Munley M T, Floyd C E, Bowsher J E, Coleman R E
Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710.
Med Phys. 1994 Dec;21(12):1889-99. doi: 10.1118/1.597167.
A spatially variant technique for quantitative single photon emission computed tomographic (SPECT) image reconstruction using an artificial neural network (ANN) is presented. This network was developed to simultaneously compensate for collimator, attenuation, and scatter effects during the reconstruction process. The network was trained using a supervised scheme which implemented the generalized delta rule. Training ended once the mean-squared error (MSE) between the ideal and reconstructed images converged to a minimum. After training, the ANN weights were held constant and could be used to reconstruct source distributions other than those used while training. In the absence of noise when only collimator effects were present, reconstruction of a Hoffman brain phantom had a 89% reduction in MSE compared to standard filtered backprojection. When collimator-and-attenuation and collimator-attenuation-and-scatter trials were tested against filtered backprojection with Chang attenuation compensation, the corresponding ANN reconstructions demonstrated 85% and 86% decreases in MSE, respectively. With noise present, and with standard noise reduction filters implemented prior to reconstruction, the ANN reconstructions displayed up to a 50% decrease in MSE compared to filtered backprojection reconstructions for 200,000 count data. These results demonstrate that an ANN can be used to reconstruct SPECT images with improved quantitative accuracy.
提出了一种使用人工神经网络（ANN）进行定量单光子发射计算机断层扫描（SPECT）图像重建的空间可变技术。开发此网络是为了在重建过程中同时补偿准直器、衰减和散射效应。该网络使用实施广义增量规则的监督方案进行训练。一旦理想图像和重建图像之间的均方误差（MSE）收敛到最小值，训练即结束。训练后，ANN权重保持不变，可用于重建训练时使用的源分布以外的其他源分布。在仅存在准直器效应且无噪声的情况下，与标准滤波反投影相比，霍夫曼脑模型的重建MSE降低了89%。当将准直器和衰减以及准直器-衰减和散射试验与采用张衰减补偿的滤波反投影进行测试时，相应的ANN重建分别显示MSE降低了85%和86%。在存在噪声且在重建前实施标准降噪滤波器的情况下，对于200,000计数数据，与滤波反投影重建相比，ANN重建显示MSE降低了50%。这些结果表明，ANN可用于重建具有更高定量准确性的SPECT图像。
