Lo J Y, Floyd C E, Baker J A, Ravin C E
Department of Radiology, Duke University Medical Center, Durham, North Carolina 27710.
Med Phys. 1993 Jul-Aug;20(4):965-73. doi: 10.1118/1.596978.
An adaptive linear element (Adaline) was developed to estimate the two-dimensional scatter exposure distribution in digital portable chest radiographs (DPCXR). DPCXRs and quantitative scatter exposure measurements at 64 locations throughout the chest were acquired for ten radiographically normal patients. The Adaline is an artificial neural network which has only a single node and linear thresholding. The Adaline was trained using DPCXR-scatter measurement pairs from five patients. The spatially invariant network would take a portion of the image as its input and estimate the scatter content as output. The trained network was applied to the other five images, and errors were evaluated between estimated and measured scatter values. Performance was compared against a convolution scatter estimation algorithm. The network was evaluated as a function of network size, initial values, and duration of training. Network performance was evaluated qualitatively by the correlation of network weights to physical models, and quantitatively by training and evaluation errors. Using DPCXRs as input, the network learned to describe known scatter exposures accurately (7% error) and estimate scatter in new images (< 8% error) slightly better than convolution methods. Regardless of size and initial shape, all networks adapted into radial exponentials with magnitude of 0.75, perhaps implying an ideal point spread function and average scatter fraction, respectively. To implement scatter compensation, the two-dimensional scatter distribution estimated by the neural network is subtracted from the original DPCXR.
开发了一种自适应线性元件（Adaline）来估计数字便携式胸部X光片（DPCXR）中的二维散射曝光分布。为10名胸部X光检查正常的患者采集了DPCXR以及胸部64个位置的定量散射曝光测量值。Adaline是一种人工神经网络，只有一个节点和线性阈值。使用来自5名患者的DPCXR - 散射测量对训练Adaline。空间不变网络将图像的一部分作为输入，并估计散射含量作为输出。将训练好的网络应用于其他五幅图像，并评估估计散射值和测量散射值之间的误差。将性能与卷积散射估计算法进行比较。根据网络大小、初始值和训练持续时间对网络进行评估。通过网络权重与物理模型的相关性对网络性能进行定性评估，并通过训练和评估误差进行定量评估。以DPCXR作为输入，该网络学会了准确描述已知的散射曝光（误差7%），并且在估计新图像中的散射方面（误差<8%）比卷积方法略好。无论大小和初始形状如何，所有网络都适应为幅度为0.75的径向指数，这可能分别意味着理想的点扩散函数和平均散射分数。为了实现散射补偿，从原始DPCXR中减去神经网络估计的二维散射分布。
