Cormack J, Shuter B
Radiology Department, Flinders Medical Centre, South Australia.
Australas Phys Eng Sci Med. 1991 Jun;14(2):86-96.
The need for random deviates arises in many scientific applications, such as the simulation of physical processes, numerical evaluation of complex mathematical formulae and the modeling of decision processes. In medical physics, Monte Carlo simulations have been used in radiology, radiation therapy and nuclear medicine. Specific instances include the modelling of x-ray scattering processes and the addition of random noise to images or curves in order to assess the effects of various processing procedures. Reliable sources of random deviates with statistical properties indistinguishable from true random deviates are a fundamental necessity for such tasks. This paper provides a review of computer algorithms which can be used to generate uniform random deviates and other distributions of interest to medical physicists, along with a few caveats relating to various problems and pitfalls which can occur. Source code listings for the generators discussed (in FORTRAN, Turbo-PASCAL and Data General ASSEMBLER) are available on request from the authors.
在许多科学应用中都需要随机偏差，比如物理过程模拟、复杂数学公式的数值评估以及决策过程建模。在医学物理领域，蒙特卡罗模拟已应用于放射学、放射治疗和核医学。具体例子包括X射线散射过程建模以及给图像或曲线添加随机噪声，以评估各种处理程序的效果。对于此类任务而言，拥有统计特性与真正随机偏差难以区分的可靠随机偏差源是基本要求。本文综述了可用于生成均匀随机偏差以及医学物理学家感兴趣的其他分布的计算机算法，并提及了可能出现的各种问题和陷阱的一些注意事项。文中讨论的生成器的源代码清单（用FORTRAN、Turbo - PASCAL和Data General ASSEMBLER编写）可应作者要求提供。
