Biomedical subjects

Craig K Abbey

Publications and source records attributed to Craig K Abbey.

24 records · Page 2Linked to original sources

PubMed2002

Validation of the localization of the target tissue for intracoronary brachytherapy.

In a previous meta-analysis of intracoronary brachytherapy (ICBT) studies, we identified the target tissue at 0.6 to 0.7 mm tissue depth and we developed two models, describing the relationship between dose and ICBT effectiveness. The purpose of the present study was to validate the identified target tissue depth and the developed dose models, using the results of 1) two prospective animal studies with ICBT, 2) a retrospective analysis of animal studies with external beam irradiation and 3) results of recent clinical ICBT trials. ICBT effectiveness in the porcine restenosis studies was quantified as inhibition of neointima proliferation. The results of these studies were correlated with the developed dose-effectiveness model. Finally, the agreement of the restenosis rates of the recent clinical trials with the developed dose-restenosis model was tested. The porcine restenosis studies demonstrated a dose-related inhibition of neointima proliferation. The radiation effectiveness of both prospective studies and the effectiveness of the studies with external beam irradiation demonstrated the best agreement with the developed dose model at a tissue depth of 0.6 mm. Furthermore, the restenosis rates of the recent clinical ICBT studies were in concordance with the developed dose-restenosis model. In conclusion, the current study validated the localization of the target tissue for ICBT at a tissue depth of 0.6 to 0.7 mm as well as the relationship between dose and ICBT effectiveness at this depth. The data provide a rationale for setting a common dose prescription point at 0.6 to 0.7 mm tissue depth.

Angioplasty, Balloon, Coronary↗

PubMed2002

Optimal shifted estimates of human-observer templates in two-alternative forced-choice experiments.

For performing simple detection and discrimination tasks in image noise, human observers are often modeled by a cross-correlation between the image and an observer template followed by the injection of the observer's internal noise. This paper is concerned with estimating this template using the two-alternative forced-choice (2AFC) experimental paradigm. The basic idea behind the estimation procedure is to average the noise fields of the images used in a 2AFC experiment with a weight that depends on whether the observer got the trial correct or incorrect. We describe a method that produces unbiased estimates of the observer template up to a constant of proportionality under the linear cross-correlation model. The method proposed here is different from some previous methods in the way it assigns weights to the noise fields and we show that the resulting errors in the estimated template are minimized. We also propose and validate a formula for approximating the error covariance associated with the template estimates.

Choice Behavior↗

PubMed2002

Evaluation of layer decomposition for multiframe quantitative coronary angiography.

Multiframe quantitative coronary angiography is typically performed by averaging measurements of artery diameter over multiple frames. This approach reduces errors attributable to random noise but may not reduce systematic errors caused by background structures, nonlinear system response, and motion blur. We attempt to reduce these sources of error by decomposing the image sequence into moving layers, one of which includes the artery. We embed simulated arteries into clinical angiographic sequences so that the true vessel dimensions are known accurately. The measurement tasks are minimum diameter, geometric percent stenosis, and densitometric percent stenosis. We compare measurements for single and multiple raw images, single images with fixed mask subtraction, single and multiple images with layered background subtraction, and time-averaged layer images. We find that both multiframe averaging and layer decomposition significantly improve geometric and densitometric accuracy compared with single-frame measurements. The best results were obtained by averaging measurements from multiple frames of layered background-subtracted images.

Algorithms↗

PubMed2002

The footprints of visual attention in the Posner cueing paradigm revealed by classification images.

In the Posner cueing paradigm, observers' performance in detecting a target is typically better in trials in which the target is present at the cued location than in trials in which the target appears at the uncued location. This effect can be explained in terms of a Bayesian observer where visual attention simply weights the information differently at the cued (attended) and uncued (unattended) locations without a change in the quality of processing at each location. Alternatively, it could also be explained in terms of visual attention changing the shape of the perceptual filter at the cued location. In this study, we use the classification image technique to compare the human perceptual filters at the cued and uncued locations in a contrast discrimination task. We did not find statistically significant differences between the shapes of the inferred perceptual filters across the two locations, nor did the observed differences account for the measured cueing effects in human observers. Instead, we found a difference in the magnitude of the classification images, supporting the idea that visual attention changes the weighting of information at the cued and uncued location, but does not change the quality of processing at each individual location.

Attention↗

PubMed2002

Classification image analysis: estimation and statistical inference for two-alternative forced-choice experiments.

We consider estimation and statistical hypothesis testing on classification images obtained from the two-alternative forced-choice experimental paradigm. We begin with a probabilistic model of task performance for simple forced-choice detection and discrimination tasks. Particular attention is paid to general linear filter models because these models lead to a direct interpretation of the classification image as an estimate of the filter weights. We then describe an estimation procedure for obtaining classification images from observer data. A number of statistical tests are presented for testing various hypotheses from classification images based on some more compact set of features derived from them. As an example of how the methods we describe can be used, we present a case study investigating detection of a Gaussian bump profile.

Choice Behavior↗

PubMed2002

Stimulus information contaminates summation tests of independent neural representations of features.

Many models of visual processing assume that visual information is analyzed into separable and independent neural codes, or features. A common psychophysical test of independent features is known as a summation study, which measures performance in a detection, discrimination, or visual search task as the number of proposed features increases. Improvement in human performance with increasing number of available features is typically attributed to the summation, or combination, of information across independent neural coding of the features. In many instances, however, increasing the number of available features also increases the stimulus information in the task, as assessed by an optimal observer that does not include the independent neural codes. In a visual search task with spatial frequency and orientation as the component features, a particular set of stimuli were chosen so that all searches had equivalent stimulus information, regardless of the number of features. In this case, human performance did not improve with increasing number of features, implying that the improvement observed with additional features may be due to stimulus information and not the combination across independent features.

Humans↗