Abstract：
:An artificial neural network using input data derived from attenuation measurements was trained to generate spectral profiles (relative number of photons versus energy). Once the relative spectral distribution is reconstructed, absolute spectra (number of photons per unit exposure spectral distribution is reconstructed, absolute spectra (number of photons per unit exposure versus energy) can be calculated. A neural network was trained on spectra generated mathematically using the Birch-Marshall model, combined with attenuation data, calculated from the spectra by numerical integration. Whereas attenuation data can be calculated in a straightforward manner from the x-ray spectra, the reverse is not true. Several neural networks were successfully taught to reconstruct the spectra, given the attenuation data. The networks were tested using kV/inherent filtration combinations that were not in the training set, and the performance of the reconstruction was excellent. Noise in the attenuation data was simulated to test the effects of noise propagation in the reconstruction. The effects of network architecture and data averaging on noise propagation were investigated. Experimentally determined spectral data complied by Fewell were also used to train a neural network, and the results of the reconstruction were also found to be excellent.
journal_name
Med Physjournal_title
Medical physicsauthors
Boone JMdoi
10.1118/1.596495subject
Has Abstractpub_date
1990-07-01 00:00:00pages
647-54issue
4eissn
0094-2405issn
2473-4209journal_volume
17pub_type
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