Computed tomography of x-ray images using neural networks

Traditional Computed Tomography (CT) reconstruction is done using the technique of Filtered Backprojection. While this technique is widely employed in industrial and medical applications, it is not generally understood that Filtered Backprojection has a fundamental flaw. Gibbs phenomena states any Fourier reconstruction (which includes Filtered Backprojection) will produce errors in the vicinity of all discontinuities, and that the error will equal 28% of the discontinuity. A number of years back, one of the authors proposed a biological perception model whereby biological neural networks perceive 3-dimensional images from stereo vision. The perception model proports an internal hard-wired neural network which emulates the external physical process. A process is repeated whereby erroneous unknown internal values are used to generate an emulated signal with is compared to external sensed data, generating an error signal. Feedback from the error signal is then used to update the erroneous internal values. The process is repeated until the error signal no longer decreases. It was soon realized that the same method could be used to obtain Computed Tomography from x-rays without having to do Fourier transforms. Neural networks have the additional potential for handling non-linearities and missing data. The technique has been applied to some coral images, collected at the Los Alamos high-energy x-ray facility. The initial images show considerable promise, in some instances showing more detail than the Filtered Backprojection images obtained from the same data. Although routine production using this new method would require a massively parallel computer, the method shows promise, especially where refined detail is required.