WO2019200959A1 - 一种基于神经网络的近红外光谱断层成像重建方法 - Google Patents
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- the artificial neural network which originated in the 1940s, is a research hotspot in the field of artificial intelligence in recent years. It simulates the human brain neuron network, establishes a simple model, forms different networks according to different connection methods and completes various information processing tasks.
- BP neural network is the most widely used neural network form. BP neural network was first discovered independently by David Runelhart, Geoffrey Hinton, Ronald W-llians and David Parker in the mid-1980s. It has good nonlinear mapping ability, self-learning and self-adaptive ability, generalization ability, and fault tolerance, so it is widely used in function approximation, pattern recognition, classification, data compression and many other aspects.
- the optical parameter distribution is known, combined with the finite element method and the boundary measurement value ⁇ is solved according to the approximate transmission equation of light.
- ⁇ is used as the input x of the BP neural network
- the optical parameter distribution is the output y of the network.
- the structure of the BP neural network is shown in Figure 1.
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Abstract
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Claims (2)
- 一种基于神经网络的近红外光谱断层成像重建方法,其特征在于:在玻尔兹曼辐射传输方程中,光的传输过程被看作是光子在介质中的吸收与散射过程,光与组织的相互作用由吸收系数、散射系数以及反应散射分布的相位函数决定,并在传输中只考虑光的粒子性,不考虑光的波动性,因此也不考虑与光的波动性相关的偏振及干涉现象,只追踪光的能量传输;求解光在组织中的能量扩散近似方程:c表示光在组织中的传输速度;t表示时间;r表示坐标位置向量;κ为散射系数;μ a为吸收系数,Φ(r,t)表示光子密度分布;q 0(r,t)表示光源;由于近红外光谱断层成像假定光源为不受时间影响的各项同性光源,因此不考虑时间对扩散方程的影响,采用连续波模式下的扩散近似方程:q 0(r)是各向同性的光源;Φ(r)是位置r处的光子密度分布;生物发光断层成像中,数学模型中还需要考虑到边界条件,当边界内外的介质折射率不相同,光子到达边界时会发生反射现象;在近红外光学断层成像中稳态扩散方程对应的边界条件是空气组织边界由指数失配的III型条件也称为Robin或混合边界条件表示,Robin边界条件是指在介质内的辐射总强度等于光子在边界被反射回介质的部分;该关系用以下等式描述:R n表示扩散传输内反射系数,n与边界内外光学折射系数偏差相关;已知光学参数分布,结合有限元法并根据光的近似传输方程求解出边界测量值Φ,将Φ作为BP神经网络的输入x,则光学参数分布为网络的输出y。
- 根据权利要求1所述的一种基于神经网络的近红外光谱断层成像重建方法,其特征在于:BP神经网络的训练分为两部分,分别是前向传播和反向传播;首先是前向传播过程;设BP网络的输入层、隐藏层和输出层分别有m、q和n个节点,输入层与隐藏层间权值为v ki,隐藏层与输出层间权值为w jk;输入层至隐 层、隐层至输出层的激活函数分别为f 1(·)和f 2(·),那么,隐藏层节点的输出z k为:输出层节点的输出y i为:接着是反向传播过程;首先定义均方误差函数(MSE)为损失函数;对于m个样本,全局均方误差为:利用最速下降法使全局误差变小,则权值变化量为:其中η为学习率;定义误差信号:同理可得隐层各节点的权值调整公式为:BP神经网络以上述公式(5)-(11)更新网络权重及偏置,直至误差满足要求或满足其他停止条件;网络输出即为光学吸收系数分布。
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CN117173343B (zh) * | 2023-11-03 | 2024-02-23 | 北京渲光科技有限公司 | 一种基于神经辐射场的重新照明方法及系统 |
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