Anthropomorphic Model Observer Research Articles

DeepAMO: a multi-slice, multi-view anthropomorphic model observer for visual detection tasks performed on volume images.

Purpose: We propose a deep learning-based anthropomorphic model observer (DeepAMO) for image quality evaluation of multi-orientation, multi-slice image sets with respect to a clinically realistic 3D defect detection task. Approach: The DeepAMO is developed based on a hypothetical model of the decision process of a human reader performing a detection task using a 3D volume. The DeepAMO is comprised of three sequential stages: defect segmentation, defect confirmation (DC), and rating value inference. The input to the DeepAMO is a composite image, typical of that used to view 3D volumes in clinical practice. The output is a rating value designed to reproduce a human observer's defect detection performance. In stages 2 and 3, we propose: (1)a projection-based DC block that confirms defect presence in two 2D orthogonal orientations and (2)a calibration method that "learns" the mapping from the features of stage 2 to the distribution of observer ratings from the human observer rating data (thus modeling inter- or intraobserver variability) using a mixture density network. We implemented and evaluated the DeepAMO in the context of -DMSA SPECT imaging. A human observer study was conducted, with two medical imaging physics graduate students serving as observers. A -fold cross-validation experiment was conducted to test the statistical equivalence in defect detection performance between the DeepAMO and the human observer. We also compared the performance of the DeepAMO to an unoptimized implementation of a scanning linear discriminant observer (SLDO). Results: The results show that the DeepAMO's and human observer's performances on unseen images were statistically equivalent with a margin of difference ( ) of 0.0426 at , using 288 training images. A limited implementation of an SLDO had a substantially higher AUC (0.99) compared to the DeepAMO and human observer. Conclusion: The results show that the DeepAMO has the potential to reproduce the absolute performance, and not just the relative ranking of human observers on a clinically realistic defect detection task, and that building conceptual components of the human reading process into deep learning-based models can allow training of these models in settings where limited training images are available.

A convolutional neural network-based anthropomorphic model observer for signal-known-statistically and background-known-statistically detection tasks

The purpose of this study is implementation of an anthropomorphic model observer using a convolutional neural network (CNN) for signal-known-statistically (SKS) and background-known-statistically (BKS) detection tasks. We conduct SKS/BKS detection tasks on simulated cone beam computed tomography (CBCT) images with eight types of signal and randomly varied breast anatomical backgrounds. To predict human observer performance, we use conventional anthropomorphic model observers (i.e. the non-prewhitening observer with an eye-filter, the dense difference-of-Gaussian channelized Hotelling observer (CHO), and the Gabor CHO) and implement CNN-based model observer. We propose an effective data labeling strategy for CNN training reflecting the inefficiency of human observer decision-making on detection and investigate various CNN architectures (from single-layer to four-layer). We compare the abilities of CNN-based and conventional model observers to predict human observer performance for different background noise structures. The three-layer CNN trained with labeled data generated by our proposed labeling strategy predicts human observer performance better than conventional model observers for different noise structures in CBCT images. This network also shows good correlation with human observer performance for general tasks when training and testing images have different noise structures.

A Convolutional Neural Network-Based Anthropomorphic Model Observer for Signal Detection in Breast CT Images Without Human-Labeled Data

Various imaging parameters in X-ray computed tomography (CT) should be examined and optimized by task-based assessment of human observer performance. Recently, convolutional neural networks (CNNs) have been introduced as anthropomorphic model observers. However, when human-labeled data are not available or limited, CNNs with an existing training strategy do not produce good performance agreement with human observers. The purpose of this study is to propose new training strategies for a CNN-based anthropomorphic model observer without human-labeled data for signal-known-exactly and background-known-statistically detection tasks. We acquired cone-beam CT projection data of breast background volume and reconstructed the projection data using the Feldkamp-Davis-Kress algorithm with 12 different imaging conditions including viewing image planes. Training data for the CNN were labeled utilizing conventional model observers. We employed an early stopping rule to reflect internal noise during the CNN training. To examine the CNN performance, we used three different training-testing schemes. The performance agreement between the human and model observers was measured via a Bland-Altman plot, the root-mean-squared error (RMSE), and the Pearson's correlation coefficient ($r$ ) of their proportion correct values. Throughout the three different training-testing schemes, CNNs with the proposed training strategies yielded narrower limits of agreements (with a bias lower than 0.03) and higher scores in both RMSE and $r$ than the conventional model observers. This indicates the proposed training strategies enable the CNN-based anthropomorphic model observer to have good performance agreement with human observers and generalize better to different imaging conditions than conventional anthropomorphic model observers.

Current pediatric administered activity guidelines for 99m Tc-DMSA SPECT based on patient weight do not provide the same task-based image quality.

In the current clinical practice, administered activity (AA) for pediatric molecular imaging is often based on the North American expert consensus guidelines or the European Association of Nuclear Medicine dosage card, both of which were developed based on the best clinical practice. These guidelines were not formulated using a rigorous evaluation of diagnostic image quality (IQ) relative to AA. In the guidelines, AA is determined by a weight-based scaling of the adult AA, along with minimum and maximum AA constraints. In this study, we use task-based IQ assessment methods to rigorously evaluate the efficacy of weight-based scaling in equalizing IQ using a population of pediatric patients of different ages and body weights. A previously developed projection image database was used. We measured task-based IQ, with respect to the detection of a renal functional defect at six different AA levels (AA relative to the AA obtained from the guidelines). IQ was assessed using an anthropomorphic model observer. Receiver-operating characteristics (ROC) analysis was applied; the area under the ROC curve (AUC) served as a figure-of-merit for task performance. In addition, we investigated patient girth (circumference) as a potential improved predictor of the IQ. The data demonstrate a monotonic and modestly saturating increase in AUC with increasing AA, indicating that defect detectability was limited by quantum noise and the effects of object variability were modest over the range of AA levels studied. The AA for a given value of the AUC increased with increasing age. The AUC vs AA plots for all the patient ages indicate that, for the current guidelines, the newborn and 10- and 15-yr phantoms had similar IQ for the same AA suggested by the North American expert consensus guidelines, but the 5- and 1-yr phantoms had lower IQ. The results also showed that girth has a stronger correlation with the needed AA to provide a constant AUC for 99m Tc-DMSA renal SPECT. The results suggest that (a) weight-based scaling is not sufficient to equalize task-based IQ for patients of different weights in pediatric 99m Tc-DMSA renal SPECT; and (b) patient girth should be considered instead of weight in developing new administration guidelines for pediatric patients.

CNN as model observer in a liver lesion detection task for x-ray computed tomography: A phantom study.

The purpose of this study was the evaluation of anthropomorphic model observers trained with neural networks for the prediction of a human observer's performance. To simulate liver lesions, a phantom with contrast targets (acrylic spheres, varying diameters, +30HU) was repeatedly scanned on a computed tomography scanner. Image data labeled with confidence ratings assessed in a reader study for a detection task of liver lesions were used to build several anthropomorphic model observers. Models were trained with images reconstructed with iterative reconstruction and evaluated with images reconstructed with filtered backprojection. A neural network, based on softmax regression (SR-MO), and convolutional neural networks (CNN-MO) were used to predict the performance of a human observer and compared to a channelized Hotelling observer [with Gabor channels and internal channel noise (CHOi)]. Model observers were evaluated by a receiver operating characteristic curve analysis and compared to the results in the reader study. Two strategies were used to train the SR-MO and CNN-MO: A) building a separate model for each lesion size; B) building one model that was applied to lesions of all sizes. All tested model observers and the human observer were highly correlated at each lesion size and dose level. With strategy A, Pearson's product-moment correlation coefficients r were 0.926 (95% confidence interval (CI): 0.679-0.985) for SR-MO and 0.979 (95% CI: 0.902-0.996) for CNN-MO. With strategy B, r was 0.860 (95% CI: 0.454-0.970) for SR-MO and 0.918 (95% CI: 0.651-0.983) for CNN-MO. For CHOi, r was 0.945 (95% CI: 0.755-0.989). With strategy A, mean absolute percentage differences (MAPD) between the model observers and the human observer were 3.7% for SR-MO and 1.2% for CNN-MO. With strategy B, MAPD were 3.7% for SR-MO and 3.0% for CNN-MO. For the CHOi the MAPD was 2.2%. Convolutional neural network model observers can accurately predict the performance of a human observer for all lesion sizes and dose levels in the evaluated signal detection task.

A projection image database to investigate factors affecting image quality in weight-based dosing: application to pediatric renal SPECT

Balancing the tradeoff between radiation dose, acquisition duration and diagnostic image quality is essential for medical imaging modalities involving ionizing radiation. Lower administered activities to the patient can reduce absorbed dose, but can result in reduced diagnostic image quality or require longer acquisition durations. In pediatric nuclear medicine, it is desirable to use the lowest amount of administered radiopharmaceutical activity and the shortest acquisition duration that gives sufficient image quality for clinical diagnosis. However, diagnostic image quality is a complex function of patient factors including body morphometry. In this study, we present a digital population of 90 computational anatomic phantoms that model realistic variations in body morphometry and internal anatomy. These phantoms were used to generate a large database of projection images modeling pediatric SPECT imaging using a 99mTc-DMSA tracer. We used an analytic projection code that models attenuation, spatially varying collimator-detector response, and object-dependent scatter to generate the projections. The projections for each organ were generated separately and can be subsequently scaled by parameters extracted from a pharmacokinetics model to simulate realistic tracer biodistribution, including variations in uptake, inside each relevant organ or tissue structure for a given tracer. Noise-free projection images can be obtained by summing these individual organ projections and scaling by the system sensitivity and acquisition duration. We applied this database in the context of 99mTc-DMSA renal SPECT, the most common nuclear medicine imaging procedure in pediatric patients. Organ uptake fractions based on literature values and patient studies were used. Patient SPECT images were used to verify that the sum of counts in the simulated projection images was clinically realistic. For each phantom, 384 uptake realizations, modeling random variations in the uptakes of organs of interest, were generated, producing 34 560 noise-free projection datasets (384 uptake realizations times 90 phantoms). Noisy images modeling various count levels (corresponding to different products of acquisition duration and administered activity) were generated by appropriately scaling these images and simulating Poisson noise. Acquisition duration was fixed; six count levels were simulated corresponding to projection images acquired using 25%, 50%, 75%, 100%, 125%, and 150% of the original weight-based administrated activity as computed using the North American Guidelines (Gelfand et al 2011 J. Nucl. Med. 52 318–22). Combined, a total number of 207 360 noisy projection images were generated, creating a realistic projection database for use in renal pediatric SPECT imaging research. The phantoms and projection datasets were used to calculate three surrogate indices for factors affecting image quality: renal count density, average radius of rotation, and scatter-to-primary ratio. Differences in these indices were seen across the phantoms for dosing based on current guidelines, and especially for the phantom modeling the newborn. We also performed an image quality study using an anthropomorphic model observer that demonstrates that the weight-based dose scaling does not equalize image quality as measured by the area under the receiver-operating characteristics curve. These studies suggest that a dosing procedure beyond weight-based scaling of administered activities is required to equalize image quality in pediatric renal SPECT.

Human and model observer performance for lesion detection in breast cone beam CT images with the FDK reconstruction.

We investigate the detectability of breast cone beam computed tomography images using human and model observers and the variations of exponent, β, of the inverse power-law spectrum for various reconstruction filters and interpolation methods in the Feldkamp-Davis-Kress (FDK) reconstruction. Using computer simulation, a breast volume with a 50% volume glandular fraction and a 2mm diameter lesion are generated and projection data are acquired. In the FDK reconstruction, projection data are apodized using one of three reconstruction filters; Hanning, Shepp-Logan, or Ram-Lak, and back-projection is performed with and without Fourier interpolation. We conduct signal-known-exactly and background-known-statistically detection tasks. Detectability is evaluated by human observers and their performance is compared with anthropomorphic model observers (a non-prewhitening observer with eye filter (NPWE) and a channelized Hotelling observer with either Gabor channels or dense difference-of-Gaussian channels). Our results show that the NPWE observer with a peak frequency of 7cyc/degree attains the best correlation with human observers for the various reconstruction filters and interpolation methods. We also discover that breast images with smaller β do not yield higher detectability in the presence of quantum noise.

Extended PCJO for the Detection-Localization of Hypersignals and Hyposignals in CT Images

The anthropomorphic model observer (MO) plays an important role in the assessment and optimization of medical imaging systems. The MO is a task-based approach; while the abnormality can appear as a hypersignal or a hyposignal for different imaging modalities, sequences, or organs, no MO has been proposed for the hyposignals detection-localization task in the literature. To improve the clinical relevance of the existing MOs, we propose an anthropomorphic MO that can also deal with hyposignals in this paper. In a previous study, we reported a perceptually relevant channelized joint observer (PCJO) for detecting and localizing multiple signals with unknown amplitude, orientation, size, and location. Here, we extend it mathematically to hyposignals task. A free-response study (close to the real-diagnostic procedure) for both hypersignals and hyposignals in cerebral and abdominal CT images was conducted with four radiologists. The equally weighted alternative free-response operating characteristic was used as the figure of merit. Statistical analyses show that the extended PCJO approaches the experts’ performances with no significant difference in the studied tasks. The results demonstrate that the extended PCJO is an alternative to replace radiologists for the evaluation and comparison of different medical image processing algorithms. The PCJO has been originally proposed on magnetic resonance imaging but tested on computerized tomography (CT) here; the coherent results show that the PCJO can be generalized to another modality-CT. We also provide in this paper, the reference values of all the parameters in the PCJO to facilitate its future application on magnetic resonance (MR) or CT images.

MO-FG-209-05: Towards a Feature-Based Anthropomorphic Model Observer

This symposium will review recent advances in the simulation methods for evaluation of novel breast imaging systems – the subject of AAPM Task Group TG234. Our focus will be on the various approaches to development and validation of software anthropomorphic phantoms and their use in the statistical assessment of novel imaging systems using such phantoms along with computational models for the x-ray image formation process. Due to the dynamic development and complex design of modern medical imaging systems, the simulation of anatomical structures, image acquisition modalities, and the image perception and analysis offers substantial benefits of reduced cost, duration, and radiation exposure, as well as the known ground-truth and wide variability in simulated anatomies. For these reasons, Virtual Clinical Trials (VCTs) have been increasingly accepted as a viable tool for preclinical assessment of x-ray and other breast imaging methods. Activities of TG234 have encompassed the optimization of protocols for simulation studies, including phantom specifications, the simulated data representation, models of the imaging process, and statistical assessment of simulated images. The symposium will discuss the state-of-the-science of VCTs for novel breast imaging systems, emphasizing recent developments and future directions. Presentations will discuss virtual phantoms for intermodality breast imaging performance comparisons, extension of the breast anatomy simulation to the cellular level, optimized integration of the simulated imaging chain, and the novel directions in the observer models design. Learning Objectives: 1. Review novel results in developing and applying virtual phantoms for inter-modality breast imaging performance comparisons; 2. Discuss the efforts to extend the computer simulation of breast anatomy and pathology to the cellular level; 3. Summarize the state of the science in optimized integration of modules in the simulated imaging chain; 4. Compare novel directions in the design of observer models for task based validation of imaging systems. PB: Research funding support from the NIH, NSF, and Komen for the Cure; NIH funded collaboration with Barco, Inc. and Hologic, Inc.; Consultant to Delaware State Univ. and NCCPM, UK. AA: Employed at Barco Healthcare.; P. Bakic, NIH: (NIGMS P20 #GM103446, NCI R01 #CA154444); M. Das, NIH Research grants

Collimator optimization and collimator-detector response compensation in myocardial perfusion SPECT using the ideal observer with and without model mismatch and an anthropomorphic model observer

The collimator is the primary factor that determines the spatial resolution and noise tradeoff in myocardial perfusion SPECT images. In this paper, the goal was to find the collimator that optimizes the image quality in terms of a perfusion defect detection task. Since the optimal collimator could depend on the level of approximation of the collimator-detector response (CDR) compensation modeled in reconstruction, we performed this optimization for the cases of modeling the full CDR (including geometric, septal penetration and septal scatter responses), the geometric CDR, or no model of the CDR. We evaluated the performance on the detection task using three model observers. Two observers operated on data in the projection domain: the Ideal Observer (IO) and IO with Model-Mismatch (IO-MM). The third observer was an anthropomorphic Channelized Hotelling Observer (CHO), which operated on reconstructed images. The projection-domain observers have the advantage that they are computationally less intensive. The IO has perfect knowledge of the image formation process, i.e. it has a perfect model of the CDR. The IO-MM takes into account the mismatch between the true (complete and accurate) model and an approximate model, e.g. one that might be used in reconstruction. We evaluated the utility of these projection domain observers in optimizing instrumentation parameters. We investigated a family of 8 parallel-hole collimators, spanning a wide range of resolution and sensitivity tradeoffs, using a population of simulated projection (for the IO and IO-MM) and reconstructed (for the CHO) images that included background variability. We simulated anterolateral and inferior perfusion defects with variable extents and severities. The area under the ROC curve was estimated from the IO, IO-MM, and CHO test statistics and served as the figure-of-merit. The optimal collimator for the IO had a resolution of 9–11 mm FWHM at 10 cm, which is poorer resolution than typical collimators used for MPS. When the IO-MM and CHO used a geometric or no model of the CDR, the optimal collimator shifted toward higher resolution than that obtained using the IO and the CHO with full CDR modeling. With the optimal collimator, the IO-MM and CHO using geometric modeling gave similar performance to full CDR modeling. Collimators with poorer resolution were optimal when CDR modeling was used. The agreement of rankings between the IO-MM and CHO confirmed that the IO-MM is useful for optimization tasks when model mismatch is present due to its substantially reduced computational burden compared to the CHO.

Anthropomorphic model observer performance in three-dimensional detection task for low-contrast computed tomography.

X-ray medical imaging is increasingly becoming three-dimensional (3-D). The dose to the population and its management are of special concern in computed tomography (CT). Task-based methods with model observers to assess the dose-image quality trade-off are promising tools, but they still need to be validated for real volumetric images. The purpose of the present work is to evaluate anthropomorphic model observers in 3-D detection tasks for low-contrast CT images. We scanned a low-contrast phantom containing four types of signals at three dose levels and used two reconstruction algorithms. We implemented a multislice model observer based on the channelized Hotelling observer (msCHO) with anthropomorphic channels and investigated different internal noise methods. We found a good correlation for all tested model observers. These results suggest that the msCHO can be used as a relevant task-based method to evaluate low-contrast detection for CT and optimize scan protocols to lower dose in an efficient way.

Eye-tracking of nodule detection in lung CT volumetric data.

Signal detection on 3D medical images depends on many factors, such as foveal and peripheral vision, the type of signal, and background complexity, and the speed at which the frames are displayed. In this paper, the authors focus on the speed with which radiologists and naïve observers search through medical images. Prior to the study, the authors asked the radiologists to estimate the speed at which they scrolled through CT sets. They gave a subjective estimate of 5 frames per second (fps). The aim of this paper is to measure and analyze the speed with which humans scroll through image stacks, showing a method to visually display the behavior of observers as the search is made as well as measuring the accuracy of the decisions. This information will be useful in the development of model observers, mathematical algorithms that can be used to evaluate diagnostic imaging systems. The authors performed a series of 3D 4-alternative forced-choice lung nodule detection tasks on volumetric stacks of chest CT images iteratively reconstructed in lung algorithm. The strategy used by three radiologists and three naïve observers was assessed using an eye-tracker in order to establish where their gaze was fixed during the experiment and to verify that when a decision was made, a correct answer was not due only to chance. In a first set of experiments, the observers were restricted to read the images at three fixed speeds of image scrolling and were allowed to see each alternative once. In the second set of experiments, the subjects were allowed to scroll through the image stacks at will with no time or gaze limits. In both static-speed and free-scrolling conditions, the four image stacks were displayed simultaneously. All trials were shown at two different image contrasts. The authors were able to determine a histogram of scrolling speeds in frames per second. The scrolling speed of the naïve observers and the radiologists at the moment the signal was detected was measured at 25-30 fps. For the task chosen, the performance of the observers was not affected by the contrast or experience of the observer. However, the naïve observers exhibited a different pattern of scrolling than the radiologists, which included a tendency toward higher number of direction changes and number of slices viewed. The authors have determined a distribution of speeds for volumetric detection tasks. The speed at detection was higher than that subjectively estimated by the radiologists before the experiment. The speed information that was measured will be useful in the development of 3D model observers, especially anthropomorphic model observers which try to mimic human behavior.

Optimization of energy window and evaluation of scatter compensation methods in myocardial perfusion SPECT using the ideal observer with and without model mismatch and an anthropomorphic model observer.

We used the ideal observer (IO) and IO with model mismatch (IO-MM) applied in the projection domain and an anthropomorphic channelized Hotelling observer (CHO) applied to reconstructed images to optimize the acquisition energy window width and to evaluate various scatter compensation methods in the context of a myocardial perfusion single-photon emission computed tomography (SPECT) defect detection task. The IO has perfect knowledge of the image formation process and thus reflects the performance with perfect compensation for image-degrading factors. Thus, using the IO to optimize imaging systems could lead to suboptimal parameters compared with those optimized for humans interpreting SPECT images reconstructed with imperfect or no compensation. The IO-MM allows incorporating imperfect system models into the IO optimization process. We found that with near-perfect scatter compensation, the optimal energy window for the IO and CHO was similar; in its absence, the IO-MM gave a better prediction of the optimal energy window for the CHO using different scatter compensation methods. These data suggest that the IO-MM may be useful for projectiondomain optimization when MM is significant and that the IO is useful when followed by reconstruction with good models of the image formation process.

Derivation of an Observer Model Adapted to Irregular Signals Based on Convolution Channels.

Anthropomorphic model observers are mathe- matical algorithms which are applied to images with the ultimate goal of predicting human signal detection and classification accuracy across varieties of backgrounds, image acquisitions and display conditions. A limitation of current channelized model observers is their inability to handle irregularly-shaped signals, which are common in clinical images, without a high number of directional channels. Here, we derive a new linear model observer based on convolution channels which we refer to as the "Filtered Channel observer" (FCO), as an extension of the channelized Hotelling observer (CHO) and the nonprewhitening with an eye filter (NPWE) observer. In analogy to the CHO, this linear model observer can take the form of a single template with an external noise term. To compare with human observers, we tested signals with irregular and asymmetrical shapes spanning the size of lesions down to those of microcalfications in 4-AFC breast tomosynthesis detection tasks, with three different contrasts for each case. Whereas humans uniformly outperformed conventional CHOs, the FCO observer outperformed humans for every signal with only one exception. Additive internal noise in the models allowed us to degrade model performance and match human performance. We could not match all the human performances with a model with a single internal noise component for all signal shape, size and contrast conditions. This suggests that either the internal noise might vary across signals or that the model cannot entirely capture the human detection strategy. However, the FCO model offers an efficient way to apprehend human observer performance for a non-symmetric signal.

Optimization and evaluation of reconstruction-based compensation methods and reconstruction parameters for Tc-99m MIBI parathyroid SPECT

The value of Tc-99m MIBI parathyroid SPECT for localizing parathyroid hyperplasia in chronic renal failure patients remains inconclusive due to limited image quality. Advanced reconstruction methods to improve image quality have been developed but require optimization and evaluation. The goal of this study was to optimize and evaluate compensation methods and reconstruction parameters for Tc-99m MIBI parathyroid SPECT. A phantom population and projection data that modelled clinically realistic variations found in patients were simulated. The 3D OS-EM reconstruction with compensation for attenuation, detector response and scatter in various combinations were studied. For each compensation, the number of updates for OS-EM and the cutoff frequency of a 3D Butterworth filter were optimized and evaluated using anthropomorphic model observer. With optimal parameters, the method with compensation for attenuation and detector response, with or without the addition of scatter compensation, provided the highest lesion detectability for Tc-99m MIBI parathyroid SPECT.

Incorporating Human Contrast Sensitivity in Model Observers for Detection Tasks

Contrast sensitivity of the human visual system is a characteristic that can adversely affect human performance in detection tasks. In this paper, we propose a method for incorporating human contrast sensitivity in anthropomorphic model observers. In our method, we model human contrast sensitivity using the Barten model with the mean luminance of a region of interest centered at the signal location. In addition, one free parameter is varied to control the effect of the contrast sensitivity on the model observer's performance. We investigate our model of human contrast sensitivity in a channelized-Hotelling observer (CHO) with difference-of-Gaussian channels. We call the CHO incorporating the contrast sensitivity a contrast-sensitive CHO (CS-CHO). The human data from a psychophysical study by Park et al. [1] are used for comparing the performance of the CS-CHO to human performance. That study used Gaussian signals with six different signal intensities in non-Gaussian lumpy backgrounds. A value of the free parameter is chosen to match the performance of the CS-CHO to the mean human performance only at the strongest signal. Results show that the CS-CHO with the chosen value of the free parameter predicts the mean human performance at the five lower signal intensities. Our results show that the CS-CHO predicts human performance well as a function of signal intensity.