Kerr J P, Bartlett E B
Adaptive Computing Laboratory, Iowa State University, Ames 50011-2241, USA.
J Digit Imaging. 1995 Aug;8(3):116-26. doi: 10.1007/BF03168085.
An artificial neural network (ANN) trained on high-quality medical tomograms or phantom images may be able to learn the planar data-to-tomographic image relationship with very high precision. As a result, a properly trained ANN can produce comparably accurate image reconstruction without the high computational cost inherent in some traditional reconstruction techniques. We have previously shown that a standard backpropagation neural network can be trained to reconstruct sections of single photon emission computed tomography (SPECT) images based on the planar image projections as inputs. In this study, we present a method of deriving activation functions for a backpropagation ANN that make it readily trainable for full SPECT image reconstruction. The activation functions used for this work are based on the estimated probability density functions (PDFs) of the ANN training set data. The statistically tailored ANN and the standard sigmoidal backpropagation ANN methods are compared both in terms of their trainability and generalization ability. The results presented show that a statistically tailored ANN can reconstruct novel tomographic images of a quality comparable with that of the images used to train the network. Ultimately, an adequately trained ANN should be able to properly compensate for physical photon transport effects, background noise, and artifacts while reconstructing the tomographic image.
在高质量医学断层图像或体模图像上训练的人工神经网络（ANN）或许能够以非常高的精度学习平面数据与断层图像之间的关系。因此，经过适当训练的人工神经网络能够在不具备某些传统重建技术所固有的高计算成本的情况下，生成精度相当的图像重建结果。我们之前已经表明，可以训练标准反向传播神经网络，以基于平面图像投影作为输入来重建单光子发射计算机断层扫描（SPECT）图像的切片。在本研究中，我们提出了一种为反向传播人工神经网络推导激活函数的方法，使其能够轻松地用于全SPECT图像重建的训练。用于这项工作的激活函数基于人工神经网络训练集数据的估计概率密度函数（PDF）。对经过统计定制的人工神经网络和标准S形反向传播人工神经网络方法在可训练性和泛化能力方面进行了比较。给出的结果表明，经过统计定制的人工神经网络能够重建出质量与用于训练网络的图像相当的新型断层图像。最终，经过充分训练的人工神经网络在重建断层图像时应该能够适当地补偿物理光子传输效应、背景噪声和伪影。
