Suppr超能文献

基于K边滤波X射线的光子计数X射线成像:一项模拟研究。

Photon counting x-ray imaging with K-edge filtered x-rays: A simulation study.

作者信息

Atak Haluk, Shikhaliev Polad M

机构信息

Department of Nuclear Engineering, Hacettepe University, Ankara 06800, Turkey.

出版信息

Med Phys. 2016 Mar;43(3):1385-400. doi: 10.1118/1.4941742.

Abstract

PURPOSE

In photon counting (PC) x-ray imaging and computed tomography (CT), the broad x-ray spectrum can be split into two parts using an x-ray filter with appropriate K-edge energy, which can improve material decomposition. Recent experimental study has demonstrated substantial improvement in material decomposition with PC CT when K-edge filtered x-rays were used. The purpose of the current work was to conduct further investigations of the K-edge filtration method using comprehensive simulation studies.

METHODS

The study was performed in the following aspects: (1) optimization of the K-edge filter for a particular imaging configuration, (2) effects of the K-edge filter parameters on material decomposition, (3) trade-off between the energy bin separation, tube load, and beam quality with K-edge filter, (4) image quality of general (unsubtracted) images when a K-edge filter is used to improve dual energy (DE) subtracted images, and (5) improvements with K-edge filtered x-rays when PC detector has limited energy resolution. The PC x-ray images of soft tissue phantoms with 15 and 30 cm thicknesses including iodine, CaCO3, and soft tissue contrast materials, were simulated. The signal to noise ratio (SNR) of the contrast elements was determined in general and material-decomposed images using K-edge filters with different atomic numbers and thicknesses. The effect of the filter atomic number and filter thickness on energy separation factor and SNR was determined. The boundary conditions for the tube load and halfvalue layer were determined when the K-edge filters are used. The material-decomposed images were also simulated using PC detector with limited energy resolution, and improvements with K-edge filtered x-rays were quantified.

RESULTS

The K-edge filters with atomic numbers from 56 to 71 and K-edge energies 37.4-63.4 keV, respectively, can be used for tube voltages from 60 to 150 kVp, respectively. For a particular tube voltage of 120 kVp, the Gd and Ho were the optimal filter materials to achieve highest SNR. For a particular K-edge filter of Gd and tube voltage of 120 kVp, the filter thickness 0.6 mm provided maximum SNR for considered imaging applications. While K-edge filtration improved SNR of CaCO3 and iodine by 41% and 36%, respectively, in DE subtracted images, it did not deteriorate SNR in general images. For x-ray imaging with nonideal PC detector, the positive effect of the K-edge filter was increased when FWHM energy resolution was degraded, and maximum improvement was at 60% FWHM.

CONCLUSIONS

This study has shown that K-edge filtered x-rays can provide substantial improvements of material selective PC x-ray and CT imaging for nearly all imaging applications using 60-150 kVp tube voltages. Potential limitations such as tube load, beam hardening, and availability of filter material were shown to not be critical.

摘要

目的

在光子计数(PC)X射线成像和计算机断层扫描(CT)中,利用具有合适K边能量的X射线滤波器可将宽X射线谱分为两部分,这有助于提高物质分解能力。最近的实验研究表明,使用K边滤波X射线时,PC CT在物质分解方面有显著改善。本研究的目的是通过全面的模拟研究对K边滤波方法进行进一步探究。

方法

研究从以下几个方面展开:(1)针对特定成像配置优化K边滤波器;(2)K边滤波器参数对物质分解的影响;(3)K边滤波器在能量区间分离、管负载和射线束质量之间的权衡;(4)使用K边滤波器改善双能(DE)减影图像时普通(未减影)图像的质量;(5)当PC探测器能量分辨率有限时,K边滤波X射线的改善情况。模拟了厚度为15 cm和30 cm的包含碘、碳酸钙和软组织对比材料的软组织体模的PC X射线图像。使用具有不同原子序数和厚度的K边滤波器,在普通图像和物质分解图像中确定了对比元素的信噪比(SNR)。确定了滤波器原子序数和滤波器厚度对能量分离因子和SNR的影响。确定了使用K边滤波器时管负载和半值层的边界条件。还使用能量分辨率有限的PC探测器模拟了物质分解图像,并对K边滤波X射线的改善情况进行了量化。

结果

原子序数分别为56至71且K边能量分别为37.4 - 63.4 keV的K边滤波器,可分别用于60至150 kVp的管电压。对于120 kVp的特定管电压,钆(Gd)和钬(Ho)是实现最高SNR的最佳滤波材料。对于钆的特定K边滤波器和120 kVp的管电压,0.6 mm的滤波器厚度为所考虑的成像应用提供了最大SNR。虽然K边滤波在DE减影图像中分别将碳酸钙和碘的SNR提高了41%和%,但在普通图像中并未降低SNR。对于使用非理想PC探测器的X射线成像,当半高宽(FWHM)能量分辨率降低时,K边滤波器的积极效果增强,在FWHM为%时改善最大。

结论

本研究表明,对于几乎所有使用60 - 150 kVp管电压的成像应用,K边滤波X射线可显著改善物质选择性PC X射线和CT成像。管负载、射线硬化和滤波材料可用性等潜在限制并非关键因素。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验