AN ARTIFICIAL NEURAL-NETWORK FOR ESTIMATING SCATTER EXPOSURES IN PORTABLE CHEST RADIOGRAPHY

An adaptive linear element (Adaline) was developed to estimate the two -dimensional scatter exposure distribution in digital portable chest r adiographs (DPCXR). DPCXRs and quantitative scatter exposure measureme nts at 64 locations throughout the chest were acquired for ten radiogr aphically 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 betwee n estimated and measured scatter values. Performance was compared agai nst a convolution scatter estimation algorithm. The network was evalua ted as a function of network size, initial values, and duration of tra ining. Network performance was evaluated qualitatively by the correlat ion of network weights to physical models, and quantitatively by train ing 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 m ethods. Regardless of size and initial shape, all networks adapted int o radial exponentials with magnitude of 0.75, perhaps implying an idea l point spread function and average scatter fraction, respectively. To implement scatter compensation, the two-dimensional scatter distribut ion estimated by the neural network is subtracted from the original DP CXR.