Manual segmentation of 129 x-ray CT transverse slices of a living male human has been done and a computerized 3-dimensional volume array modeling all major internal structures of the body has been created. Each voxel of the volume contains a index number designating it as belonging to a given organ or internal structure. The original x-ray CT images were reconstructed in a 512 x 512 matrix with a resolution of 1 mm in the x,y plane. The z-axis resolution is 1 cm from neck to midthigh and 0.5 cm from neck to crown of the head. This volume array represents a high resolution model of the human anatomy and can serve as a voxel-based anthropomorphic phantom suitable for many computer-based modeling and simulation calculations.
Proposes a Bayesian method whereby maximum a posteriori (MAP) estimates of functional (PET and SPECT) images may be reconstructed with the aid of prior information derived from registered anatomical MR images of the same slice. The prior information consists of significant anatomical boundaries that are likely to correspond to discontinuities in an otherwise spatially smooth radionuclide distribution. The authors' algorithm, like others proposed recently, seeks smooth solutions with occasional discontinuities; the contribution here is the inclusion of a coupling term that influences the creation of discontinuities in the vicinity of the significant anatomical boundaries. Simulations on anatomically derived mathematical phantoms are presented. Although computationally intense in its current implication, the reconstructions are improved (ROI-RMS error) relative to filtered backprojection and EM-ML reconstructions. The simulations show that the inclusion of position-dependent anatomical prior Information leads to further improvement relative to Bayesian reconstructions without the anatomical prior. The algorithm exhibits a certain degree of robustness with respect to errors in the location of anatomical boundaries.
The observation that laser-induced fluorescence (LIF) spectra of atherosclerotic and normal artery are different has been proposed as the basis for guiding a "smart" laser angioplasty system. The purpose of this study was to investigate the causes of this difference in LIF. Helium-cadmium laser-induced (325 nm) fluorescence was recorded from pure samples of known constituents of normal and atherosclerotic artery including collagen, elastin, calcium, cholesterol, and glycosaminoglycans. Similarities between the LIF spectra of atherosclerotic plaque and collagen and normal aorta and elastin were noted. LIF spectroscopy was then performed on specimens of atherosclerotic aortic plaque (n=9) and normal aorta (n=13) and on their extracted lipid, collagen, and elastin. Lipid extraction did not significantly alter atherosclerotic plaque or normal aortic LIF, suggesting a minor contribution of lipid to arterial LIF. The LIF spectra of normal aorta wall was similar to the spectra of the extracted elastin, whereas the LIF spectra of atherosclerotic aortic plaque was similar to the spectra of the extracted collagen. These observations are consistent with the reported relative collagen-to-elastin content ratio of 0.5 for normal arterial wall and 7.3 for atherosclerotic plaque. A classification algorithm was developed to discriminate normal and atherosclerotic aortic spectra based on an elastin and collagen spectral decomposition. A discriminant score was formed by the difference of elastin and collagen (E-C) coefficients and used to classify 182 aortic fluorescence spectra. The mean E-C value was +0.83±0.04 for normal and -0.48±0.07 for atherosclerotic aorta (p<0.001). Classification accuracy was 92%. With 325-nm excitation, collagen and elastin are therefore the major fluorophores of aortic atherosclerotic plaque and normal aortic wall, respectively, and the difference between normal and atherosclerotic arterial fluorescence appears to be due to differences in relative collagen and elastin content. Consistent with this observation, a classification algorithm based on a collagen and elastin spectral decomposition can accurately classify normal and atherosclerotic aortic fluorescence spectra. Other laser lines may excite different chromophores. These findings will require validation for muscular arteries. (Circulation 1989;80:1893-1901 The difference between the laser-induced fluorescence spectra of atherosclerotic plaque and normal arterial wall has been well established1-5 and has been proposed as the basis
The ability to theoretically model the propagation of photon noise through PET and SPECT tomographic reconstruction algorithms is crucial in evaluating the reconstructed image quality as a function of parameters of the algorithm. In a previous approach for the important case of the iterative ML-EM (maximum-likelihood-expectation-maximization) algorithm, judicious linearizations were used to model theoretically the propagation of a mean image and a covariance matrix from one iteration to the next. Our analysis extends this approach to the case of MAP (maximum a posteriori)-EM algorithms, where the EM approach incorporates prior terms. We analyse in detail two cases: a MAP-EM algorithm incorporating an independent gamma prior, and a one-step-late (OSL) version of a MAP-EM algorithm incorporating a multivariate Gaussian prior, for which familiar smoothing priors are special cases. To validate our theoretical analyses, we use a Monte Carlo methodology to compare, at each iteration, theoretical estimates of mean and covariance with sample estimates, and show that the theory works well in practical situations where the noise and bias in the reconstructed images do not assume extreme values.
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