Computed tomography of x-ray images using neural networks

Lloyd G. Allred; Martin H. Jones; Matthew J. Sheats; Anthony W. Davis

doi:10.1117/12.380600

30 March 2000 Computed tomography of x-ray images using neural networks

Lloyd G. Allred, Martin H. Jones, Matthew J. Sheats, Anthony W. Davis

Proceedings Volume 4055, Applications and Science of Computational Intelligence III; (2000) https://doi.org/10.1117/12.380600
Event: AeroSense 2000, 2000, Orlando, FL, United States

Abstract

Traditional 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 FB has a fundamental flaw. Gibbs phenomena states any Fourier reconstruction will produce errors in the vicinity of all discontinuities, and that the error will equal 28 percent of the discontinuity. A number of years back, one of the authors proposed a biological perception model whereby biological neural networks perceive 3D 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 sued to update the erroneous internal values. The process is repeated until the error signal no longer decrease. It was soon realized that the same method could be used to obtain CT 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 FB 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.

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Lloyd G. Allred, Martin H. Jones, Matthew J. Sheats, and Anthony W. Davis "Computed tomography of x-ray images using neural networks", Proc. SPIE 4055, Applications and Science of Computational Intelligence III, (30 March 2000); https://doi.org/10.1117/12.380600

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KEYWORDS

Neural networks

X-rays

Glasses

X-ray imaging

Signal processing

Sensors

Fourier transforms

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