Dept. of Radiol., Duke Univ., Durham, NC.
IEEE Trans Med Imaging. 1991;10(3):485-7. doi: 10.1109/42.97600.
An artificial neural network has been developed to reconstruct quantitative single photon emission computed tomographic (SPECT) images. The network is trained with an ideal projection-image pair to learn a shift-invariant weighting (filter) for the projections. Once trained, the network produces weighted projections as a hidden layer when acquired projection data are presented to its input. This hidden layer is then backprojected to form an image as the network output. The learning algorithm adjusts the weighting coefficients using a backpropagation algorithm which minimizes the mean squared error between the ideal training image and the reconstructed training image. The response of the trained network to an impulse projection resembles the ramp filter typically used with backprojection, and reconstructed images are similar to filtered backprojection images.
已经开发出一种人工神经网络，用于重建定量单光子发射计算机断层扫描（SPECT）图像。该网络使用理想的投影图像对进行训练，以学习投影的平移不变加权（滤波器）。一旦经过训练，当获取的投影数据被呈现给其输入时，网络会在隐藏层中生成加权投影。然后，该隐藏层通过反向投影形成网络输出的图像。学习算法使用反向传播算法调整加权系数，该算法最小化理想训练图像和重建训练图像之间的均方误差。训练后的网络对脉冲投影的响应类似于通常与反向投影一起使用的斜坡滤波器，并且重建图像类似于滤波反向投影图像。
