Craig K. Abbey

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.

Human linear template with mammographic backgrounds estimated with a genetic algorithm

We estimated human observer linear templates underlying the detection of a realistic, spherical mass signal with mammographic backgrounds. Five trained naïve observers participated in two-alternative forced-choice (2-AFC) detection experiments with the signal superimposed on synthetic, clustered lumpy backgrounds (CLBs) in one condition and on nonstationary real mammographic backgrounds in another. Human observer linear templates were estimated using a genetic algorithm. A variety of common model observer templates were computed, and their shapes and associated performances were compared with those of the human observer. The estimated linear templates are not significantly different for stationary CLBs and real mammographic backgrounds. The estimated performance of the linear template compared with that of the human observers is within 5% in terms of percent correct (Pc) for the 2-AFC task. Channelized Hotelling models can fit human performance, but the templates differ considerably from the human linear template. Due to different local statistics, detection efficiency is significantly higher on nonstationary real backgrounds than on globally stationary synthetic CLBs. This finding emphasizes that nonstationary backgrounds need to be described by their local statistics.

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.

Adaptation and visual search in mammographic images

Radiologists face the visually challenging task of detecting suspicious features within the complex and noisy backgrounds characteristic of medical images. We used a search task to examine whether the salience of target features in x-ray mammograms could be enhanced by prior adaptation to the spatial structure of the images. The observers were not radiologists, and thus had no diagnostic training with the images. The stimuli were randomly selected sections from normal mammograms previously classified with BIRADS Density scores of “fatty” versus “dense,” corresponding to differences in the relative quantities of fat versus fibroglandular tissue. These categories reflect conspicuous differences in visual texture, with dense tissue being more likely to obscure lesion detection. The targets were simulated masses corresponding to bright Gaussian spots, superimposed by adding the luminance to the background. A single target was randomly added to each image, with contrast varied over five levels so that they varied from difficult to easy to detect. Reaction times were measured for detecting the target location, before or after adapting to a gray field or to random sequences of a different set of dense or fatty images. Observers were faster at detecting the targets in either dense or fatty images after adapting to the specific background type (dense or fatty) that they were searching within. Thus, the adaptation led to a facilitation of search performance that was selective for the background texture. Our results are consistent with the hypothesis that adaptation allows observers to more effectively suppress the specific structure of the background, thereby heightening visual salience and search efficiency.

Evaluation of search strategies for microcalcifications and masses in 3D images.

Medical imaging is quickly evolving towards 3D image modalities such as computed tomography (CT), magnetic resonance imaging (MRI) and digital breast tomosynthesis (DBT). These 3D image modalities add volumetric information but further increase the need for radiologists to search through the image data set. Although much is known about search strategies in 2D images less is known about the functional consequences of different 3D search strategies. We instructed readers to use two different search strategies: drillers had their eye movements restricted to a few regions while they quickly scrolled through the image stack, scanners explored through eye movements the 2D slices. We used real-time eye position monitoring to ensure observers followed the drilling or the scanning strategy while approximately preserving the percentage of the volumetric data covered by the useful field of view. We investigated search for two signals: a simulated microcalcification and a larger simulated mass. Results show an interaction between the search strategy and lesion type. In particular, scanning provided significantly better detectability for microcalcifications at the cost of 5 times more time to search while there was little change in the detectability for the larger simulated masses. Analyses of eye movements support the hypothesis that the effectiveness of a search strategy in 3D imaging arises from the interaction of the fixational sampling of visual information and the signals’ visibility in the visual periphery.

Observer templates in 2D and 3D localization tasks.

In this study we examine search performance for 3D forced-localization tasks in Gaussian random textures in which subjects are able to freely scroll through the image as part of their search for the target. We also evaluate a 2D single-slice version of the same task for comparison. We analyze these experiments using both efficiency with respect to the Ideal Observer and the classification image technique, which directly estimates the weighting function used by observers for a task. We are particularly interested in whether subjects can efficiently integrate across multiple slices in depth as part of performing the localization task. In the 3D tasks, the image display we use allows subjects to freely scroll through a volumetric image, and a localization response is made through a mouse-click on the image. The search region has a relatively modest size (approx. 8.8° visual angle). Localization responses are considered correct if they are close to the target center (within 6 voxels). The classification image methodology uses noise fields from the incorrect localizations to build an estimate of the weights used by the observer to perform the task. The basic idea is that incorrect localizations occur in regions of the image where the noise field matches the weighting profile, thereby eliciting a strong internal response. The efficiency results indicate differences between 2D and 3D search tasks, with lower efficiency for large target in the 3D task. The classification images suggest that this finding can be explained by the lack of spatial integration across slices.