Nonlinear visual-search (VS) observers have shown an ability to model humans for realistic detection, localization, and classification tasks with tomographic reconstructions. As reconstruction studies test the joint effects of data acquisition and postprocessing, applying observers for similar tasks in the projection domain is also of interest. To help investigate what information a non-ideal model can provide in this role, we have developed an analytical method for assessing acquisition quality in the reconstruction domain. This approach is most useful for assessing acquisition quality based on lesion-localization tasks. Observer studies with simulated Ga-67 SPECT data were conducted to test our method. The data were acquired for different numbers of projection angles. The results illustrate how image reconstruction processes can improve on acquisition quality as measured for an anthropomorphic model observer.
A principal difference between the channelized Hotelling (CH) and visual-search (VS) model observers is how they respond to noise texture in images. We compared the two observers in lesion-detection studies to evaluate linear and angular sampling parameters for CT. Simulated lung images were generated from a single two-dimensional mathematical torso phantom containing circular lesions of fixed radius and relative contrast. Projection datasets were produced for two detector pixel sizes and from 15 to 128 projections at 15 and 65 M counts per set. Filtered backprojection reconstructions were obtained with dimensions of 128 × 128 and 256 × 256. A localization receiver operating characteristic study was conducted with two human observers, three single-feature VS observers, and a feature-adaptive VS observer. The effects of the sampling parameters on performance were similar for all of these observers. The CH observer, applied in location-known studies with and without background variability, was not affected by the variations in angular sampling. The two-stage VS framework was an effective modification of the CH observer for assessing the effects of noise texture on human-observer performance in this study.
Past studies with tomographic reconstructions have shown that visual-search (VS) model observers can be used to evaluate acquisition protocols in medical imaging. However, projection-space studies could be more efficient. A localization ROC (LROC) study was conducted with two VS observers and sets of simulated CT projections and reconstructions generated from a clinically realistic 2D lumbar-spine phantom. The phantom was an axial slice through the L3 vertebrae of a clinical CT. Simulated 1-cm circular lesions had a relative contrast of 1.5. The acquisitions contained from 15 to 512 parallel-beam projections over 180 degrees. Reconstructions were generated with backprojection (BP) and filtered BP (FBP). Both observers identified and compared suspicious candidate locations in an image by means of feature extraction. One observer used the lesion gradient while the other used the gradients for a set of approximate lesion profiles. Observer performance with projections and BP images was highly correlated as a function of the number of projections. FBP performance was lower but still correlated with projection and BP-image performance. VS observers may provide a novel means of optimizing CT acquisitions under clinically relevant tasks using projection data.
Lesion-detection studies that analyze a fixed target position are generally considered predictive of studies involving lesion search, but the extent of the correlation often goes untested. The purpose of this work was to develop a visual-search (VS) model observer for location-known tasks that, coupled with previous work on localization tasks, would allow efficient same-observer assessments of how search and other task variations can alter study outcomes. The model observer featured adjustable parameters to control the search radius around the fixed lesion location and the minimum separation between suspicious locations. Comparisons were made against human observers, a channelized Hotelling observer and a nonprewhitening observer with eye filter in a two-alternative forced-choice study with simulated lumpy background images containing stationary anatomical and quantum noise. These images modeled single-pinhole nuclear medicine scans with different pinhole sizes. When the VS observer’s search radius was optimized with training images, close agreement was obtained with human-observer results. Some performance differences between the humans could be explained by varying the model observer’s separation parameter. The range of optimal pinhole sizes identified by the VS observer was in agreement with the range determined with the channelized Hotelling observer.
Our aim is to devise model observers (MOs) to evaluate acquisition protocols in medical imaging. To optimize protocols for human observers, an MO must reliably interpret images containing quantum and anatomical noise under aliasing conditions. In this study of sampling parameters for simulated lung CT, the lesion-detection performance of human observers was compared with that of visual-search (VS) observers, a channelized nonprewhitening (CNPW) observer, and a channelized Hoteling (CH) observer. Scans of a mathematical torso phantom modeled single-slice parallel-hole CT with varying numbers of detector pixels and angular projections. Circular lung lesions had a fixed radius. Twodimensional FBP reconstructions were performed. A localization ROC study was conducted with the VS, CNPW and human observers, while the CH observer was applied in a location-known ROC study. Changing the sampling parameters had negligible effect on the CNPW and CH observers, whereas several VS observers demonstrated a sensitivity to sampling artifacts that was in agreement with how the humans performed.