John M. M. Anderson

Weighted least-squares reconstruction methods for positron emission tomography

We present unpenalized and penalized weighted least-squares (WLS) reconstruction methods for positron emission tomography (PET), where the weights are based on the covariance of a model error and depend on the unknown parameters. The penalty function for the latter method is chosen so that certain a priori information is incorporated. The algorithms used to minimize the WLS objective functions guarantee nonnegative estimates and, experimentally, they converged faster than the maximum likelihood expectation-maximization (ML-EM) algorithm and produced images that had significantly better resolution and contrast. Although simulations suggest that the proposed algorithms are globally convergent, a proof of convergence has not yet been found. Nevertheless, we are able to show that the unpenalized method produces estimates that decrease the objective function monotonically with increasing iterations.

Regularized image reconstruction algorithms for positron emission tomography

We develop algorithms for obtaining regularized estimates of emission means in positron emission tomography. The first algorithm iteratively minimizes a penalized maximum-likelihood (PML) objective function. It is based on standard de-coupled surrogate functions for the ML objective function and de-coupled surrogate functions for a certain class of penalty functions. As desired, the PML algorithm guarantees nonnegative estimates and monotonically decreases the PML objective function with increasing iterations. The second algorithm is based on an iteration dependent, de-coupled penalty function that introduces smoothing while preserving edges. For the purpose of making comparisons, the MLEM algorithm and a penalized weighted least-squares algorithm were implemented. In experiments using synthetic data and real phantom data, it was found that, for a fixed level of background noise, the contrast in the images produced by the proposed algorithms was the most accurate.

Image motion estimation algorithms using cumulants

A class of algorithms is presented that estimates the displacement vector from two successive image frames consisting of signal plus noise. In the model, the signals are assumed to be either non-Gaussian or (quasistationary) deterministic; and, via a consistency result for cumulant estimators, the authors unify the stochastic and deterministic signal viewpoints. The noise sources are assumed to be Gaussian (perhaps spatially and temporally correlated) and of unknown covariance. Viewing image motion estimation as a 2D time delay estimation problem, the displacement vector of a moving object is estimated by solving linear equations involving third-order auto-cumulants and cross-cumulants. Additionally, a block-matching algorithm is developed that follows from a cumulant-error optimality criterion. Finally, the displacement vector for each pel is estimated using a recursive algorithm that minimizes a mean 2D fourth-order cumulant criterion. Simulation results are presented and discussed.

Treatment of Aspergillus infection of the proximal aortic prosthetic graft with associated vertebral osteomyelitis

This is a case report of an unusual vascular graft infection involving an invasive Aspergillus species with associated vertebral osteomyelitis. Successful treatment was obtained by graft incision, extra-anatomic bypass, and prolonged antibiotic therapy. To our knowledge this is the first successful treatment of invasive Aspergillus of an aortic prosthetic graft.

Improved Poisson intensity estimation: denoising application using Poisson data

Recently, Timmermann and Nowak (1999) developed algorithms for estimating the means of independent Poisson random variables. The algorithms are based on a multiscale model where certain random variables are assumed to obey a beta-mixture density function. Timmermann and Nowak simplify the density estimation problem by assuming the beta parameters are known and only one mixture parameter is unknown. They use the observed data and the method of moments to estimate the unknown mixture parameter. Taking a different approach, we generate training data from the observed data and compute maximum likelihood estimates of all of the beta-mixture parameters. To assess the improved performance obtained by the proposed modification, we consider a denoising application using Poisson data.

The effects of caffeine, deoxyribose nucleic acid and insulin on the metabolism of glucose by adipose tissue in vitro☆

Abstract The main effects and interactions of caffeine, DNA and insulin have been studied in the isolated rat epididymal fat pad, utilizing a 2 3 factorial design, with balanced segments from 5 groups of 4 rats each. The results were evaluated by analysis of variance. Insulin has the expected stimulatory effect, DNA no effect, and caffeine an inhibitory effect on C 14 O 2 production and C 14 lipid incorporation from glucose-1-C 14 in a Krebs bicarbonate buffer. Both DNA and insulin were significantly antagonistic to caffeine in this system, and this was also true of DNA and insulin together in the presence of caffeine. Possible explanations and significance of these findings are discussed.

Intracellular free calcium in rat anterior pituitary cells monitored by fura-2

Rat anterior pituitary cells, loaded with the calcium indicator dye fura-2 after primary culture, were challenged with prolactin and growth hormone secretagogues and inhibitory hormones. To initially validate the technique, the calcium channel activator maitotoxin effectively increased intracellular free calcium [( Ca++]i). Various concentrations of the secretagogues thyrotropin releasing hormone or angiotensin II induced peak increases in [Ca++]i within 15 sec, followed by a lower and prolonged plateau phase. The inhibitory hormones dopamine and somatostatin maximally reduced [Ca++]i by 15-20 sec, followed by a spontaneous return to baseline over 5-10 min. The receptor antagonists saralacin and spiperone blocked the angiotensin II and dopamine effects, respectively. Thus, fura-2 appears to be an adequate probe for resolving second-to-second changes in [Ca++]i induced by hormone receptor activation in anterior pituitary cells.

Hidden Markov model based attenuation correction for positron emission tomography

In this paper, we present a new algorithm for segmenting short-duration transmission images in positron emission tomography (PET). Additionally, we show how the information provided by the segmentation algorithm can be used to obtain accurate attenuation correction factors. The key idea behind the segmentation algorithm is that transmission images can be viewed as hidden Markov models (HMMs). Using this viewpoint and a training procedure, it is possible to incorporate both a priori anatomical information and the statistical properties of the estimator used to reconstruct the transmission images. The main advantages of the proposed segmentation algorithm, referred to as the HMM segmentation algorithm, are that it is robust and directly addresses the inhomogeneity of the lung region. Once an attenuation image is segmented; the pixel values in the various regions are replaced by more accurate attenuation coefficient values. Then, the resulting image is smoothed with a Gaussian filter and reprojected to obtain the desired attenuation correction factors. Using data from a thorax phantom and a patient, we demonstrate the effectiveness of the HMM-based attenuation correction method.

The effects of starvation and refeeding on hexosemonophosphate shunt enzyme activity and DNA, RNA, and nitrogen content of rat adipose tissue

Abstract The activities of the enzymes glucose-6-phosphate dehydrogenase (G6PD) and 6-phosphogluconic dehydrogenase (6PGD), and the RNA, DNA, and nitrogen content of rat adipose tissue have been estimated during periods of starvation and refeeding. The activities of the enzymes fall on starvation and rise on refeeding, and the RNA content of the fat shows a similar pattern. There is no change in DNA content and adipose tissue nitrogen rises on starvation, remaining high after refeeding has been commenced following a 9-day fast. These data are interpreted as being consistent with the idea that the changes in enzyme activity observed are directly related to changes in protein synthesis in adipose tissue, and reflect atrophy and resynthesis of enzymes during the period studied.

A weighted least-squares method for PET

In this paper, the authors present a reconstruction algorithm for positron emission tomography that minimizes a weighted least-squares (WLS) objective function. The weights are based on the covariance matrix of the model error and depend on the unknown parameters. The algorithm guarantees nonnegative estimates, and in simulation studies it converged faster and had significantly better resolution and contrast than the ML-EM algorithm. Although simulations suggest that the proposed algorithm is globally convergent, a proof of convergence has not been found yet. Nevertheless, the authors are able to show that it produces estimates that decrease the objective function monotonically with increasing iterations.