在MATLAB环境下对所研究的图像重建用RBF神经网络进行训练，并通过有限元法获得训练所需要的训练样本集。
The RBF neural networks for image reconstruction were trained in MATLAB environment. The training samples were obtained by using finite element method.
该文研究了岩石样本孔隙和颗粒二维图象三维重建的一种新方法。
This paper presents a new method for reconstructing pores and particles from 2D images of stone samples.
通过样本实验研究对所提出的校正算法进行了验证，通过比较重建图像的信噪比对该算法的效果进行了评估。
The calibration algorithm is examined through phantom studies and evaluated by comparing the artifacts and noise in reconstructed images.
通过样本实验研究对所提出的校正算法进行了验证，通过比较重建图像的信噪比对该算法的效果进行了评估。
The calibration algorithm is examined through phantom studies and evaluated by comparing the artifacts and noise in reconstructed images.
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