Effect of inherent location uncertainty on detection of stationary targets in noisy image sequences

Channelized model observer for the detection and estimation of signals with unknown amplitude, orientation, and size

As a task-based approach for medical image quality assessment, model observers (MOs) have been proposed as surrogates for human observers. While most MOs treat only signal-known-exactly tasks, there are few studies on signal-known-statistically (SKS) MOs, which are clinically more relevant. In this paper, we present a new SKS MO named channelized joint detection and estimation observer (CJO), capable of detecting and estimating signals with unknown amplitude, orientation, and size. We evaluate its estimation and detection performance using both synthesized (correlated Gaussian) backgrounds and real clinical (magnetic resonance) backgrounds. The results suggest that the CJO has good performance in the SKS detection-estimation task.

A perceptually relevant channelized joint observer (PCJO) for the detection-localization of parametric signals

Many numerical observers have been proposed in the framework of task-based approach for medical image quality assessment. However, the existing numerical observers are still limited in diagnostic tasks: the detection task has been largely studied, while the localization task concerning one signal has been little studied and the localization of multiple signals has not been studied yet. In addition, most existing numerical observers need a priori knowledge about all the parameters of the underdetection signals, while only a few of them need at least two signal parameters. In this paper, we propose a novel numerical observer called the perceptually relevant channelized joint observer (PCJO), which cannot only detect but also localize multiple signals with unknown amplitude, orientation, size and location. We validated the PCJO for predicting human observer task performance by conducting a clinically relevant free-response subjective experiment in which six radiologists (including two experts) had to detect and localize multiple sclerosis (MS) lesions on magnetic resonance (MR) images. By using the jackknife alternative free-response operating characteristic (JAFROC) as the figure of merit (FOM), the detection-localization task performance of the PCJO was evaluated and then compared to that of the radiologists and two other numerical observers--channelized hotelling observer (CHO) and Goossenss CHO for detecting asymmetrical signals with random orientations. Overall, the results show that the PCJO performance was closer to that of the experts than to that of the other radiologists. The JAFROC1 FOMs of the PCJO (around 0.75) are not significantly different from those of the two experts (0.7672 and 0.7110), while the JAFROC1 FOMs of the numerical observers mentioned above (always over 0.84) outperform those of the experts. This indicates that the PCJO is a promising method for predicting radiologists' performance in the joint detection-localization task.

Intrinsic position uncertainty explains detection and localization performance in peripheral vision

Efficient performance in visual detection tasks requires excluding signals from irrelevant spatial locations. Indeed, researchers have found that detection performance in many tasks involving multiple potential target locations can be explained by the uncertainty the added locations contribute to the task. A similar type of Location Uncertainty may arise within the visual system itself. Converging evidence from hyperacuity and crowding studies suggests that feature localization declines rapidly in peripheral vision. This decline should add inherent position uncertainty to detection tasks. The current study used a modified detection task to measure how intrinsic position uncertainty changes with eccentricity. Subjects judged whether a Gabor target appeared within a cued region of a noisy display. The eccentricity and size of the region varied across blocks. When subjects detected the target, they used a mouse to indicate its location. This allowed measurement of localization as well as detection errors. An ideal observer degraded with internal response noise and position noise (uncertainty) accounted for both the detection and localization performance of the subjects. The results suggest that position uncertainty grows linearly with visual eccentricity and is independent of target contrast. Intrinsic position uncertainty appears to be a critical factor limiting search and detection performance.

The Coffee Challenge: A new method for the study of everyday action errors

Errors in everyday activities pose significant consequences for individuals with mild cognitive deficits. However, there are few performance-based methods available to study action in these populations; the Coffee Challenge (CC) was designed for this purpose. Experiment 1 examined CC performance in healthy participants across 10 practice trials. Analyses showed evidence for routinization after 10 trials. In Experiment 2, CC performance was disrupted by dividing attention. Errors increased significantly, but performance was not qualitatively different from baseline. The results shed light on action impairments in patient populations and validate the CC as a promising new tool for future studies.

Optimization of spiral MRI using a perceptual difference model

We systematically evaluated a variety of MR spiral imaging acquisition and reconstruction schemes using a computational perceptual difference model (PDM) that models the ability of humans to perceive a visual difference between a degraded “fast” MRI image with subsampling of k-space and a “gold standard” image mimicking full acquisition. Human subject experiments performed using a modified double-stimulus continuous-quality scale (DSCQS) correlated well with PDM, over a variety of images. In a smaller set of conditions, PDM scores agreed very well with human detectability measurements of image quality. Having validated the technique, PDM was used to systematically evaluate 2016 spiral image conditions (six interleave patterns, seven sampling densities, three density compensation schemes, four reconstruction methods, and four noise levels). Voronoi (VOR) with conventional regridding gave the best reconstructions. At a fixed sampling density, more interleaves gave better results. With noise present more interleaves and samples were desirable. With PDM, conditions were determined where equivalent image quality was obtained with 50% sampling in noise-free conditions. We conclude that PDM scoring provides an objective, useful tool for the assessment of fast MR image quality that can greatly aid the design of MR acquisition and signal processing strategies.

The effect of nonlinear human visual system components on performance of a channelized Hotelling observer in structured backgrounds

Linear model observers based on statistical decision theory have been used successfully to predict human visual detection of aperiodic signals in a variety of noisy backgrounds. However, some models have included nonlinearities such as a transducer or nonlinear decision rules to handle intrinsic uncertainty. In addition, masking models used to predict human visual detection of signals superimposed on one of two identical backgrounds (masks) usually include a number of nonlinear components in the channels that reflect properties of the firing of cells in the primary visual cortex (V1). The effect of these nonlinearities on the ability of linear model observers to predict human signal detection in real patient structured backgrounds is unknown. We evaluate the effect of including different nonlinear human visual system components into a linear channelized Hotelling observer (CHO) using a signal known exactly but variable (SKEV) task. In particular, we evaluate whether the rank order of two compression algorithms (JPEG versus JPEG 2000) and two compression encoder settings (JPEG 2000 default versus JPEG 2000 optimized) based on model observer signal detection performance in X-ray coronary angiograms is altered by inclusion of nonlinear components. The results show: 1) the simpler linear CHO model observer outperforms CHO model with the nonlinear components; 2) the rank order of model observer performance for the compression algorithms/parameters does not change when the nonlinear components are included. For the present task and images, the results suggest that the addition of the nonlinearities to a channelized Hotelling model may add complexity to the model observers without great impact on rank order evaluation of image processing and/or acquisition algorithms.

Optimization of detector pixel size for stent visualization in x-ray fluoroscopy

Pixel size is of great interest in the flat-panel detector design because of its potential impact on image quality. In the particular case of angiographic x-ray fluoroscopy, small pixels are required in order to adequately visualize interventional devices such as guidewires and stents which have wire diameters as small as 200 and 50 microm, respectively. We used quantitative experimental and modeling techniques to investigate the optimal pixel size for imaging stents. Image quality was evaluated by the ability of subjects to perform two tasks: detect the presence of a stent and discriminate a partially deployed stent from a fully deployed one in synthetic images. With measurements at 50, 100, 200, and 300 microm, the 100 microm pixel size gave the maximum contrast sensitivity for the detection experiment with the idealized direct detector. For an idealized indirect detector with a scintillating layer, an optimal pixel size was obtained at 200 microm pixel size. A channelized human observer model predicted a peak at 150 and 170 microm, for the idealized direct and indirect detectors, respectively. With regard to the stent deployment task for both detector types, smaller pixel sizes are favored and there is a steep drop in performance with larger pixels. In general, with the increasing exposures, the model and measurements give the enhanced contrast sensitivities and a smaller optimal pixel size. The effects of electronic noise and fill factor were investigated using the model. We believe that the experimental results and human observer model predications can help guide the flat-panel detector design. In addition, the human observer model should work on the similar images and be applicable to the future model and actual flat-panel implementations.

Efficiency of the human observer detecting random signals in random backgrounds

The efficiencies of the human observer and the channelized-Hotelling observer relative to the ideal observer for signal-detection tasks are discussed. Both signal-known-exactly (SKE) tasks and signal-known-statistically (SKS) tasks are considered. Signal location is uncertain for the SKS tasks, and lumpy backgrounds are used for background uncertainty in both cases. Markov chain Monte Carlo methods are employed to determine ideal-observer performance on the detection tasks. Psychophysical studies are conducted to compute human-observer performance on the same tasks. Efficiency is computed as the squared ratio of the detectabilities of the observer of interest to the ideal observer. Human efficiencies are approximately 2.1% and 24%, respectively, for the SKE and SKS tasks. The results imply that human observers are not affected as much as the ideal observer by signal-location uncertainty even though the ideal observer outperforms the human observer for both tasks. Three different simplified pinhole imaging systems are simulated, and the humans and the model observers rank the systems in the same order for both the SKE and the SKS tasks.

Hyperefficient detection of targets in noisy images

We compared human detection of visual targets in noisy images with that of a theoretically optimum matched filter. Using a small thin target with vertically aligned markers, we obtained hyperefficient detection as high as 91% as compared with the theoretical optimum, a value far exceeding the 30-50% value typically reported. When the markers were removed, detection efficiencies degraded to an average of 27%, even though subjects were aware that the target was always placed in the center of a reasonably small panel. Using a nine-alternative forced-choice experiment, we compared detection by human observers with a matched-filter computational observer on a trial-by-trial basis. With the markers present, when humans missed the correct panel, they most often chose the panel with the second-highest decision variable output from the computational observer, suggesting that the template-matching model is a good one. To model results without the markers, we included location uncertainty and additional noise sources in the template matching of the computational observer. A location uncertainty of only 1 pixel, corresponding to a retinal distance of approximately 12 microm, a dimension of the order of the size of the receptive field of photoreceptors, explained the psychometric data. With the marker present, the model suggests that hyperefficient detection is obtained by limiting target location uncertainty to <6 microm. Together these results give important new insights into human visual detection mechanisms.

Quantitative image quality analysis of a nonlinear spatio-temporal filter

Digital temporal and spatial filtering of fluoroscopic image sequences can be used to improve the quality of images acquired at low X-ray exposure. In this study, we characterized a nonlinear edge preserving, spatio-temporal noise reduction filter, the bidirectional multistage (BMS) median filter of Arce (1991). To assess image quality, signal detection and discrimination experiments were performed on stationary targets using a four-alternative forced-choice paradigm. A measure of detectability, d', was obtained for filtered and unfiltered noisy image sequences at different signal amplitudes. Filtering gave statistically significant, average d' improvements of 20% (detection) and 31% (discrimination). A nonprewhitening detection model modified to include the human spatio-temporal visual system contrast-sensitivity underestimated enhancement, predicting an improvement of 6%. Pixel noise standard deviation, a commonly applied image quality measure, greatly overestimated effectiveness giving 67% improvement in d'. We conclude that human testing is required to evaluate the filter effectiveness and that human perception models must be improved to account for the spatio-temporal filtering of image sequences.