Background:This work proposed a joint L1 and total variation (TV) regularized reconstruction method for X-ray fluorescence tomography (XFT), and investigated the performance of this method in quantitative imaging of gold nanoparticles (GNPs). Methods:We developed a dual-modality XFT/CT imaging system which consisted of a benchtop X-ray source, a translation/rotation stage, a silicon drift detector for X-ray fluorescence (XRF) detection, and a flat panel detector for transmission X-ray detection. A pencil-beam collimator was 3D printed with steel and employed in sample excitation. The sensitivity of the XFT imaging system was determined by imaging water phantoms with multiple inserts containing GNP solutions of various concentrations (0.02-0.16 wt.%). A joint L1 and total variation (TV) regularized algorithm was developed for XFT reconstruction, where the L1 regularization was used to reduce image artifacts and the TV regularization was used to preserve the shape of targets. Nonlinear conjugate gradient (NCG) descent algorithm with backtracking line search was adopted to solve the reconstruction problem. We compared the L1 + TV regularization method with filtered back projection (FBP), maximum likelihood expectation maximization (ML-EM), L1 regularization, and TV regularization methods. Contrast-to-noise ratio (CNR), Dice similarity coefficient (DSC) and localization error (LE) metrics were used to compare the performance of different methods. The CT and XFT imaging doses were also measured using EBT2 radiochromic films. Results:The 3D printed pencil-beam collimator shaped an excitation beam with a 2 mm full width at half maximum at the imaging isocenter. Based on the phantom imaging experiments, the joint L1 and TV regularization method performed better than FBP, ML-EM, L1 regularization and TV regularization methods, with higher localization accuracy (offset <0.6 mm), CNR and DSC values. Compared with CT, XFT with L1 + TV regularized reconstruction demonstrated higher sensitivity in GNP imaging, and could detect GNP at a concentration of 0.02 wt.% or lower. Moreover, there existed a significant linear correlation (R2>0.99) between the reconstructed and true GNP concentration. The estimated XFT imaging dose is about 41.22 cGy under current setting. Conclusions:The joint L1 + TV regularized reconstruction algorithm performed better in noise suppression and shape preservation. Using the L1 + TV regularized reconstruction, the XFT system is able to localize GNP targets with submillimeter accuracy and quantify GNP distribution at a concentration of 0.02 wt.% or lower.

译文

背景:这项工作提出了一种用于X射线荧光层析成像(XFT)的联合L1和总变异(TV)正则化重构方法,并研究了该方法在金纳米颗粒(GNP)定量成像中的性能。
方法:我们开发了一种双模态XFT / CT成像系统,该系统由台式X射线源,平移/旋转台,用于X射线荧光(XRF)检测的硅漂移检测器和用于透射的平板检测器组成X射线检测。笔形光束准直器用钢进行3D打印,并用于样品激发。 XFT成像系统的灵敏度是通过对带有多个插入物的水体模进行成像来确定的,这些插入物包含各种浓度(0.02-0.16 wt。%)的GNP溶液。针对XFT重建,开发了联合的L1和总变异(TV)正则化算法,其中L1正则化用于减少图像伪影,而TV正则化用于保留目标的形状。采用带回溯线搜索的非线性共轭梯度下降算法来解决重构问题。我们将L1 TV正则化方法与滤波后向投影(FBP),最大似然期望最大化(ML-EM),L1正则化和TV正则化方法进行了比较。对比噪声比(CNR),骰子相似系数(DSC)和定位误差(LE)指标用于比较不同方法的性能。还使用EBT2射线变色胶片测量了CT和XFT成像剂量。
结果:3D打印的笔形光束准直器塑造了一个激发光束,在成像等角点处的半峰全宽为2 mm。基于幻像成像实验,联合L1和TV正则化方法的性能优于FBP,ML-EM,L1正则化和TV正则化方法,具有更高的定位精度(偏移<0.6 mm),CNR和DSC值。与CT相比,采用L1 TV常规重建的XFT在GNP成像中显示出更高的灵敏度,并且可以检测到0.02 wt。%或更低的GNP。此外,重建的和实际的GNP浓度之间存在显着的线性相关性(R2> 0.99)。在当前设置下,估计的XFT成像剂量约为41.22 cGy。
结论:联合L1 TV正则化重构算法在抑制噪声和保持形状方面表现更好。使用L1 TV正则化重建,XFT系统能够以亚毫米级精度定位GNP目标,并以0.02 wt。%或更低的浓度量化GNP分布。

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