PubMed · 5793115
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R Plonsey. 1969. Ventricular vector.. https://doi.org/10.1016/s0022-0736(69)81019-8
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A spilldown correction method is proposed for the thallium-201 window image in simultaneous dual-isotope technetium-99m/thallium-201 single-photon emission tomographic (SPET) imaging based on a single acquisition into three energy windows. In this method, images are simultaneously acquired in two standard energy windows over the 99mTc and 201Tl photopeak regions and a third spilldown window adjacent to the 201Tl window. Using a Monte Carlo simulation of SPET, the fractional amount of 99mTc and 201Tl spilldown in the 201Tl window with respect to the total counts from the spilldown window, k12, was calculated for simulated images of point sources at varying depths within a water-filled elliptical tub phantom. When applied to experimental acquisitions, k12, multiplied by the total counts from the spilldown window, is then subtracted from the 201Tl window image to produce the corrected image. However, for successful applications in SPET, k12 must be determined on a projection-by-projection basis since k12 is depth dependent. Thus, a regression relation was obtained between k12 and the total count ratio of the spilldown to 99mTc windows, k23. The spilldown correction method was applied to 201Tl photopeak images of an extended source distribution in uniform and nonuniform attenuating media with dual-isotope 99mTc/201Tl and single-isotope 201Tl. A marked improvement in image contrast was observed between the corrected and uncorrected 201Tl window images. The average count ratio of uncorrected dual-isotope 201Tl/single-isotope 201Tl was 3.08 for uniform and 2.99 for non-uniform attenuating media.(ABSTRACT TRUNCATED AT 250 WORDS)
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In previous work it has been shown that a standard backpropagation neural network can be trained to reconstruct sections of single photon emission computed tomography (SPECT) images based on the planar image projections as inputs. In this study, it is demonstrated that an artificial neural network (ANN) trained on a series of simulated SPECT images or trained on a set of rudimentary geometric images can learn the planar data-to-tomographic image relationship for 64 x 64 tomograms. As a result, a properly trained ANN can produce accurate, novel image reconstructions but without the high computational cost inherent in some traditional reconstruction techniques. We also present a method of deriving activation functions for a backpropagation ANN that make it readily trainable for cardiac SPECT image reconstruction. The activation functions are derived from the estimated probability density functions (p.d.f.s) of the ANN training set data. The performance of the statistically tailored ANNs are compared with the performance of standard sigmoidal back-propagation ANNs, both in terms of their trainability and generalization ability. The results presented demonstrate that statistically tailored ANNs are significantly better than standard sigmoidal ANNs at reconstructing novel tomographic images based on a simulated SPECT image training set or a rudimentary geometric image training set. Neural network based image reconstruction has two potential advantages over conventional reconstruction methods. The first advantage is that ANNs can rapidly reconstruction tomograms. Secondly, the quality of the reconstructions produced are directly correlated to the quality of the images used to train the ANN.