Regularization technique for restoration of x-ray fluoroscopic images

Robert A. Close; James Stuart Whiting

doi:10.1117/12.224156

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9 October 1995 Regularization technique for restoration of x-ray fluoroscopic images

Robert A. Close, James Stuart Whiting

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Proceedings Volume 2570, Experimental and Numerical Methods for Solving Ill-Posed Inverse Problems: Medical and Nonmedical Applications; (1995) https://doi.org/10.1117/12.224156
Event: SPIE's 1995 International Symposium on Optical Science, Engineering, and Instrumentation, 1995, San Diego, CA, United States

Abstract

X-ray fluoroscopic images are degraded by x-ray scattering within the subject and veiling glare in the image intensifer. Densitometric accuracy is further degraded by beam hardening. Scattering, veiling glare, or both are modeled as a blurred representation of the primary image plus an offset. If the image can be represented by convolution of the primary with a known response function, then an estimate of the primary component of the image can be computed by deconvolution. We describe a technique for estimating a parameterized response function so that a good estimate of the subject density profile can be recovered even if the response function parameters are not known in advance. This is important for x-ray imaging (particularly fluoroscopy) since the acquisition parameters are variable. A reference object designed to be uncorrelated with the subject is imaged in superposition with the subject. The unknown parameters are then adjusted to minimize a cost function subject to the constraint that the correlation between the known reference density and the estimated subject density be zero. The method can be extended to include a correction for beam hardening.

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Robert A. Close and James Stuart Whiting "Regularization technique for restoration of x-ray fluoroscopic images", Proc. SPIE 2570, Experimental and Numerical Methods for Solving Ill-Posed Inverse Problems: Medical and Nonmedical Applications, (9 October 1995); https://doi.org/10.1117/12.224156

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