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.

For the 2-class detection problem (signal absent/present), the likelihood ratio is an ideal observer in that it minimizes Bayes risk for arbitrary costs and it maximizes the area under the receiver operating characteristic (ROC) curve [AUC]. The AUC-optimizing property makes it a valuable tool in imaging system optimization. If one considered a different task, namely, joint detection and localization of the signal, then it would be similarly valuable to have a decision strategy that optimized a relevant scalar figure of merit. We are interested in quantifying performance on decision tasks involving location uncertainty using the localization ROC (LROC) methodology. Therefore, we derive decision strategies that maximize the area under the LROC curve, A(LROC). We show that these decision strategies minimize Bayes risk under certain reasonable cost constraints. The detection-localization task is modeled as a decision problem in three increasingly realistic ways. In the first two models, we treat location as a discrete parameter having finitely many values resulting in an (L + 1) class classification problem. In our first simple model, we do not include search tolerance effects and in the second, more general, model, we do. In the third and most general model, we treat location as a continuous parameter and also include search tolerance effects. In all cases, the essential proof that the observer maximizes A(LROC) is obtained with a modified version of the Neyman-Pearson lemma. A separate form of proof is used to show that in all three cases, the decision strategy minimizes the Bayes risk under certain reasonable cost constraints.

We propose an algorithm, E-COSEM (enhanced complete-data ordered subsets expectation-maximization), for fast maximum likelihood (ML) reconstruction in emission tomography. E-COSEM is founded on an incremental EM approach. Unlike the familiar OSEM (ordered subsets EM) algorithm which is not convergent, we show that E-COSEM converges to the ML solution. Alternatives to the OSEM include RAMLA, and for the related maximum a posteriori (MAP) problem, the BSREM and OS-SPS algorithms. These are fast and convergent, but require ajudicious choice of a user-specified relaxation schedule. E-COSEM itself uses a sequence of iteration-dependent parameters (very roughly akin to relaxation parameters) to control a tradeoff between a greedy, fast but non-convergent update and a slower but convergent update. These parameters are computed automatically at each iteration and require no user specification. For the ML case, our simulations show that E-COSEM is nearly as fast as RAMLA.

We investigate a new, provably convergent OSEM-like (ordered-subsets expectation-maximization) reconstruction algorithm for emission tomography. The new algorithm, which we term C-OSEM (complete-data OSEM), can be shown to monotonically increase the log-likelihood at each iteration.The familiar ML-EM reconstruction algorithm for emission tomography can be derived in a novel way. One may write a single objective function with complete, incomplete data and the reconstruction variables as in the EM approach. But in the objective function approach, there is no E-step. Instead, a suitable alternating descent on the complete data and then the reconstruction variables results in two update equations that can be shown to be equivalent to the familiar EM algorithm. Hence, minimizing this objective becomes equivalent to maximizing the likelihood.We derive our C-OSEM algorithm by modifying the above approach to update the complete data only along ordered subsets. The resulting update equation is quite different from OSEM, but still retains the speed-enhancing feature of the updates due to the limited backprojection facilitated by the ordered subsets. Despite this modification, we are able to show that the objective function decreases at each iteration, and (given a few more mild assumptions regarding the number of fixed points) conclude that the C-OSEM algorithm provides a monotonic convergence toward the maximum likelihood solution.We simulated noisy and noiseless emission projection data, and reconstructed them using the ML-EM, and the proposed C-OSEM with 4 subsets. We also reconstruct the data using the OSEM method. Anecdotal results show that the C-OSEM algorithm is much faster than ML-EM though slower than OSEM.

In emission tomography, anatomical side information, in the form of organ and lesion boundaries, derived from intra-patient coregistered CT or MR scans can be incorporated into the reconstruction. Our interest is in exploring the efficacy of such side information for lesion detectability. To assess detectability we used the SNR of a channelized Hotelling observer and a signal-known exactly/background-known exactly detection task. In simulation studies, we incorporated anatomical side information into a SPECT MAP (maximum a posteriori) reconstruction by smoothing within but not across organ or lesion boundaries. A non-anatomical prior was applied by uniform smoothing across the entire image. We investigated whether the use of anatomical priors with organ boundaries alone or with perfect lesion boundaries alone would change lesion detectability relative to the case of a prior with no anatomical information. Furthermore, we investigated whether any such detectability changes for the organ-boundary case would be a function of the distance of the lesion to the organ boundary. We also investigated whether any detectability changes for the lesion-boundary case would be a function of the degree of proximity, i.e. a difference in the radius of the true functional lesion and the radius of the anatomical lesion boundary. Our results showed almost no detectability difference with versus without organ boundaries at any lesion-to-organ boundary distance. Our results also showed no difference in lesion detectability with and without lesion boundaries, and no variation of lesion detectability with degree of proximity.