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首页> 外文期刊>IEEE Transactions on Medical Imaging >Analysis of lesion detectability in Bayesian emission reconstruction with nonstationary object variability
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Analysis of lesion detectability in Bayesian emission reconstruction with nonstationary object variability

机译：非平稳物体变异的贝叶斯排放重建中病灶可检测性分析

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摘要

Bayesian methods based on the maximum a posteriori principle (also called penalized maximum-likelihood methods) have been developed to improve image quality in emission tomography. To explore the full potential of Bayesian reconstruction for lesion detection, we derive simplified theoretical expressions that allow fast evaluation of the detectability of a lesion in Bayesian reconstruction. This work is built on the recent progress on the theoretical analysis of image properties of statistical reconstructions and the development of numerical observers. We explicitly model the nonstationary variation of the lesion and background without assuming that they are locally stationary. The results can be used to choose the optimum prior parameters for the maximum lesion detectability. The theoretical results are validated using Monte Carlo simulations. The comparisons show good agreement between the theoretical predictions and the Monte Carlo results. We also demonstrate that the lesion detectability can be reliably estimated using one noisy data set.
机译：已经开发了基于最大后验原理的贝叶斯方法（也称为罚最大似然法）来改善发射断层扫描中的图像质量。为了探索贝叶斯重建在病变检测中的全部潜力，我们导出了简化的理论表达式，可以快速评估贝叶斯重建中病变的可检测性。这项工作建立在统计重建图像特性的理论分析和数值观测器发展的最新进展的基础上。我们明确地模拟了病变和背景的非平稳变化，而不假定它们是局部静止的。结果可用于选择最佳的先验参数，以实现最大的病变检测能力。理论结果使用蒙特卡洛模拟进行了验证。比较表明理论预测和蒙特卡洛结果之间有很好的一致性。我们还证明可以使用一个嘈杂的数据集可靠地估计病变的可检测性。

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