基于 YSO/SiPM 探测器的虚拟单色图像合成可行性研究。

IF 1.7 3区 医学 Q3 INSTRUMENTS & INSTRUMENTATION Journal of X-Ray Science and Technology Pub Date : 2024-09-28 DOI:10.3233/XST-240039
Du Zhang, Bin Wu, Daoming Xi, Rui Chen, Peng Xiao, Qingguo Xie
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引用次数: 0

摘要

背景:光子计数 CT 系统的开发主要集中在碲锌镉(CZT)和碲化镉(CdTe)等半导体探测器上。然而,这些探测器面临着高成本和电荷共享问题,从而扭曲了能谱。使用带硅光电倍增管(SiPM)的正硅酸钇(YSO)闪烁体进行间接探测提供了一种具有高探测效率、低暗计数率和高传感器增益的经济有效的替代方法:本研究旨在通过评估虚拟单色图像的合成误差,证明基于多电压阈值(MVT)采样方法的 YSO/SiPM 检测器(DexScanner L103)作为光子计数 CT 检测器的可行性:在这项研究中,我们开发了一个概念验证型台式光子计数 CT 系统,并在不包含 X 射线光谱信息的情况下,根据最小平方偏差原理,采用多项式拟合的直接方法进行经验虚拟单色图像合成(EVMIS)。通过使用平均相对误差(MRE)和均方误差(MSE)比较校准材料和测试材料的重建衰减系数和实际衰减系数,评估了经验能量校准技术的准确性:在双材料成像实验中,不同材料的三幅单能量图像的总体平均合成误差为 2.53% ±2.43%。同样,在包含四种材料的 K 边成像实验中,三幅单能图像的总体平均合成误差为 4.04% ±2.63%。在大鼠生物软组织成像实验中,我们进一步预测了大鼠各种组织的密度:骨密度为 1.41±0.07 g/cm3,脂肪组织密度为 0.91±0.06 g/cm3,心脏组织密度为 1.09±0.04 g/cm3,肺组织密度为 0.32±0.07 g/cm3。这些结果表明,重建的虚拟单色图像对每种材料都具有良好的一致性:本研究表明,基于 SiPM 的光子计数探测器可用于单色图像合成,是一种很有前途的光谱计算机断层扫描系统开发方法。
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Feasibility study of YSO/SiPM based detectors for virtual monochromatic image synthesis.

Background: The development of photon-counting CT systems has focused on semiconductor detectors like cadmium zinc telluride (CZT) and cadmium telluride (CdTe). However, these detectors face high costs and charge-sharing issues, distorting the energy spectrum. Indirect detection using Yttrium Orthosilicate (YSO) scintillators with silicon photomultiplier (SiPM) offers a cost-effective alternative with high detection efficiency, low dark count rate, and high sensor gain.

Objective: This work aims to demonstrate the feasibility of the YSO/SiPM detector (DexScanner L103) based on the Multi-Voltage Threshold (MVT) sampling method as a photon-counting CT detector by evaluating the synthesis error of virtual monochromatic images.

Methods: In this study, we developed a proof-of-concept benchtop photon-counting CT system, and employed a direct method for empirical virtual monochromatic image synthesis (EVMIS) by polynomial fitting under the principle of least square deviation without X-ray spectral information. The accuracy of the empirical energy calibration techniques was evaluated by comparing the reconstructed and actual attenuation coefficients of calibration and test materials using mean relative error (MRE) and mean square error (MSE).

Results: In dual-material imaging experiments, the overall average synthesis error for three monoenergetic images of distinct materials is 2.53% ±2.43%. Similarly, in K-edge imaging experiments encompassing four materials, the overall average synthesis error for three monoenergetic images is 4.04% ±2.63%. In rat biological soft-tissue imaging experiments, we further predicted the densities of various rat tissues as follows: bone density is 1.41±0.07 g/cm3, adipose tissue density is 0.91±0.06 g/cm3, heart tissue density is 1.09±0.04 g/cm3, and lung tissue density is 0.32±0.07 g/cm3. Those results showed that the reconstructed virtual monochromatic images had good conformance for each material.

Conclusion: This study indicates the SiPM-based photon-counting detector could be used for monochromatic image synthesis and is a promising method for developing spectral computed tomography systems.

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来源期刊
CiteScore
4.90
自引率
23.30%
发文量
150
审稿时长
3 months
期刊介绍: Research areas within the scope of the journal include: Interaction of x-rays with matter: x-ray phenomena, biological effects of radiation, radiation safety and optical constants X-ray sources: x-rays from synchrotrons, x-ray lasers, plasmas, and other sources, conventional or unconventional Optical elements: grazing incidence optics, multilayer mirrors, zone plates, gratings, other diffraction optics Optical instruments: interferometers, spectrometers, microscopes, telescopes, microprobes
期刊最新文献
Industrial digital radiographic image denoising based on improved KBNet. Research on the effectiveness of multi-view slice correction strategy based on deep learning in high pitch helical CT reconstruction. A fully linearized ADMM algorithm for optimization based image reconstruction. A reconstruction method for ptychography based on residual dense network. Can AI generate diagnostic reports for radiologist approval on CXR images? A multi-reader and multi-case observer performance study.
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