取出的結果的精確性較高。結論通過選取最優(yōu)的α值作為因子分析模型的測度,并引入PET圖像像素的動力學聚類信息,能精確地獲得血輸入函數(shù)及局部組織的時間活度曲線,在視覺評價及量化評價均具有優(yōu)質表現(xiàn)。
[Abstract]:Objective to establish a factor analysis method based on dynamic clustering and 偽 divergence measure to analyze the time activity curve of blood input function and local tissue from dynamic myocardial PET images.Methods by minimizing the 偽 divergence between the dynamic image and the factor model, the dynamic PET image was preliminarily decomposed to obtain the initial factor and factor image.Then combining the priori information of PET pixel dynamic clustering to solve the non-uniqueness problem of the solution in the factor analysis model, the initial factor and the factor image are transformed into the physiological tissue activity curve and the tissue spatial distribution by spatial transformation.Results compared with the traditional least square measure and the minimum factor image overlap degree constraint model, this model is less sensitive to noise, and the accuracy of the extracted results is higher.Conclusion by selecting the optimal 偽 value as the measure of the factor analysis model and introducing the dynamic clustering information of the pixels of the PET image, the blood input function and the time activity curve of the local tissue can be accurately obtained.In visual evaluation and quantitative evaluation have high quality performance.
【作者單位】： 南方醫(yī)科大學生物醫(yī)學工程學院//廣東省醫(yī)學圖像處理重點實驗室;
【基金】：國家自然科學基金(81501541,61628105) 廣東省自然科學基金(2014A030310243,2016A030313577) 國家重點研發(fā)計劃(2016YFC0104003) 廣州市珠江科技新星專項(201610010011)~~
【分類號】：R541;R817.4

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