粒子云-点扩散函数在X射线闪烁体成像中的应用  被引量:1

Particle-in-cell point spread function and its application in X-ray scintillator imaging system

在线阅读下载全文

作  者:赵文聪 吴衍青[2] 夏慧娟 张磊[1] Zhao Wencong;Wu Yanqing;Xia Huijuan;Zhang Lei(Shanghai University,Shanghai 200444,China;Shanghai Institute of Applied Physics,Chinese Academy of Sciences,Shanghai 201204,China)

机构地区:[1]上海大学,上海200444 [2]中国科学院上海应用物理研究所,上海201204

出  处:《电子测量技术》2018年第18期42-47,共6页Electronic Measurement Technology

基  金:国家重点研发项目(2017YFA0206002,2016YFA0401302);国家自然科学基金面上项目(11275255,11775291)资助

摘  要:针对X射线闪烁体成像系统图像复原过程中点扩函数难以通过实验精确测量又难以通过理论简单构建,导致图像信噪比不高的问题。粒子云点扩散函数模型利用粒子云分配方法,分解系统成像的各种退化因素,然后将所有退化图像卷积;同时配合目标信息反馈,筛选并确定模型参数,最终获得系统的粒子云点扩散函数。由图像复原效果可知,相比实验获得的系统点扩散函数,粒子云点扩散函数模型可以更有效的复原图像;其信噪比提高约15 dB,远远高于实验测量点扩散函数的复原结果。而且构建相对简单,避免了大量复杂的光学原理分析,具有很强的实用性和适用性。In the image restoration process of an X-ray scintillator imaging system, the point spread function is difficult to measure accurately through experiments and is also difficult to construct easily by theory, resulting in a low signal-to-noise ratio of the recovered image. To solve this problem, the particle-in-cell point spread function(PIC-PSF) model first decomposed the various degradation factors of the system imaging by using the particle-in-cell distribution method, and then convoluted all the degraded images;meanwhile,filtrated and determined the model parameters with the target information feedback, and finally get the system's PIC-PSF. Known from experiments, the PIC-PSF model can recover the image more effectively than the point spread function obtained from experimental measurement;And the signal to noise ratio improved about 15 dB which is far more than the latter. Moreover,the construction of PIC-PSF is relatively simple, avoid analyzing a large number of complex optical principle, with strong practicality and applicability.

关 键 词:X射线 闪烁体成像 粒子云分配 点扩散函数 

分 类 号:O72[理学—晶体学]

 

参考文献:

正在载入数据...

 

二级参考文献:

正在载入数据...

 

耦合文献:

正在载入数据...

 

引证文献:

正在载入数据...

 

二级引证文献:

正在载入数据...

 

同被引文献:

正在载入数据...

 

相关期刊文献:

正在载入数据...

相关的主题
相关的作者对象
相关的机构对象