The detection limitations inherent in statistically limited computed tomographic (CT) images are described through the application of signal detection theory. The detectability of large-area, low-contrast objects is shown to be chiefly dependent upon the low-frequency content of the noise power spectral density. For projection data containg uncorrelated noise, the resulting ramplike, low-frequency behavior of the noise power spectrum of CT reconstructions may be conveniently characterized by the number of noise-equivalent x-ray quanta (NEQ) detected in the projection measurements. The NEQ for a given image may be determined either from a measurement of the noise power spectrum or from the noise granularity computed with an appropriate weighting function. A measure of the efficiency of scanner dose utilization is proposed which compares the average dose to that required by an ideal scanner to obtain the same NEQ.

Currently available tomographic image reconstruction schemes for optical tomography (OT) are mostly based on the limiting assumptions of small perturbations and a priori knowledge of the optical properties of a reference medium. Furthermore, these algorithms usually require the inversion of large, full, ill-conditioned Jacobian matrixes. In this work a gradient-based iterative image reconstruction (GIIR) method is presented that promises to overcome current limitations. The code consists of three major parts: 1) A finite-difference, timeresolved, diffusion forward model is used to predict detector readings based on the spatial distribution of optical properties; 2) An objective function that describes the difference between predicted and measured data; 3) An updating method that uses the gradient of the objective function in a line minimization scheme to provide subsequent guesses of the spatial distribution of the optical properties for the forward model. The reconstruction of these properties is completed, once a minimum of this objective function is found. After a presentation of the mathematical background, two-and three-dimensional reconstruction of simple heterogeneous media as well as the clinically relevant example of ventricular bleeding in the brain are discussed. Numerical studies suggest that intraventricular hemorrhages can be detected using the GIIR technique, even in the presence of a heterogeneous background.

Background Approximately 4 million U.S. travelers to developing countries are ill enough to seek healthcare with 1,500 malaria cases reported in the U.S. annually. The diagnosis of malaria is frequently delayed due to the time to prepare malaria blood films and lack of technical expertise. An easy, reliable rapid diagnostic test (RDT) with high sensitivity and negative predictive value (NPV), particularly for Plasmodium falciparum, would be clinically useful. The study objective was to determine the diagnostic performance of the FDA-approved NOW® Malaria Test in comparison to traditional thick and thin blood smears for malaria diagnosis. Methods This prospective study tested 852 consecutive blood samples sent for thick and thin smears with blinded, malaria rapid tests at three hospital laboratories during 2003–2006. Polymerase chain reaction (PCR) verified positive tests and discordant results. Results Malaria occurred in 11% (95/852). The rapid test had superior performance than the standard Giemsa thick blood smear (P=.003). The rapid test’s sensitivity for all malaria was 97% (92/95) vs. 85% (81/95) by blood smear, and the RDT had superior NPV of 99.6% vs. 98.2% (P=.001). The P. falciparum performance was excellent with 100% rapid test sensitivity versus only 88% (65/74) by blood smear (P=.003). Conclusions This operational study demonstrates the FDA-approved rapid malaria test is superior to a single set of blood smears performed under routine U.S. clinical laboratory conditions. The most valuable clinical role of the RDT is in the rapid diagnosis or the exclusion of P. falciparum malaria, which is particularly useful in outpatient settings when evaluating febrile travelers.

BACTEC media has faster time to detection and increased bacterial recovery over the BacT/Alert media in the following categories: overall growth, pathogens, septic events, gram-positive cocci, gram-negative rods, Staphylococcus aureus, and cultures where antimicrobials were dosed up to 48 hours before culture collection.

A general probabilistic technique for estimating background contributions to measured spectra is presented. A Bayesian model is used to capture the defining characteristics of the problem, namely, that the background is smoother than the signal. The signal is allowed to have positive and/or negative components. The background is represented in terms of a cubic spline basis. A variable degree of smoothness of the background is attained by allowing the number of knots and the knot positions to be adaptively chosen on the basis of the data. The fully Bayesian approach taken provides a natural way to handle knot adaptivity and allows uncertainties in the background to be estimated. Our technique is demonstrated on a particle induced x-ray emission spectrum from a geological sample and an Auger spectrum from iron, which contains signals with both positive and negative components.

In the local basis-function approach, a reconstruction is represented as a linear expansion of basis functions, which are arranged on a rectangular grid and possess a local region of support. The basis functions considered here are positive and may overlap. It is found that basis functions based on cubic B-splines offer significant improvements in the calculational accuracy that can be achieved with iterative tomographic reconstruction algorithms. By employing repetitive basis functions, the computitisnal effort involved in these algorithms can be minimized through the use of tabulated values for the line or strip integralsover a single-basis function. The local nature of the basis functions reduces the difficulties associated with applying iocal wnstraints on reconstruction values, such as upper and lower limits. Sincea r e~l l s t~d i~n is specified everywhere by a set of coefficients, display of a coarsely represented image does not require an arbitrary choice of an interpolation function

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