Suppr超能文献

基于拉普拉斯变换的直接反卷积算法在核医学应用中的研究

Direct deconvolution algorithms based on Laplace transforms in nuclear medicine applications.

作者信息

Carlsen O

机构信息

Department of Clinical Physiology and Nuclear Medicine, Vejle Hospital, Denmark.

出版信息

Nucl Med Commun. 2000 Sep;21(9):857-68. doi: 10.1097/00006231-200009000-00013.

Abstract

A set of algorithms is presented for direct deconvolution of the residue signal of an organ with the input signal to the organ. The deconvolution process yields the residual impulse response from which the distribution of transit times and the important mean transit time can be readily determined. The deconvolution method is based on the Laplace transform and it requires that the input signal can be fitted with an expression consisting of one, two or three exponentials with or without a bolus term at zero time or a constant term. These types of exponential expressions for the input signal cover a wide range of the input signals encountered in nuclear medicine applications. Simulation studies of the residue signal by convolution of various input signals with a number of residual impulse response models yielded an excellent accuracy of the deconvoluted residual impulse response for a suitably small sampling time. The simulations provide an opportunity to understand further the shapes of the residue curves depending on the shape of the input signal and the distribution of transit times. Simulations with Gaussian-distributed noise and noise spikes superimposed on the residue signal were also made to investigate the robustness of the direct deconvolution algorithm using apparently real-life data.

摘要

提出了一组算法,用于将器官的残留信号与器官的输入信号进行直接去卷积。去卷积过程产生残留脉冲响应,由此可以很容易地确定传输时间的分布和重要的平均传输时间。去卷积方法基于拉普拉斯变换,它要求输入信号可以用一个由一个、两个或三个指数组成的表达式拟合,该表达式在零时刻可以有或没有团注项或常数项。这些类型的输入信号指数表达式涵盖了核医学应用中遇到的广泛的输入信号。通过将各种输入信号与多个残留脉冲响应模型进行卷积对残留信号进行模拟研究,对于适当小的采样时间,去卷积后的残留脉冲响应具有极好的精度。这些模拟提供了一个机会,可以根据输入信号形状和传输时间分布进一步了解残留曲线的形状。还对叠加在残留信号上的高斯分布噪声和噪声尖峰进行了模拟,以使用看似真实的数据研究直接去卷积算法的稳健性。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验