Atmospheric transmission algorithm for pulsed X-rays from high-altitude nuclear detonations based on scattering correction

In high-altitude nuclear detonations,the proportion of pulsed X-ray energy can exceed 70%,making it a specific monitoring signal for such events.These pulsed X-rays can be captured using a satellite-borne X-ray detector following atmospheric transmission.To quantitatively analyze the effects of different satellite detection altitudes,burst heights,and transmission angles on the physical processes of X-ray transport and energy fluence,we developed an atmospheric transmission algorithm for pulsed X-rays from high-altitude nuclear detonations based on scattering correction.The proposed method is an improvement over the traditional analytical method that only computes direct-transmission X-rays.The traditional analytical method exhibits a maximum relative error of 67.79% compared with the Monte Carlo method.Our improved method reduces this error to within 10% under the same conditions,even reaching 1% in certain scenarios.Moreover,its computation time is 48,000 times faster than that of the Monte Carlo method.These results have important theoretical significance and engineering application value for designing satellite-borne nuclear detonation pulsed X-ray detectors,inverting nuclear detonation source terms,and assessing ionospheric effects.

Emission-Based Scatter Correction in SPECT Imaging

Scatter correction in single photon emission computed tomography （SPECT） has been focused on either using multiple-window acquisition technique or the scatter modeling technique in iterative image reconstruction. We propose a technique that uses ：only the emission data for scatter correction in SPECT. We assume that the scatter data can be approximated by convolving the primary data with a scatter kernel followed by the normalization using the scatter-to-primary ratio （SPR）, Since the emission data is the superposition of the primary data and the scatter data, the convolution normalization process approximately results in the sum of the scatter data and a convolved version of scatter data with the kernel. By applying a proper scaling factor, we can make the estimation approximately equal to or less than the scatter data anywhere in the projection domain. Phantom and patient cardiac SPECT studies show that using the proposed emission-based scatter estimation can effectively reduce the scatter-introduced background in the reconstructed images. And additionally, the computational time for scatter correction is negligible as compared to no scatter correction in iterative image reconstruction.

Scatter correction method for cone-beam CT based on interlacing-slit scan

Cone-beam computed tornography （CBCT） has the notable features of high efficiency and high precision, and is widely used in areas such as medical imaging and industrial non-destructive testing. However, the presence of the ray scatter reduces the quality of CT images. By referencing the slit collimation approach, a scatter correction method for CBCT based on the interlacing-slit scan is proposed. Firstly, according to the characteristics of CBCT imaging, a scatter suppression plate with interlacing slits is designed and fabricated. Then the imaging of the scatter suppression plate is analyzed, and a scatter correction Calculation method for CBCT based on the image fusion is proposed, which can splice out a complete set of scatter suppression projection images according to the interlacing-slit projection images of the left and the right imaging regions in the scatter suppression plate, and simultaneously complete the scatter correction within the fiat panel detector （FPD）. Finally, the overall process of scatter suppression and correction is provided. The experimental results show that this method can significantly improve the clarity of the slice images and achieve a good scatter correction.

An effective scatter correction method based on single scatter simulation for a 3D whole-body PET scanner

Hamamatsu SHR74000 is a newly designed full three-dimensional （3D） whole body positron emission tomography （PET） scanner with small crystal size and large field of view （FOV）.With the improvement of sensitivity,the scatter events increase significantly at the same time,especially for large objects.Monte Carlo simulations help us to understand the scatter phenomena and provide good references for scatter correction. In this paper,we introduce an effective scatter correction method based on single scatter simulation for the new PET scanner,which accounts for the full 3D scatter correction.With the results from Monte Carlo simulations,we implement a new scale method with special concentration on scatter events from outside the axial FOV and multiple scatter events.The effects of scatter correction are investigated and evaluated by phantom experiments;the results show good improvements in quantitative accuracy and contrast of the images,even for large objects.

Analytical Calculation of the Compton Single Scatter Component of Pencil Beam Scatter Kernel for Scatter Correction in kV Cone Beam CT (kV-CBCT)

The accuracy of conventional superposition or convolution methods for scatter correction in kV-CBCT is usually compromised by the spatial variation of pencil-beam scatter kernel (PBSK) due to finite size, irregular external contour and heterogeneity of the imaged object. This study aims to propose an analytical method to quantify the Compton single scatter (CSS) component of the PBSK, which dominates the spatial distribution of total scatter assuming that multiple scatter can be estimated as a constant background and Rayleigh scatter is the secondary source of scatter. The CSS component of PBSK is the line integration of scatter production by incident primary photons along the beam line followed by the post-scattering attenuation as the scattered photons traverse the object. We propose to separate the object-specific attenuation term from the line integration and equivalently replace it with an average value such that the line integration of scatter production is object independent but only beam specific. We derived a quartic function formula as an approximate solution to the spatial distribution of the unattenuated CSS component of PBSK. The “effective scattering center” is introduced to calculate the average attenuation. The proposed analytical framework to calculate the CSS was evaluated using parameter settings of the On-Board Imager kV-CBCT system and was found to be in high agreement with the reference results. The proposed method shows highly increased computational efficiency compared to conventional analytical calculation methods based on point scattering model. It is also potentially useful for correcting the spatial variant PBSK in adaptive superposition method.

Improve the Prediction Accuracy of Apple Tree Canopy Nitrogen Content through Multiple Scattering Correction Using Spectroscopy

Method: Use Multiple Scattering Correction to eliminate the interference of scattering on spectrum in the process of field measurement so as to improve the accuracy of prediction model of tree canopy nitrogen content. Apple trees in Qixia of Yantai City were taken as the test material. The spectral reflectivity of apple tree canopy went through the First Derivative (FD) and Multiple Scattering Correction (MSC) plus first derivative, respectively. The correlation coefficients were calculated between spectral reflectivity and nitrogen content. The Support Vector Machine (SVM) method was used to establish the prediction model. The result indicates that the MSC pre-processing can improve the correlation between spectral reflectivity and nitrogen content. The SVM model with MSC + FD pre-processing was a good way to predict the nitrogen content. The calibration R2 of the model was 0.746;the validation R2 was 0.720;and its RMSE was 0.452 g·kgˉ1. MSC can commendably eliminate scattering error to improve the prediction accuracy of prediction model.

Scattering correction method for panel detector based cone beam computed tomography system

A scattering correction method for a panel detector based cone beam computed tomography system is presented. First, the x-ray spectrum of the system is acquired by using the Monte Carlo simulation method. Secondly, scattered photon distribution is calculated and stored as correction matrixes by using the Monte Carlo simulation method according to scanned objects and computed tomography system specialties. Thirdly, scattered photons are removed from projection data by correction matrixes. A comparison of reconstruction image between before and after scattering correction demonstrates that the scattering correction method is effective for the panel detector based cone beam computed tomography system.

Improvement of scattering correction for in situ coastal and inland water absorption measurement using exponential fitting approach

The absorption coefficient of water is an important bio-optical parameter for water optics and water color remote sensing. However, scattering correction is essential to obtain accurate absorption coefficient values in situ using the nine-wavelength absorption and attenuation meter AC9. Establishing the correction always fails in Case 2 water when the correction assumes zero absorption in the near-infrared(NIR) region and underestimates the absorption coefficient in the red region, which affect processes such as semi-analytical remote sensing inversion. In this study, the scattering contribution was evaluated by an exponential fitting approach using AC9 measurements at seven wavelengths(412, 440, 488, 510, 532, 555, and 715 nm) and by applying scattering correction. The correction was applied to representative in situ data of moderately turbid coastal water, highly turbid coastal water, eutrophic inland water, and turbid inland water. The results suggest that the absorption levels in the red and NIR regions are significantly higher than those obtained using standard scattering error correction procedures. Knowledge of the deviation between this method and the commonly used scattering correction methods will facilitate the evaluation of the effect on satellite remote sensing of water constituents and general optical research using different scatteringcorrection methods.

基于校正光谱序列融合的小麦腥黑穗病籽粒分类方法

针对小麦腥黑穗病轻度患病籽粒易与健康籽粒混淆,人工识别难度大的问题,将校正光谱序列融合技术与深度学习模型相结合,实现小麦腥黑穗病籽粒快速、精准分类。以健康、轻度患病、重度患病各300粒小麦籽粒的高光谱数据为样本,通过多元散射校正算法(MSC)和标准正态变换算法(SNV)对原始光谱进行预处理,并利用二维相关光谱法(2D-COS)分析SNV与MSC算法处理后的光谱之间的互补性。使用校正光谱序列融合技术将原始光谱、SNV预处理光谱与MSC预处理光谱三者进行融合得到序列融合光谱,以充分利用不同光谱预处理数据间的互补信息。最终,利用序列融合光谱数据建立基于ResNet 50算法的小麦腥黑病分类模型。试验结果表明,序列融合光谱ResNet 50模型总体准确率最高为93.89%,F1值为93.87%,分类性能优于单一预处理光谱建立的ResNet 50模型。为进一步评估模型分类效果,使用序列融合光谱分别建立偏最小二乘判别分析(PLS-DA)、支持向量机(SVM)以及集成学习算法模型随机森林(RF)与极端梯度提升树(XGBoost)模型,并进行对比,结果显示:SVM、PLS-DA、RF与XGBoost总体准确率分别为81.67%、84.44%、89.44%与90.55%,F1值分别为81.59%、84.04%、89.49%与90.59%,ResNet 50总体准确率与F1值优于传统光谱分析模型。因此,本研究表明校正光谱序列融合技术结合深度学习模型,能够实现对不同患病程度腥黑穗病籽粒的有效分类。

电镀污泥成分X荧光检测水分影响机制及校正方法研究

电镀污泥是金属加工、电子元器件制造等行业在废水处理过程中所产生含重金属危险废物。我国每年产生大量电镀污泥约1000万吨,因含有Zn、Cu、Fe、Ni、Cr等多种有价金属元素其资源化利用潜力巨大,同时大量污泥处置不当也会危害人体健康和污染环境。传统化学分析测试过程复杂周期长,不利于电镀污泥实时污染防控。能量色散X射线荧光(EDXRF)光谱法分析快速简便、费用低廉、可实现原位检测,然而电镀污泥样品含水率高、成分含量不稳定,影响测试结果的及时性和准确度,导致关键元素的资源转化效率难以保证、环境污染管控难度高。因此需要分析X荧光检测水分影响机制并探究水分校正方法,以提高EDXRF测试结果准确度。以不同含水率电镀污泥为对象,研究了水分对电镀污泥EDXRF测试过程中光谱本底、散射峰、目标元素特征峰的影响机制,通过目标元素特征峰与瑞利散射峰强度的比值、样品含水率ω0 wt%和目标元素含量Ci%建立水分校正模型,探究了Ca、Cr、Fe、Ni、Cu、Zn目标元素的水分校正方程中的实验修正因子。结果表明,水分抬高了光谱本底改变谱峰轮廓,对低能段、计数小的谱峰测试结果影响较大。含水率越高,散射峰净强度越大,瑞利散射线更能准确描述散射峰净强度和含水率的关系,目标元素特征峰与瑞利散射峰强度比值与含水率、目标元素含量能够准确符合线性关系。水分校正模型对Cr、Fe、Ni、Cu、Zn五种重金属校正结果准确度较高,校正值与基准值相关系数R2均大于0.95,RMSE均小于0.05,对Ca元素校正准确度较低R2为0.93,RMSE为1.046需增加相关校正因子进一步优化。该研究有望应用于再生铜行业电镀污泥处置现场测量的水分校正方法中,在提高电镀污泥资源利用效率的同时降低环境污染风险。

基于斜孔散射校正板的锥束X射线CT散射校正方法

锥束X射线CT和二维扇束、平行束CT系统相比具有扫描速度快、射线利用率高、重建图像轴向分辨率和水平分辨率一致等优点,是当前工业CT技术发展的重点。然而,由于散射线的存在,其成像质量受到影响。为了减小散射线对图像质量的影响,提出一种新的基于斜孔散射校正板的散射校正方法,对该方法的原理和实现进行了深入的研究,通过获取原始扫描数据以及斜孔散射校正板后的扫描数据,利用插值和平滑处理的方法获得散射场数据。然后,通过将原始数据减去散射场数据后进行重建,即可得到无散射的CT图像。通过与光栅式散射校正板校正方法进行对比,结果表明,该方法应用于涡轮叶片的锥束CT扫描结果校正,典型区域(叶片内冷却通道及叶片内壁)对比度噪声比分别提升了14.2%和56.8%,而光栅式散射校正板校正后,同一位置对比度噪声比分别仅提升了5.6%和27.6%,验证了基于斜孔散射校正板散射校正方法的优越性。

基于修正电磁散射系数的距离频域脉冲相干法SAR回波仿真

针对传统合成孔径雷达(Synthetic Aperture Radar,SAR)回波仿真方法无法同时满足仿真精度高、速度快的缺点,设计了一种利用距离历程修正电磁散射系数的距离频域脉冲相干法(Range Frequency Domain Pulse Coherence,RFPC)目标回波仿真方案。该方案在离散方位采样点处采用物理光学法计算关于目标电磁散射特性的二维电磁散射系数,利用天线相位中心与目标间的距离历程修正电磁散射系数的相位,根据脉冲信号3 dB波束宽度修正电磁散射系数的幅度,利用RFPC在距离频域进行目标电磁散射系数与SAR发射信号的乘积处理,获取目标的二维回波信号,采用RD成像算法进行成像处理得到目标的SAR仿真图像。利用该方案分别对金属球和舰船模型进行了回波仿真试验,并与RaySAR的仿真结果进行对比,验证了该方案的精确性与高效性。

海洋环境下摄像机图像去雾技术研究

针对海洋雾气环境下摄像机类设备图像对比度低、能见度差的问题,提出了一种基于图像前处理的单图融合增强去雾方法,首先对大气辉光分量进行校正,其次结合图像传感器R通道透射率高的特征利用小波融合技术提升图像细节,最后利用导向滤波器以进行图像去雾增强,通过实际浓雾环境下采集的图像对比处理实验验证了所提出方法的可行性、有效性。

Savitzky-Golay平滑结合多元散射校正应用于甲醛固定的深静脉血栓红外光谱数据预处理

为探究甲醛固定血栓样品红外光谱数据的最优预处理方案,应用傅里叶变换红外光谱(FTIR)技术,采集经甲醛固定的大鼠深静脉血栓组织红外光谱数据,选择偏最小二乘回归法建立红外光谱分析机器学习模型,测试Savitzky-Golay(S-G)平滑、基线校正、多元散射校正、标准正态变化、矢量标准化等常用的光谱预处理方式对光谱数据的处理效能。结果显示,大鼠深静脉血栓组织红外光谱的最优预处理方案为先采用S-G平滑,再进行多元散射校正,其中S-G平滑最佳参数组合是阶数为2、项数为3、点数为17。预处理后预测决定系数(R_(p)^(2))为0.948,预测均方根误差(RMSEP)为0.486 d,模型预测能力较原始光谱显著提升。

硝酸盐传感器在深海压力下的校准方法

研究表明,海水中溴化物的紫外吸收光谱受到深海压力的影响会影响海水中硝酸盐的测量准确度。现有的水下硝酸盐传感器已暴露出测量误差大,环境适应性差、无法应用于2000 m以下的深海探测等问题。因此,为了更准确地计算硝酸盐浓度,有必要开展深海压力对海水紫外吸收光谱的校正研究。实验室对模拟深海压力环境中的硝酸盐进行测试试验,提出了两种算法用来矫正压力下的紫外光谱,通过实验验证了这两种压力校正算法对硝酸盐计算的预测准确性。结果表明,多元散射校正-偏最小二乘法回归校正算法具有最好的测量效果,R^(2)为0.997,平均绝对误差(MAE)为1.294μmol/L,平均偏差误差(MBE)为0.037μmol/L。

锥束CT散射校正的平板探测研究

为了解决锥束CT在工业CT无损检测中所产生的散射问题,从而提升图像质量,本文提出了一种基于平板探测器的锥束CT散射硬件校正方法。该方法通过在平板探测器BSA上增加散射校正单元,对原始数据进行预处理,从而有效去除散射噪声,以减少散射对锥束CT图像的影响。实验结果表明,该方法能够显著提高锥束CT图像的清晰度和对比度,降低散射对图像质量的影响。

Burnup analysis for the pebble-bed fluoride-salt-cooled high-temperature reactor based on the energy-dependent elastic scattering cross-sectional model

To carry out accurate burnup calculations for a pebble-bed fluoride-salt-cooled high-temperature reactor,the energy-dependent cross-sectional model based on the Doppler broadening rejection correction method has been proposed to develop the energy-dependent elastic scattering cross-sectional model. In this study, the Monte Carlo continuous energy code PSG2/Serpent was used to examine the difference between the constant cross-sectional model and the energy-dependent cross-sectional model during burnup. For the cases analyzed in this study,numerical simulations show that the multiplication coefficient was improved by hundreds pcm and ^(239)Pu concentration was improved by approximately 1–2% during burnup when the energy-dependent elastic scattering crosssectional model is considered.

Analysis of multiple scattering from two-dimensional dielectric sea surface with iterative Kirchhoff approximation

An iterative method in the Kirchhoff approximation is proposed for high frequency multiple electromagnetic scattering from two-dimensional dielectric sea surface. The multiple interaction of the scattering field is characterized with the corrected electromagnetic currents of the wind-driven sea surface. The actual surface currents are approximated with the iterative solution of the corrected currents. A newly developed sea spectrum, Elfouhaily spectrum, is utilized to build the sea surface model. The shadowing correction is improved by the Dept-Buffer algorithm. The validity of the iterative Kirchhoff approximation is verified by the agreement of backscattering coefficients with the measured data.

模糊K-Harmonic-Kohonen网络的FTIR光谱数据聚类分析

食品的品种不同则其含有营养成分和功效存在差异,得到的傅里叶变换红外光谱也存在差异。为了准确的实现品种分类,设计了一种将傅里叶变换红外光谱与模糊聚类分析方法相结合的品种鉴别方法。在模糊Kohonen聚类网络(FKCN)基础上将模糊K调和聚类(FKHM)引入到Kohonen聚类网络的学习速率和更新策略中,提出了模糊K-Harmonic-Kohonen网络(FKHKCN)算法。FKHKCN利用模糊C均值(FCM)聚类的模糊隶属度计算其学习速率,以FKHM的聚类中心为基础通过推导计算得到FKHKCN的聚类中心,可以解决模糊Kohonen聚类网络方法对于初始类中心敏感而导致聚类结果不稳定的问题。FKHKCN作为一种模糊聚类算法,可实现傅里叶变换红外光谱数据的聚类分析。采用三种数据集:(1)采集产自四川的三种茶叶(优质和劣质的乐山竹叶青以及峨眉山毛峰)作为实验样本,样本总数为96。(2)两个品种(robusta和arabica)的咖啡样本。(3)三个品种(鸡肉、猪肉和火鸡)的肉类样本。首先对三个光谱数据集进行预处理,利用多元散射校正降低茶叶样本原始光谱数据集的散射影响,使用Savitzky-Golay减少噪声对肉类和咖啡这两个光谱数据集的影响。再利用主成分分析将高维的三种光谱数据集压缩至低维。然后采用线性判别分析进行特征提取,将光谱数据投影到求得的鉴别向量上。最后分别采用FCM,FKCN和FKHKCN对茶叶、肉类和咖啡进行判别。最终结果如下:FCM,FKCN和FKHKCN对茶叶品种的聚类准确率分别为90.91%,90.91%和93.94%;对肉类品种的聚类准确率分别为90.83%,0.00%和92.50%;对咖啡品种的聚类准确率分别为89.17%,89.17%和90.83%。以上实验结果表明:采用傅里叶红外光谱技术结合主成分分析、线性判别分析和FKHKCN的方法能够较有效地对食品的品种进行鉴别,且鉴别准确率比FCM和FKCN更高,聚类结果更稳定。

基于深度学习的医用锥形束CT图像散射伪影校正的研究进展

医用计算机断层扫描成像系统中,X射线与物体相互作用产生的康普顿散射光子严重影响了图像质量,尤其在锥形束计算机断层扫描和多层探测器系统中。目前已有许多散射伪影校正方法,归纳为3类:硬件校正、软件校正、软硬件混合校正方法,但近年随着计算机计算能力的提高以及深度学习在医学图像处理领域的发展,出现了一些新的散射校正方法。本文首先介绍传统校正方法;然后详细介绍基于深度学习方法进行散射伪影校正,并将其分为基于图像域和基于投影域的深度学习方法,以及对不同的深度学习网络在散射伪影校正中的应用进行讨论;最后展望深度学习在多源计算机断层扫描技术中的应用前景。