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A New Image Reconstruction Approach to Optical Computed Tomography Based on BP Neural Network

机译:基于BP神经网络的光学计算断层扫描的新图像重构方法

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A new image reconstruction approach to optical computed tomography is proposed in which a BP neural network is used to express the nonlinear relation between the change of optical properties inside the biology tissue and photons distributing change around the surface. A modeling and simulation tool named Femlab and finite element method has been tested wherein the forward model on a basis of the diffusion equation. Based on the forward problem, a BP neural network was established to solve the inverse problem. Thus, the position and its corresponding optical properties of tissue change could be recognized by the network. New approach was suitable for clinical application for its fast reconstruct characteristic.
机译:提出了一种新的图像重建方法,其中提出了一种BP神经网络,用于表达在生物组织内的光学性质的变化与围绕表面的光子的变化之间的非线性关系。 已经测试了名为FemlAb和有限元方法的建模和仿真工具,其中基于扩散方程的前向模型。 基于前向问题,建立了一个BP神经网络来解决逆问题。 因此,可以通过网络识别组织变化的位置及其相应的光学性质。 新方法适用于其快速重建特征的临床应用。

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