An Algorithm for the Reconstruction of Entrance Beam Fluence from Virtual Patient Exit Electronic Portal Images

• 作者: E. Ishmael Parsai Nicholas N. Sperling
• 来源期刊: 医学物理学、临床工程、放射肿瘤学(英文)
• 学科: 医学
• 关键词: BEAM FLUENCE EPID IMAGES In-Vivo Dosimetry Monte Carlo ALGORITHM Dose RECONSTRUCTION
• 年，卷（期）: 2015,（2）
• 页码范围: 177-183
• 页数: 7页
• 分类号: R73

摘要

The problem of determining the in vivo dosimetry for patients undergoing radiation treatment has been an area of interest since the development of the field. More recent methods of measurement employ Electronic Portal Image Devices (EPID), or dosimeter arrays, for entrance or exit fluence determination. The more recent methods of in vivo dosimetry make use of detector arrays and reconstruction techniques to determine dose throughout the patient volume. One method uses an array of ion chambers located upstream of the patient. This requires a special hardware device and places an additional attenuator in the beam path, which may not be desirable. An alternative to this approach is to use the existing EPID, which is part of most modern linear accelerators, to image the patient using the treatment beam. Methods exist to deconvolve the detector function of the EPID using a series of weighted exponentials [1]. Additionally, this method has been extended to the deconvolution of the patient scatter in order to determine in vivo dosimetry. The method developed here intends to use EPID images and an iterative deconvolution algorithm to reconstruct the impinging primary fluence on the patient. This primary fluence may then be employed, using treatment time volumetric imaging, to determine dose through the entire patient volume. Presented in this paper is the initial discussion of the algorithm, and a theoretical evaluation of its efficacy using montecarlo derived virtual fluence measurements. The results presented here indicate an agreement of 1% dose difference within 95% the field area receiving 10% of the entrance fluence for a set of sample highly modulated fields. These results warrant continued investigation in applying this algorithm to clinical patient treatments.