碲化镉作为多光谱医学成像探测器材料的质量评估
Quality assessment of Cadmium Telluride as a detector material for multispectral medical imaging
摘要：
碲化镉（CdTe）是一种具有优异光子辐射吸收性能的高Z材料，使其成为辐射探测技术中有前途的材料。然而，CdTe晶体的脆性以及它们的缺陷浓度的变化需要在复杂的检测器处理过程之前进行彻底的质量评估。我们提出了我们的质量评估的碲化镉作为探测器材料的多光谱医学成像，一项研究，这是作为联盟项目多光谱光子计数医学成像和光束表征（MPMIB）的一部分进行。该项目的目的是开发新型CdTe探测器，并获得每像素光谱信息，从而可以区分不同的辐射类型和组织。为了评估晶体内部的缺陷密度-这可能会降低探测器的性能-我们采用红外显微镜（IR）。后验数据分析使我们能够将缺陷分布可视化为3D缺陷图。此外，我们研究了材料的正面和背面差异与电流-电压（IV）测量，以确定晶体像素化的首选表面，并与原型进行测试测量，为进一步处理提供反馈。我们提出了我们的质量评估链的不同部分，并将关闭与我们的原型PC探测器之一，在一个小的层析设置获得的第一个实验结果。
英文摘要：
Cadmium Telluride (CdTe) is a high-Z material with excellent photon radiation absorption properties, making it a promising material to include in radiation detection technologies. However, the brittleness of CdTe crystals as well as their varying concentration of defects necessitate a thorough quality assessment before the complex detector processing procedure. We present our quality assessment of CdTe as a detector material for multispectral medical imaging, a research which is conducted as part of the Consortium Project Multispectral Photon-counting for Medical Imaging and Beam characterization (MPMIB). The aim of the project is to develop novel CdTe detectors and obtain spectrum-per-pixel information that make the distinction between different radiation types and tissues possible. To evaluate the defect density inside the crystals -- which can deteriorate the detector performance -- we employ infrared microscopy (IRM). Posterior data analysis allows us to visualise the defect distributions as 3D defect maps. Additionally, we investigate front and backside differences of the material with current-voltage (IV) measurements to determine the preferred surface for the pixelisation of the crystal, and perform test measurements with the prototypes to provide feedback for further processing. We present the different parts of our quality assessment chain and will close with first experimental results obtained with one of our prototype PC detectors in a small tomographic setup.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
