• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>18th International Conference on Information Processing in Medical Imaging IPMI 2003 Jul 20-25, 2003 Ambleside, UK >Theoretical Evaluation of the Detectability of Random Lesions in Bayesian Emission Reconstruction
【24h】

Theoretical Evaluation of the Detectability of Random Lesions in Bayesian Emission Reconstruction

机译：贝叶斯排放重建中随机病变可检测性的理论评价

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Detecting cancerous lesion is an important task in positron emission tomography (PET). Bayesian methods based on the maximum a posteriori principle (also called penalized maximum likelihood methods) have been developed to deal with the low signal to noise ratio in the emission data. Similar to the filter cut-off frequency in the filtered backprojection method, the prior parameters in Bayesian reconstruction control the resolution and noise trade-off and hence affect detectability of lesions in reconstructed images. Bayesian reconstructions are difficult to analyze because the resolution and noise properties are nonlinear and object-dependent. Most research has been based on Monte Carlo simulations, which are very time consuming. Building on the recent progress on the theoretical analysis of image properties of statistical reconstructions and the development of numerical observers, here we develop a theoretical approach for fast computation of lesion detectability in Bayesian reconstruction. The results can be used to choose the optimum hyperpa-rameter for the maximum lesion detectability. New in this work is the use of theoretical expressions that explicitly model the statistical variation of the lesion and background without assuming that the object variation is (locally) stationary. The theoretical results are validated using Monte Carlo simulations. The comparisons show good agreement between the theoretical predications and the Monte Carlo results.
机译：检测癌变病变是正电子发射断层扫描（PET）中的重要任务。已经开发出基于最大后验原理的贝叶斯方法（也称为惩罚最大似然法）来处理排放数据中的低信噪比。与滤波反投影方法中的滤波器截止频率相似，贝叶斯重建中的先验参数控制分辨率和噪声权衡，因此影响重建图像中病变的可检测性。贝叶斯重构很难分析，因为分辨率和噪声属性是非线性的并且取决于对象。大多数研究都基于蒙特卡洛模拟，这非常耗时。基于统计重建图像特性的理论分析的最新进展以及数值观测器的发展，在这里，我们开发了一种快速计算贝叶斯重建中病变可检测性的理论方法。结果可用于选择最佳的超参数以获得最大的病变检测能力。这项工作中的新功能是使用理论表达式来明确建模病变和背景的统计变化，而无需假设对象变化是（局部）平稳的。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。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号