Biomedical subjects

Brandon D Gallas

Publications and source records attributed to Brandon D Gallas.

6 recordsLinked to original sources

PubMed2006

Detectability decreases with off-normal viewing in medical liquid crystal displays.

RATIONALE AND OBJECTIVES: To quantify the reduction in detection performance of subtle signals at off-normal viewing directions in medical active-matrix liquid crystal displays (AMLCDs). MATERIALS AND METHODS: Fifty synthetic image pairs per viewing condition (a total of 350) were used in a two-alternative forced-choice experiment in which 11 trained observers viewed images at 0, 30, and 45 degrees from the display normal, along the diagonal axis of a 5 million pixel in-plane switching monochrome AMLCD. The images were generated using white-noise backgrounds. A Gaussian signal was added to the signal-present set with three different signal amplitudes (4, 8, and 12 gray levels in a 10-bit scale). RESULTS: The average percent correct achieved for a signal of 4 gray levels was 79.6 (95% confidence intervals based on reader and case variability: 71.6-86.9), 63.4 (CI 56.0-71.3), and 55.3 (CI 48.4-62.0), for 0, 30 and 45 degrees from the display normal, respectively. When the signal amplitude was increased by a factor of two, the performance was 76.9 and 57.0 for 30 and 45 degrees, respectively, and 95.3 and 85.3 when the amplitude was increased by a factor of three. The observers took on average about twice as long and as much as seven times as long to reach decisions in off-normal viewing. CONCLUSIONS: Off-normal viewing of diagnostic images in AMLCDs significantly reduces the detection of low-contrast abnormalities. Increased off-normal signal amplitudes were required to regain the detection performance measured for normal viewing. We observed this decrease in detection performance for off-normal viewing even when measured decision times were about twice as long as for normal viewing.

Computer Terminals↗

PubMed2006

One-shot estimate of MRMC variance: AUC.

RATIONALE AND OBJECTIVES: One popular study design for estimating the area under the receiver operating characteristic curve (AUC) is the one in which a set of readers reads a set of cases: a fully crossed design in which every reader reads every case. The variability of the subsequent reader-averaged AUC has two sources: the multiple readers and the multiple cases (MRMC). In this article, we present a nonparametric estimate for the variance of the reader-averaged AUC that is unbiased and does not use resampling tools. MATERIALS AND METHODS: The one-shot estimate is based on the MRMC variance derived by the mechanistic approach of Barrett et al. (2005), as well as the nonparametric variance of a single-reader AUC derived in the literature on U statistics. We investigate the bias and variance properties of the one-shot estimate through a set of Monte Carlo simulations with simulated model observers and images. The different simulation configurations vary numbers of readers and cases, amounts of image noise and internal noise, as well as how the readers are constructed. We compare the one-shot estimate to a method that uses the jackknife resampling technique with an analysis of variance model at its foundation (Dorfman et al. 1992). The name one-shot highlights that resampling is not used. RESULTS: The one-shot and jackknife estimators behave similarly, with the one-shot being marginally more efficient when the number of cases is small. CONCLUSIONS: We have derived a one-shot estimate of the MRMC variance of AUC that is based on a probabilistic foundation with limited assumptions, is unbiased, and compares favorably to an established estimate.

Area Under Curve↗

PubMed2006

Toward objective and quantitative evaluation of imaging systems using images of phantoms.

The use of imaging phantoms is a common method of evaluating image quality in the clinical setting. These evaluations rely on a subjective decision by a human observer with respect to the faintest detectable signal(s) in the image. Because of the variable and subjective nature of the human-observer scores, the evaluations manifest a lack of precision and a potential for bias. The advent of digital imaging systems with their inherent digital data provides the opportunity to use techniques that do not rely on human-observer decisions and thresholds. Using the digital data, signal-detection theory (SDT) provides the basis for more objective and quantitative evaluations which are independent of a human-observer decision threshold. In a SDT framework, the evaluation of imaging phantoms represents a "signal-known-exactly/background-known-exactly" ("SKE/ BKE") detection task. In this study, we compute the performance of prewhitening and nonprewhitening model observers in terms of the observer signal-to-noise ratio (SNR) for these "SK E/BKE" tasks. We apply the evaluation methods to a number of imaging systems. For example, we use data from a laboratory implementation of digital radiography and from a full-field digital mammography system in a clinical setting. In addition, we make a comparison of our methods to human-observer scoring of a set of digital images of the CDMAM phantom available from the internet (EUREF-European Reference Organization). In the latter case, we show a significant increase in the precision of the quantitative methods versus the variability in the scores from human observers on the same set of images. As regards bias, the performance of a model observer estimated from a finite data set is known to be biased. In this study, we minimize the bias and estimate the variance of the observer SNR using statistical resampling techniques, namely, "bootstrapping" and "shuffling" of the data sets. Our methods provide objective and quantitative evaluation of imaging systems with increased precision and reduced bias.

Algorithms↗

PubMed2004

Lubberts effect in columnar phosphors.

Noise transfer in granular x-ray imaging phosphor screens is not proportional to the square of the magnitude of the signal transfer when the transfer properties are considered for the entire screen thickness, unless appropriately weighted at each depth of interaction. This property, known as the Lubberts effect, has not yet been studied in columnar structured screens because of a lack of a generalized description of the depth-dependent light transport. In this paper, we investigate the signal and noise transfer characteristics of columnar phosphors used in digital mammography detectors using DETECT-II, an optical Monte Carlo light transport simulation code. We first validate our choice of optical parameters for the description of granular and columnar screens using published normalized modulation transfer (MTF) experimental data. Our calculations of MTF match empirically measured MTFs for a granular film/screen analog system, and for an indirect x-ray digital imaging system with CsI:Tl screen representative of digital mammography systems. Using the depth-dependent spread functions and collection efficiencies, we calculate the signal and noise transfer functions and the Lubberts fraction, which is the ratio of the signal transfer function to the noise transfer function, for different screen thicknesses of granular and columnar phosphors. We find that the Lubberts fraction of a 85 microm granular screen model corresponding to a Gd2O2S:Tb screen is similar to the fraction for a 100 microm columnar CsI:Tl screen.

Computer Simulation↗

PubMed2004

An energy- and depth-dependent model for x-ray imaging.

In this paper, we model an x-ray imaging system, paying special attention to the energy- and depth-dependent characteristics of the inputs and interactions: x rays are polychromatic, interaction depth and conversion to optical photons is energy-dependent, optical scattering and the collection efficiency depend on the depth of interaction. The model we construct is a random function of the point process that begins with the distribution of x rays incident on the phosphor and ends with optical photons being detected by the active area of detector pixels to form an image. We show how the point-process representation can be used to calculate the characteristic statistics of the model. We then simulate a Gd2O2S:Tb phosphor, estimate its characteristic statistics, and proceed with a signal-detection experiment to investigate the impact of the pixel fill factor on detecting spherical calcifications (the signal). The two extremes possible from this experiment are that SNR2 does not change with fill factor or changes in proportion to fill factor. In our results, the impact of fill factor is between these extremes, and depends on the diameter of the signal.

Calcinosis↗

PubMed2003

Validating the use of channels to estimate the ideal linear observer.

Image quality can be objectively defined according to how well an observer can perform a task of practical interest given the image. We review a practical model observer for the signal-detection task. The ideal observer for this task is a function of the image probability distributions, which are multidimensional and complicated. This observer is often too difficult to derive or estimate. An alternative to the ideal observer is the ideal linear observer, which can still be unmanageable. Our alternative is the ideal linear observer constrained to a small set of channels: the channelized-Hotelling observer.

Diagnostic Imaging↗