Hidden-data spaces for maximum-likelihood PET reconstruction

IEEE Conference on Nuclear Science Symposium and Medical Imaging Pub Date : 1992-10-25 DOI:10.1109/NSSMIC.1992.301014

J. Fessler

引用次数: 4

Abstract

The author shows that expectation-maximization (EM) algorithms based on smaller complete data spaces will typically converge faster. As an example, he compares the two maximum-likelihood (ML) image reconstruction algorithms of D. G. Politte and D. L. Snyder (1991) which are based on measurement models that account for attenuation and accidental coincidences in positron-emission tomography (PET).<>

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最大似然PET重建的隐藏数据空间

作者表明，基于较小完整数据空间的期望最大化(EM)算法通常收敛速度更快。作为一个例子，他比较了D. G. Politte和D. L. Snyder(1991)的两种最大似然(ML)图像重建算法，这两种算法基于考虑正电子发射断层扫描(PET)中衰减和偶然巧合的测量模型

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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IEEE Conference on Nuclear Science Symposium and Medical Imaging

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