Abstract. When searching through volumetric images [e.g., computed tomography (CT)], radiologists appear to use two different search strategies: "drilling" (restrict eye movements to a small region of the image while quickly scrolling through slices), or "scanning" (search over large areas at a given depth before moving on to the next slice). To computationally identify the type of image information that is used in these two strategies, 23 naïve observers were instructed with either "drilling" or "scanning" when searching for target T's in 20 volumes of faux lung CTs. We computed saliency maps using both classical two-dimensional (2-D) saliency, and a three-dimensional (3-D) dynamic saliency that captures the characteristics of scrolling through slices. Comparing observers' gaze distributions with the saliency maps showed that search strategy alters the type of saliency that attracts fixations. Drillers' fixations aligned better with dynamic saliency and scanners with 2-D saliency. The computed saliency was greater for detected targets than for missed targets. Similar results were observed in data from 19 radiologists who searched five stacks of clinical chest CTs for lung nodules. Dynamic saliency may be superior to the 2-D saliency for detecting targets embedded in volumetric images, and thus "drilling" may be more efficient than "scanning."

This work evaluated the performance of a detector-based spectral CT system by obtaining objective reference data, evaluating attenuation response of iodine and accuracy of iodine quantification, and comparing conventional CT and virtual monoenergetic images in three common phantoms. Scanning was performed using the hospital's clinical adult body protocol. Modulation transfer function (MTF) was calculated for a tungsten wire and visual line pair targets were evaluated. Image noise power spectrum (NPS) and pixel standard deviation were calculated. MTF for monoenergetic images agreed with conventional images within 0.05 lp cm. NPS curves indicated that noise texture of 70 keV monoenergetic images is similar to conventional images. Standard deviation measurements showed monoenergetic images have lower noise except at 40 keV. Mean CT number and CNR agreed with conventional images at 75 keV. Measured iodine concentration agreed with true concentration within 6% for inserts at the center of the phantom. Performance of monoenergetic images at detector based spectral CT is the same as, or better than, that of conventional images. Spectral acquisition and reconstruction with a detector based platform represents the physical behaviour of iodine as expected and accurately quantifies the material concentration.

As psychophysical studies are resource-intensive to conduct, model observers are commonly used to assess and optimize medical imaging quality. Model observers are typically designed to detect at most one signal. However, in clinical practice, there may be multiple abnormalities in a single image set (e.g. multifocal multicentric (MFMC) breast cancer), which can impact treatment planning. Prevalence of signals can be different across anatomical regions, and human observers do not know the number or location of signals a priori. As new imaging techniques have the potential to improve multiple-signal detection (e.g. digital breast tomosynthesis may be more effective for diagnosis of MFMC than mammography), image quality assessment approaches addressing such tasks are needed. In this study, we present a model observer to detect multiple signals in an image dataset. A novel implementation of partial least squares (PLS) was developed to estimate different sets of efficient channels directly from the images. The PLS channels are adaptive to the characteristics of signals and the background, and they capture the interactions among signal locations. Corresponding linear decision templates are employed to generate both image-level and location-specific scores on the presence of signals. Our results show that: (1) the model observer can achieve high performance with a reasonably small number of channels; (2) the model observer with PLS channels outperforms that with benchmark modified Laguerre-Gauss channels, especially when realistic signal shapes and complex background statistics are involved; (3) the tasks of clinical interest, and other constraints such as sample size would alter the optimal design of the model observer.