递变能量成像中最佳X射线管电压预测算法

X射线递变能量成像是依次获取复杂结构件在递变能量下的局部有效信息，并通过多谱融合获取完整结构信息。但是目前的能量选择主要以人工设定管电压步进为主，无法匹配检测对象的有效厚度变化率，成像效率及射线利用率较低。基于递变能量成像规律，提出一种最佳X射线管电压预测算法。该方法通过对检测物体进行变能量预扫描，提取图像序列中有效厚度(高质量区域)和临近厚度(预测区域)，建立有效厚度的图像灰度与管电压、X射线光谱之间的物理模型，及临近厚度灰度差与电压的函数模型，进而得到临近厚度最佳成像时的能量预测模型。通过模型求解，实现了能量的自适应预测。以不同厚度钢块为对象，利用该算法逐一预测各个厚度钢块最佳成像时的管电压，并与实际值对比。实验结果显示，在低能时可跨3~4 mm准确预测，高能时可跨7~10 mm预测，精度可以达到95%以上。

The Prediction Algorithm of the Optimal X-Ray Tube Voltage in Variable Energy Imaging

X-ray variable energy imaging can obtain the sectional information of complicated structural component successively, and get the whole information by multi-spectrum fusion. Now the energy parameters of X ray imaging mainly depend on man-made setting with the certain step voltage. However this modulation doesn’tmatch to the attenuation thickness variation of the object. Therefore, this paper proposes an optimum tube voltage prediction algorithm based on variable energy imaging. It extracts the effective thickness (ET) and near the effective thickness (NET) in the image sequences which are acquired by pre-scanning the detected object. Then it establishes a physical model between image gray, tube voltage and X ray spectrum. And the model of voltage and gray difference between the ET (high quality area) and NET (prediction area) is also established. On the basis of these two models, the optimal imaging energy forecasting model of NET is modeled. Then, solve the model and get the optimal voltage for NET. At last, by the experiment of the steel blocks with different thickness, testify this prediction algorithm. The results compared with the actual values showed that the prediction algorithm can accurately predict 3 or 4 mm at low voltage and 7 or 10 mm at high voltage. Prediction accuracy is over 95%.