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PubMed · 3496435

Interactive visual optimization of SPECT prereconstruction filtering.

Abstract

A number of factors must be considered when forming a digital filter to two-dimensionally filter single photon emission computed tomographic (SPECT) acquisition images. In an effort to provide subjectively optimal filtering, a program has been developed which provides "real-time" visual feedback. This allows a user to select from among a family of Metz filters tailored for the imaging conditions (i.e., formed to deconvolve scatter, septal penetration, and combined collimator and intrinsic spatial resolution losses). Also, a guideline for assisting the user in selecting from among the possible Metz filters has been formulated. This guideline is based upon knowledge of the probability distribution of the noise power spectrum, and consists of choosing the filter which has a value of 1.0 when the one-dimensional compression of the image power spectrum equals the 90% confidence limit for noise fluctuations. The program starts by filtering a planar reference image with the Metz filter computed for the radionuclide, collimator, magnification, and count-level of the image. This filter is displayed beside the image where it is overlayed on a plot of the logarithm of the one-dimensional compression of the image power spectrum. The user is then allowed to vary the filter parameters through movement of a joystick. By doing the filtering using an array processor, a new filtered image is formed and displayed less than a second after movement of the joystick. Visual feedback from the series of filtered images thus produced as well as the plots of the filter overlayed on the estimated blurred object power spectrum are used to obtain a visually "optimal" filter. The filter can be adapted to the visual preferences of the individual reader, and serves as a useful teaching tool on the effects of filtering.

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BibTeXRIS

M A King, S J Glick, B C Penney, R B Schwinger, P W Doherty. 1987. Interactive visual optimization of SPECT prereconstruction filtering.. https://pubmed.ncbi.nlm.nih.gov/3496435/

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