Showing papers by "Craig K. Abbey published in 2013"

Association between power law coefficients of the anatomical noise power spectrum and lesion detectability in breast imaging modalities

Abstract: Previous research has demonstrated that a parameter extracted from a power function fit to the anatomical noise power spectrum, ?, may be predictive of breast mass lesion detectability in x-ray based medical images of the breast. In this investigation, the value of ? was compared with a number of other more widely used parameters, in order to determine the relationship between ? and these other parameters. This study made use of breast CT data sets, acquired on two breast CT systems developed in our laboratory. A total of 185 breast data sets in 183 women were used, and only the unaffected breast was used (where no lesion was suspected). The anatomical noise power spectrum computed from two-dimensional region of interests (ROIs), was fit to a power function (NPS(f) = ? f??), and the exponent parameter (?) was determined using log/log linear regression. Breast density for each of the volume data sets was characterized in previous work. The breast CT data sets analyzed in this study were part of a previous study which evaluated the receiver operating characteristic (ROC) curve performance using simulated spherical lesions and a pre-whitened matched filter computer observer. This ROC information was used to compute the detectability index as well as the sensitivity at 95% specificity. The fractal dimension was computed from the same ROIs which were used for the assessment of ?. The value of ? was compared to breast density, detectability index, sensitivity, and fractal dimension, and the slope of these relationships was investigated to assess statistical significance from zero slope. A statistically significant non-zero slope was considered to be a positive association in this investigation. All comparisons between ? and breast density, detectability index, sensitivity at 95% specificity, and fractal dimension demonstrated statistically significant association with p

Objective Assessment of Sonographic: Quality II Acquisition Information Spectrum

Abstract: This paper describes a task-based, information-theoretic approach to the assessment of image quality in diagnostic sonography. We expand the Kullback-Leibler divergence metric J, which quantifies the diagnostic information contained within recorded radio-frequency echo signals, into a spatial-frequency integral comprised of two spectral components: one describes patient features for low-contrast diagnostic tasks and the other describes instrumentation properties. The latter quantity is the acquisition information spectrum (AIS), which measures the density of object information that an imaging system is able to transfer to the echo data at each spatial frequency. AIS is derived based on unique properties of acoustic scattering in tissues that generate object contrast. Predictions made by the J integral expression were validated through Monte Carlo studies using echo-signal data from simulated lesions. Our analysis predicts the diagnostic performance of any sonographic system at specific diagnostic tasks based on engineering properties of the instrument that constitute image quality.

Objective Assessment of Sonographic Quality I: Task Information

Abstract: In this paper, we explore relationships between the performance of the ideal observer and information-based measures of class separability in the context of sonographic breast-lesion diagnosis. This investigation was motivated by a finding that, since the test statistic of the ideal observer in sonography is a quadratic function of the echo data, it is not generally normally distributed. We found for some types of boundary discrimination tasks often required for sonographic lesion diagnosis, the deviation of the test statistic from a normal distribution can be significant. Hence the usual relationships between performance and information metrics become uncertain. Using Monte Carlo studies involving five common sonographic lesion-discrimination tasks, we found in each case that the detectability index dA2 from receiver operating characteristic analysis was well approximated by the Kullback-Leibler divergence J, a measure of clinical task information available from the recorded radio-frequency echo data. However, the lesion signal-to-noise ratio, SNRI2, calculated from moments of the ideal observer test statistic, consistently underestimates dA2 for high-contrast boundary discrimination tasks. Thus, in a companion paper, we established a relationship between image-quality properties of the imaging system and J in order to predict ideal performance. These relationships provide a rigorous basis for sonographic instrument evaluation and design.

Estimating the relative utility of screening mammography

Abstract: Background. The concept of diagnostic utility is a fundamental component of signal detection theory, going back to some of its earliest works. Attaching utility values to the various possible outcomes of a diagnostic test should, in principle, lead to meaningful approaches to evaluating and comparing such systems. However, in many areas of medical imaging, utility is not used because it is presumed to be unknown. Methods. In this work, we estimate relative utility (the utility benefit of a detection relative to that of a correct rejection) for screening mammography using its known relation to the slope of a receiver operating characteristic (ROC) curve at the optimal operating point. The approach assumes that the clinical operating point is optimal for the goal of maximizing expected utility and therefore the slope at this point implies a value of relative utility for the diagnostic task, for known disease prevalence. We examine utility estimation in the context of screening mammography using the Digital ...

Utility as a rationale for choosing observer performance assessment paradigms for detection tasks in medical imaging

Abstract: Purpose: Studies of lesion detectability are often carried out to evaluate medical imaging technology. For such studies, several approaches have been proposed to measure observer performance, such as the receiver operating characteristic (ROC), the localization ROC (LROC), the free-response ROC (FROC), the alternative free-response ROC (AFROC), and the exponentially transformed FROC (EFROC) paradigms. Therefore, an experimenter seeking to carry out such a study is confronted with an array of choices. Traditionally, arguments for different approaches have been made on the basis of practical considerations (statistical power, etc.) or the gross level of analysis (case-level or lesion-level). This article contends that a careful consideration of utility should form the rationale for matching the assessment paradigm to the clinical task of interest. Methods: In utility theory, task performance is commonly evaluated with total expected utility, which integrates the various event utilities against the probability of each event. To formalize the relationship between expected utility and the summary curve associated with each assessment paradigm, the concept of a “natural” utility structure is proposed. A natural utility structure is defined for a summary curve when the variables associated with the summary curve axes are sufficient for computing total expected utility, assuming that the disease prevalence is known. Results: Natural utility structures for ROC, LROC, FROC, AFROC, and EFROC curves are introduced, clarifying how the utilities of correct and incorrect decisions are aggregated by summary curves. Further, conditions are given under which general utility structures for localization-based methodologies reduce to case-based assessment. Conclusions: Overall, the findings reveal how summary curves correspond to natural utility structures of diagnostic tasks, suggesting utility as a motivating principle for choosing an assessment paradigm.

Adaptation aftereffects in the perception of radiological images.

Abstract: Radiologists must classify and interpret medical images on the basis of visual inspection. We examined how the perception of radiological scans might be affected by common processes of adaptation in the visual system. Adaptation selectively adjusts sensitivity to the properties of the stimulus in current view, inducing an aftereffect in the appearance of stimuli viewed subsequently. These perceptual changes have been found to affect many visual attributes, but whether they are relevant to medical image perception is not well understood. To examine this we tested whether aftereffects could be generated by the characteristic spatial structure of radiological scans, and whether this could bias their appearance along dimensions that are routinely used to classify them. Measurements were focused on the effects of adaptation to images of normal mammograms, and were tested in observers who were not radiologists. Tissue density in mammograms is evaluated visually and ranges from "dense" to "fatty." Arrays of images varying in intermediate levels between these categories were created by blending dense and fatty images with different weights. Observers first adapted by viewing image samples of dense or fatty tissue, and then judged the appearance of the intermediate images by using a texture matching task. This revealed pronounced perceptual aftereffects - prior exposure to dense images caused an intermediate image to appear more fatty and vice versa. Moreover, the appearance of the adapting images themselves changed with prolonged viewing, so that they became less distinctive as textures. These aftereffects could not be accounted for by the contrast differences or power spectra of the images, and instead tended to follow from the phase spectrum. Our results suggest that observers can selectively adapt to the properties of radiological images, and that this selectivity could strongly impact the perceived textural characteristics of the images.

Statistical properties of a utility measure of observer performance compared to area under the ROC curve

Abstract: The receiver operating characteristic (ROC) curve has become a common tool for evaluating diagnostic imaging technologies, and the primary endpoint of such evaluations is the area under the curve (AUC), which integrates sensitivity over the entire false positive range. An alternative figure of merit for ROC studies is expected utility (EU), which focuses on the relevant region of the ROC curve as defined by disease prevalence and the relative utility of the task. However if this measure is to be used, it must also have desirable statistical properties keep the burden of observer performance studies as low as possible. Here, we evaluate effect size and variability for EU and AUC. We use two observer performance studies recently submitted to the FDA to compare the EU and AUC endpoints. The studies were conducted using the multi-reader multi-case methodology in which all readers score all cases in all modalities. ROC curves from the study were used to generate both the AUC and EU values for each reader and modality. The EU measure was computed assuming an iso-utility slope of 1.03. We find mean effect sizes, the reader averaged difference between modalities, to be roughly 2.0 times as big for EU as AUC. The standard deviation across readers is roughly 1.4 times as large, suggesting better statistical properties for the EU endpoint. In a simple power analysis of paired comparison across readers, the utility measure required 36% fewer readers on average to achieve 80% statistical power compared to AUC.

Medical Imaging 2013: Image Perception, Observer Performance, and Technology Assessment

Abstract: Cooperating Organizations AAPM—American Association of Physicists in Medicine (United States) • APS—American Physiological Society • CARS—Computer Assisted Radiology and Surgery (Germany) Medical Image Perception Society (United States) • Radiological Society of North America (United States) • The DICOM Standards Committee (United States) • Society for Imaging Informatics in Medicine (United States) • Florida Photonics Cluster (United States) World Molecular Imaging Society