摘要
To solve the problems associated with low resolution and high computational effort infinite time,this paper proposes a fast forward modeling method for muon energy loss transmission tomography based on a model voxelization energy loss projection algorithm.First,the energy loss equation for muon transmission tomography is derived from the Bethe–Bloch formula,and the imaging region is then dissected into several units using the model voxelization method.Thereafter,the three-dimensional(3-D)imaging model is discretized into parallel and equally spaced two-dimensional(2-D)slices using the model layering method to realize a dimensional reduction of the 3-D volume data and accelerate the forward calculation speed.Subsequently,the muon energy loss transmission tomography equation is discretized using the ray energy loss projection method to establish a set of energy loss equations for the muon penetration voxel model.Finally,the muon energy loss values at the outgoing point are obtained by solving the projection coefficient matrix of the ray length-weighted model,achieving a significant reduction in the number of muons and improving the computational efficiency.A comparison of our results with the simulation results based on the Monte Carlo method verifies the accuracy and effectiveness of the algorithm proposed in this paper.The metallic mineral identification tests show that the proposed algorithm can quickly identify high-density metallic minerals.The muon energy loss response can accurately identify the boundary of the anomalies and their spatial distribution characteristics.
为了克服传统μ子成像技术在有限时间内成像分辨率低、计算量大的问题,本文提出了基于模型体素化能损投射算法的μ子能损透射成像快速正演方法。首先从Bethe-Bloch公式出发,推导了μ子透射成像满足的能损方程,利用模型体素化方法将成像区域剖分成若干个单元,再通过模型分层方法,将三维成像模型离散化为平行等距的二维切片,实现对三维体数据的维数降阶,加快正演速度。然后,通过射线能损投射法对μ子能损成像控制方程进行离散,建立μ子穿透体素模型的能损方程组。最后,通过求解射线长度加权模型的投影系数矩阵得到出射点处μ子能损解,实现μ子数目计算量的大幅度降低,提高计算效率。通过与基于蒙特卡罗方法的仿真结果对比,验证本文算法的准确性和有效性。金属矿识别测试表明本文算法可快速识别高密度金属矿物。正演计算的μ子能损响应具有准确识别异常体的边界及其空间展布特征的能力。
基金
supported by the National Key Research and Development Project of China(2016YFC0303104)
the National Natural Science Foundation of China(41304090)。
