Biomedical subjects

Craig K Abbey

Publications and source records attributed to Craig K Abbey.

At least 19 recordsLinked to original sources

PubMed2006

Rapamycin inhibits growth of premalignant and malignant mammary lesions in a mouse model of ductal carcinoma in situ.

PURPOSE: Rapamycin has been shown to have antitumor effects in various tumor models. To study the effect of rapamycin at different stages of breast cancer development, we used two unique mouse models of breast cancer with activated phosphatidylinositol 3-kinase (PI3K) pathway. Met-1 tumors are highly invasive and metastatic, and mammary intraepithelial neoplasia-outgrowths (MIN-O), a model for human ductal carcinoma in situ, are transplantable premalignant mammary lesions that develop invasive carcinoma with predictable latencies. Both of these models were derived from mammary lesions in Tg(MMTV-PyV-mT) mice. EXPERIMENTAL DESIGN: Met-1 tumors were used to study the effect of rapamycin treatment on invasive disease. Transplanted MIN-O model was used to study the effect of rapamycin on premalignant mammary lesions. Animals were in vivo micro-positron emission tomography imaged to follow the lesion growth and transformation to tumor during the treatment. Cell proliferation, angiogenesis, and apoptosis was assayed by immunohistochemistry. RESULTS: Rapamycin inhibited in vitro tumor cell proliferation and in vivo Met-1 tumor growth. The growth inhibition was correlated with dephosphorylation of mammalian target of rapamycin (mTOR) targets. Rapamycin treatment significantly reduced the growth of the premalignant MIN-O lesion, as well as tumor incidence and tumor burden. Growth inhibition was associated with reduced cell proliferation and angiogenesis and increased apoptosis. CONCLUSIONS: In PyV-mT mouse mammary models, rapamycin inhibits the growth of premalignant lesions and invasive tumors. Although the inhibitory effect of rapamycin was striking, rapamycin treatment did not completely obliterate the lesions.

Animals↗

PubMed2006

Classification images for detection, contrast discrimination, and identification tasks with a common ideal observer.

We consider three simple forced-choice visual tasks--detection, contrast discrimination, and identification--in Gaussian white noise. The three tasks are designed so that the difference signal in all three cases is the same difference-of-Gaussians (DOG) profile. The distribution of the image noise implies that the ideal observer uses the same DOG filter to perform all three tasks. But do human observers also use the same visual strategy to perform these tasks? We use classification image analysis to evaluate the visual strategies of human observers. We find significantly different subject classification images across the three tasks. The domain of greatest variability appears to be low spatial frequencies [<5 cycles per degree (cpd)]. In this range, we find frequency enhancement in the detection task, and frequency suppression and reversal in the contrast discrimination task. In the identification task, subject classification images agree reasonably well with the ideal observer filter. We evaluate the effect of nonlinear transducers and intrinsic spatial uncertainty to explain divergence from the ideal observer found in detection and contrast discrimination tasks.

Choice Behavior↗

PubMed2006

Preclinical imaging of mammary intraepithelial neoplasia with positron emission tomography.

Small-animal imaging with positron emission tomography (PET) has become a valuable tool for evaluating preclinical models of breast cancer and other diseases. In this review, we examine a number of issues related to preclinical imaging studies with PET, using transgenic models of ductal carcinoma in situ and metastasis as specific examples. We discuss imaging components such as reconstruction, normalization, and extraction of quantitative parameters. We also analyze the effect of longitudinal correlations on cohort size and present some simple statistical techniques for determining cohort sizes that may be helpful in designing preclinical imaging studies. We describe studies that are greatly facilitated by access to non-invasive imaging data including a study involving multiple endpoints and another investigating metastasis. We conclude with a brief survey of emerging approaches in small-animal PET imaging.

Animals↗

PubMed2006

Observer efficiency in discrimination tasks simulating malignant and benign breast lesions imaged with ultrasound.

We investigate and extend the ideal observer methodology developed by Smith and Wagner to detection and discrimination tasks related to breast sonography. We provide a numerical approach for evaluating the ideal observer acting on radio frequency (RF) frame data, which involves inversion of large nonstationary covariance matrices, and we describe a power-series approach to computing this inverse. Considering a truncated power series suggests that the RF data be Wiener-filtered before forming the final envelope image. We have compared human performance for Wiener-filtered and conventional B-mode envelope images using psychophysical studies for 5 tasks related to breast cancer classification. We find significant improvements in visual detection and discrimination efficiency in four of these five tasks. We also use the Smith-Wagner approach to distinguish between human and processing inefficiencies, and find that generally the principle limitation comes from the information lost in computing the final envelope image.

Algorithms↗

PubMed2006

Adaptive detection mechanisms in globally statistically nonstationary-oriented noise.

Studies have shown that human observers can adapt their detection strategies on the basis of the statistical properties of noisy backgrounds. One common property of such studies is that the backgrounds studied are (or are assumed to be) statistically stationary. Less is known about how humans detect signals in the more complex setting of nonstationary backgrounds. We investigated detection performance in the presence of a globally nonstationary oriented noise background. We controlled for noise-correlation effects by considering a stationary background with a power spectrum matched to the average spectrum of the nonstationary process. Performance of a nonadaptive linear filter that was unable to make use of differences in local statistics yielded constant performance in both the stationary and the nonstationary backgrounds. In contrast, performance of an ideal observer that uses local noise statistics yielded substantially higher (140%) detectability with the nonstationary backgrounds than the stationary ones. Human observers showed significantly higher (33%) detection performance in the nonstationary backgrounds, suggesting that they can adapt their detection mechanisms to the local orientation properties.

Journal Article↗

PubMed2005

Measures of performance in nonlinear estimation tasks: prediction of estimation performance at low signal-to-noise ratio.

Maximum-likelihood (ML) estimation is an established paradigm for the assessment of imaging system performance in nonlinear quantitation tasks. At high signal-to-noise ratio (SNR), ML estimates are asymptotically Gaussian-distributed, unbiased and efficient, thereby attaining the Cramer-Rao bound (CRB). Therefore, at high SNR the CRB is useful as a predictor of the variance of ML estimates and, consequently, as a basis for measures of estimation performance. At low SNR, however, the achievable parameter variances are often substantially larger than the CRB and the estimates are no longer Gaussian-distributed. These departures imply that inference about the estimates that is based on the CRB and the assumption of a normal distribution will not be valid. We have found previously that for some tasks these effects arise at noise levels considered clinically acceptable. We have derived the mathematical relationship between a new measure, chi2(pdf-ML), and the expected probability density of the ML estimates, and have justified the use of chi2(pdf-ML)-isocontours in parameter space to describe the ML estimates. We validated this approach by simulation experiments using spherical objects imaged with a Gaussian point spread function. The parameters, activity concentration and size, were estimated simultaneously by ML, and variances and covariances calculated over 1000 replications per condition from 3D image volumes and from 2D tomographic projections of the same object. At low SNR, where the CRB is no longer achievable, chi2(pdf-ML)-isocontours provide a robust prediction of the distribution of the ML estimates. At high SNR, the chi2(pdf-ML)-isocontours asymptotically approach the analogous chi2(pdf-F)-contours derived from the Fisher information matrix. The chi2(pdf-ML) model appears to be suitable for characterization of the influence of the noise level and characteristics, the task, and the object on the shape of the probability density of the ML estimates at low SNR. Furthermore, it provides unique insights into the causes of the variability of estimation performance.

Algorithms↗

PubMed2005

Spatial profiles of local and nonlocal effects upon contrast detection/discrimination from classification images.

We used classification images (A. J. Ahumada, Jr., & J. Lovell, 1971) to estimate the perceptual filter in a task designed to assess both local and nonlocal effects upon contrast detection/discrimination. Three observers performed a yes/no detection or discrimination task of a uniform circular decrement (radius = 0.68 deg) near threshold presented for 100 to 400 ms. Stimuli were presented in ring image noise that either covered the signal and an annular surrounding area (out to 1.36 deg), or only the surrounding annular area (out to 1.36 deg). Both the signal and the annular surround appeared on a uniform background. With ring noise over both the signal and surround, the amplitudes of the classification images in the signal area decreased as radial distance increased from the signal/surround border, and no effect of the surround was found. With ring noise only in the surround, classification images indicated noncontiguous effects at both the signal/surround border (local) and the surround/background border (nonlocal). The spatial extents of the nonlocal effects (< 0.07 deg) were smaller than local effects (0.25 deg), whereas the peak amplitudes of the local and nonlocal effects were comparable. These results suggest that the nonlocal effects were smaller than the local effects, and that the smaller effects would be due to smaller effective areas, as opposed to smaller amplitudes over the same area. Little or no change was found in the classification images across stimulus duration, suggesting that both the local and nonlocal processes found in this study were completed within 100 ms.

Adult↗

PubMed2005

Detection performance theory for ultrasound imaging systems.

A rigorous statistical theory for characterizing the performance of medical ultrasound systems for lesion detection tasks is developed. A design strategy for optimizing ultrasound systems should be to adjust parameters for maximum information content, which is obtained by maximizing the ideal observer performance. Then, given the radio-frequency data, image and signal processing algorithms are designed to extract as much diagnostically relevant information as possible. In this paper, closed-form and low-contrast approximations of ideal observer performance are derived for signal known statistically detection tasks. The accuracy of the approximations are tested by comparing with Monte Carlo techniques. A metric borrowed and modified from photon imaging, Generalized Noise Equivalent Quanta, is shown to be a useful and measurable target-independent figure of merit when adapted for ultrasound systems. This theory provides the potential to optimize design tradeoffs for detection tasks. For example it may help us understand how to push the limits of specific features, such as spatial resolution, without significantly compromising overall detection performance.

Algorithms↗

PubMed2004

Perceptual learning through optimization of attentional weighting: human versus optimal Bayesian learner.

Human performance in visual detection, discrimination, identification, and search tasks typically improves with practice. Psychophysical studies suggest that perceptual learning is mediated by an enhancement in the coding of the signal, and physiological studies suggest that it might be related to the plasticity in the weighting or selection of sensory units coding task relevant information (learning through attention optimization). We propose an experimental paradigm (optimal perceptual learning paradigm) to systematically study the dynamics of perceptual learning in humans by allowing comparisons to that of an optimal Bayesian algorithm and a number of suboptimal learning models. We measured improvement in human localization (eight-alternative forced-choice with feedback) performance of a target randomly sampled from four elongated Gaussian targets with different orientations and polarities and kept as a target for a block of four trials. The results suggest that the human perceptual learning can occur within a lapse of four trials (<1 min) but that human learning is slower and incomplete with respect to the optimal algorithm (23.3% reduction in human efficiency from the 1st-to-4th learning trials). The greatest improvement in human performance, occurring from the 1st-to-2nd learning trial, was also present in the optimal observer, and, thus reflects a property inherent to the visual task and not a property particular to the human perceptual learning mechanism. One notable source of human inefficiency is that, unlike the ideal observer, human learning relies more heavily on previous decisions than on the provided feedback, resulting in no human learning on trials following a previous incorrect localization decision. Finally, the proposed theory and paradigm provide a flexible framework for future studies to evaluate the optimality of human learning of other visual cues and/or sensory modalities.

Adult↗

PubMed2004

In vivo positron-emission tomography imaging of progression and transformation in a mouse model of mammary neoplasia.

Imaging mouse models of human cancer promises more effective analysis of tumor progression and reduction of the number of animals needed for statistical power in preclinical therapeutic intervention trials. This study utilizes positron emission tomography imaging of 2-[18F]-fluoro-deoxy-D-glucose to monitor longitudinal development of mammary intraepithelial neoplasia outgrowths in immunocompetent FVB/NJ mice. The mammary intraepithelial neoplasia outgrowth tissues mimic the progression of breast cancer from premalignant ductal carcinoma in situ to invasive carcinoma. Progression of disease is clearly evident in the positron emission tomography images, and tracer uptake correlates with histological evaluation. Furthermore, quantitative markers of disease extracted from the images can be used to track proliferation and progression in vivo over multiple time points.

Animals↗

PubMed2004

Search for lesions in mammograms: statistical characterization of observer responses.

We investigate human performance for visually detecting simulated microcalcifications and tumors embedded in x-ray mammograms as a function of signal contrast and the number of possible signal locations. Our results show that performance degradation with an increasing number of locations is well approximated by signal detection theory (SDT) with the usual Gaussian assumption. However, more stringent statistical analysis finds a departure from Gaussian assumptions for the detection of microcalcifications. We investigated whether these departures from the SDT Gaussian model could be accounted for by an increase in human internal response correlations arising from the image-pixel correlations present in 1/f spectrum backgrounds and/or observer internal response distributions that departed from the Gaussian assumption. Results were consistent with a departure from the Gaussian response distributions and suggested that the human observer internal responses were more compact than the Gaussian distribution. Finally, we conducted a free search experiment where the signal could appear anywhere within the image. Results show that human performance in a multiple-alternative forced-choice experiment can be used to predict performance in the clinically realistic free search experiment when the investigator takes into account the search area and the observers' inherent spatial imprecision to localize the targets.

Female↗

PubMed2004

Linear approach to axial resolution in elasticity imaging.

Thus far axial resolution in elasticity imaging has been addressed only empirically. No clear analytical approaches have emerged because the estimator is non-linear in the data, correlation functions are nonstationary, and system responses vary spatially. This paper describes a linear systems approach based on a small-strain impulse approximation that results in the derivation of a local impulse response (LIR) and local modulation transfer function (LMTF). Closed-form solutions for strain LIR are available to provide new insights on the role of instrumentation and processing on axial strain resolution. Novel phantom measurements are generated to validate results. We found that the correlation window determines axial resolution in most practical situations, but that the the same system properties that determine B-mode resolution ultimately limit elasticity imaging.

Animals↗

PubMed2003

Ideal observer model for detection of blood perfusion and flow using ultrasound.

An ideal observer model is developed for the task of detecting blood perfusing or flowing through tissue. The ideal observer theory relies on a linear systems model that describes tissue and blood object functions and electronic noise as random processes. When aliasing is minimal, the system is characterized by a quantity similar to Noise-Equivalent Quanta used in photon imaging modalities. A simple 1-D model is used to illustrate the effect of the system and object parameters on task performance. Velocity and decorrelation are seen to be advantageous for detection. Aliasing can degrade performance. The ideal observer model provides a framework for assessing the performance of Power Doppler ultrasound systems, and may aid in their design.

Algorithms↗

PubMed2003

Linear system models for ultrasonic imaging: application to signal statistics.

Linear equations for modeling echo signals from shift-variant systems forming ultrasonic B-mode, Doppler, and strain images are analyzed and extended. The approach is based on a solution to the homogeneous wave equation for random inhomogeneous media. When the system is shift-variant, the spatial sensitivity function--defined as a spatial weighting function that determines the scattering volume for a fixed point of time--has advantages over the point-spread function traditionally used to analyze ultrasound systems. Spatial sensitivity functions are necessary for determining statistical moments in the context of rigorous image quality assessment, and they are time-reversed copies of point-spread functions for shift variant systems. A criterion is proposed to assess the validity of a local shift-invariance assumption. The analysis reveals realistic situations in which in-phase signals are correlated to the corresponding quadrature signals, which has strong implications for assessing lesion detectability. Also revealed is an opportunity to enhance near- and far-field spatial resolution by matched filtering unfocused beams. The analysis connects several well-known approaches to modeling ultrasonic echo signals.

Computer Simulation↗

PubMed2003

Computer aided detection of masses in mammography using subregion Hotelling observers.

We propose to investigate the use of the subregion Hotelling observer for the basis of a computer aided detection scheme for masses in mammography. A database of 1320 regions of interest (ROIs) was selected from the DDSM database collected by the University of South Florida using the Lumisys scanner cases. The breakdown of the cases was as follows: 656 normal ROIs, 307 benign ROIs, and 357 cancer ROIs. Each ROI was extracted at a size of 1024 x 1024 pixels and sub-sampled to 128 x 128 pixels. For the detection task, cancer and benign cases were considered positive and normal was considered negative. All positive cases had the lesion centered in the ROI. We chose to investigate the subregion Hotelling observer as a classifier to detect masses. The Hotelling observer incorporates information about the signal, the background, and the noise correlation for prediction of positive and negative and is the optimal detector when these are known. For our study, 225 subregion Hotelling observers were set up in a 15 x 15 grid across the center of the ROIs. Each separate observer was designed to "observe," or discriminate, an 8 x 8 pixel area of the image. A leave one out training and testing methodology was used to generate 225 "features," where each feature is the output of the individual observers. The 225 features derived from separate Hotelling observers were then narrowed down by using forward searching linear discriminants (LDs). The reduced set of features was then analyzed using an additional LD with receiver operating characteristic (ROC) analysis. The 225 Hotelling observer features were searched by the forward searching LD, which selected a subset of 37 features. This subset of 37 features was then analyzed using an additional LD, which gave a ROC area under the curve of 0.9412 +/- 0.006 and a partial area of 0.6728. Additionally, at 98% sensitivity the overall classifier had a specificity of 55.9% and a positive predictive value of 69.3%. Preliminary results suggest that using subregion Hotelling observers in combination with LDs can provide a strong backbone for a CAD scheme to help radiologists with detection. Such a system could be used in conjunction with CAD systems for false positive reduction.

Algorithms↗

PubMed2003

Comparison of two weighted integration models for the cueing task: linear and likelihood.

In a task in which the observer must detect a signal at two locations, presenting a precue that predicts the location of a signal leads to improved performance with a valid cue (signal location matches the cue), compared to an invalid cue (signal location does not match the cue). The cue validity effect has often been explained with a limited capacity attentional mechanism improving the perceptual quality at the cued location. Alternatively, the cueing effect can also be explained by unlimited capacity models that assume a weighted combination of noisy responses across the two locations. We compare two weighted integration models, a linear model and a sum of weighted likelihoods model based on a Bayesian observer. While qualitatively these models are similar, quantitatively they predict different cue validity effects as the signal-to-noise ratios (SNR) increase. To test these models, 3 observers performed in a cued discrimination task of Gaussian targets with an 80% valid precue across a broad range of SNR's. Analysis of a limited capacity attentional switching model was also included and rejected. The sum of weighted likelihoods model best described the psychophysical results, suggesting that human observers approximate a weighted combination of likelihoods, and not a weighted linear combination.

Adult↗

PubMed2003

An ideal observer with channels versus feature-independent processing of spatial frequency and orientation in visual search performance.

An influential assumption for the front end of models in vision, visual search, and object recognition is an analysis of independent features that correspond to basic image properties, such as motion, shape, and color. Empirically, one common test of independent features (a cue-summation study) measures performance with increasing available cues or features, with improving performance leading to conclusions of summation across independent features. In a study by Shimozaki et al. [J. Vision 2, 354-370 (2002)], both ideal and human observers showed no summation with large stimulus differences, in contrast to independent-feature models and suggesting that stimulus information (as assessed by an ideal observer) might affect cue-summation studies. Extending the previous summation study, observers performed a visual search of four Gabors differing in only orientation, only spatial frequency, or both orientation and spatial frequency, across a range of target-distractor differences. An ideal observer underpredicted human summation for small differences, whereas the independent-orientation and spatial-frequency feature models overpredicted human summation for large differences. An ideal observer with channels jointly tuned to spatial frequency and orientation predicted human performance across both small and large target-distractor differences.

Cues↗

PubMed2002

Improved localization of coronary stents using layer decomposition.

Accurate placement and expansion of coronary stents is hindered by the fact that most stents are only slightly radiopaque and hence difficult to see in typical coronary X-ray images. We propose a new technique for improved image guidance of multiple coronary stent deployment using layer decomposition of coronary X-ray image sequences. We hypothesize that layer decomposition can improve the accuracy of localization of the end of a deployed stent. Layer decomposition is used to obtain good quality images of a stent in vitro. The resultant background-subtracted stent images are embedded into other cine X-ray image sequences to form a database of simulated image sequences. For each simulated sequence, the position of the stent edge is estimated from raw and layer-decomposed images using a small region of the original layer image as a template. Layer decomposition reduced median position errors in 33 of 47 image sequences (70%), including 16 of 18 sequences in which the position errors for raw and layer images differed by 5.0 pixels (0.5 mm) or more. Layer decomposition significantly reduces errors in determination of stent edge location in simulated cine X-ray image sequences.

Cineangiography↗