In this paper, we apply the channelized Hotelling observer (CHO) using a defect detection task to the optimization and evaluation of three-dimensional iterative reconstruction-based compensation methods for myocardial perfusion single-photon emission computed tomography (SPECT). We used a population of 24 mathematical cardiac-torso phantoms that realistically model the activity and attenuation distribution in three classes of patients: females, and males with flat diaphragms and raised diaphragms. Projection data were generated and subsequently reconstructed using methods based on the ordered subsets-expectation maximization (OSEM) algorithm. The methods evaluated included compensation for attenuation, detector response blurring, and scatter in various combinations. We applied the CHO to optimize the number of iterations for OSEM and the cutoff frequency and order of a three-dimensional postreconstruction Butterworth filter. Using the optimal parameters, we then compared the compensation methods. The index of comparison in these studies was the area under the receiver operating characteristics curve (AUC) for the CHO. We found that attenuation compensation with either detector response or scatter compensation gave statistically significant increases in the AUC compared to attenuation compensation alone. The greatest increase in the AUC occurred when all three compensations were applied. These results indicate that compensation for detector response and scatter, in addition to attenuation compensation, will improve defect detectability in myocardial SPECT images.

The development and expansion of wind energy is considered a key global threat to bat populations. Bat carcasses are being found underneath wind turbines across North and South America, Eurasia, Africa, and the Austro‐Pacific. However, relatively little is known about the comparative impacts of techniques designed to modify turbine operations in ways that reduce bat fatalities associated with wind energy facilities. This study tests a novel approach for reducing bat fatalities and curtailment time at a wind energy facility in the United States, then compares these results to operational mitigation techniques used at other study sites in North America and Europe. The study was conducted in Wisconsin during 2015 using a new system of tools for analyzing bat activity and wind speed data to make near real‐time curtailment decisions when bats are detected in the area at control turbines (N = 10) vs. treatment turbines (N = 10). The results show that this smart curtailment approach (referred to as Turbine Integrated Mortality Reduction, TIMR) significantly reduced fatality estimates for treatment turbines relative to control turbines for pooled species data, and for each of five species observed at the study site: pooled data (–84.5%); eastern red bat (Lasiurus borealis, –82.5%); hoary bat (Lasiurus cinereus, –81.4%); silver‐haired bat (Lasionycteris noctivagans, –90.9%); big brown bat (Eptesicus fuscus, –74.2%); and little brown bat (Myotis lucifugus, –91.4%). The approach reduced power generation and estimated annual revenue at the wind energy facility by ≤ 3.2% for treatment turbines relative to control turbines, and we estimate that the approach would have reduced curtailment time by 48% relative to turbines operated under a standard curtailment rule used in North America. This approach significantly reduced fatalities associated with all species evaluated, each of which has broad distributions in North America and different ecological affinities, several of which represent species most affected by wind development in North America. While we recognize that this approach needs to be validated in other areas experiencing rapid wind energy development, we anticipate that this approach has the potential to significantly reduce bat fatalities in other ecoregions and with other bat species assemblages in North America and beyond.

The goal of this study was to develop and apply a population of phantoms that realistically models patient variability and use it to optimize and evaluate different compensation methods used during reconstruction process with respect to defect detection in myocardial SPECT images. Various combinations of attenuation, detector response and scatter compensation were used in this study. A major difference between this and previous studies was that the level of realism was significantly increased by inclusion of variability in heart and organ uptakes, in the heart size and orientation, and in the defect size and contrast. In this study we used a population of 24 4-D NCAT phantoms [1] (half male, half female) recently developed with statistical models for organ uptake and organ size based on clinical data. Almost noise-free projection data of the torso, heart, liver, lungs, and other organs were simulated for each phantom using the SIMSET MC simulation code. They were then combined to form 72 sets of projections for each phantom using randomly sampled activity ratios from a clinically realistic distribution. Poisson noise was then added to the projection data. We applied the channelized hotelling observer (CHO) and receiver operating characteristic (ROC) analysis to optimize iteration number for OSEM and cutoff frequency of a 3-D post-reconstruction Butterworth filter. We found that the area under the ROC curve (AUC) values were reduced compared to a previous study that included significantly less phantom variability, even though the defect contrast was higher and noise level was lower. The resulting AUC values were similar to those obtained using patient data. We found, in agreement with the previous study, that including compensation for more effects resulted in improved defect detectability. However, the optimal filter cutoff frequency was increased compared to the previous study. These studies demonstrate the importance of including realistic levels of phantom variability in myocardial perfusion studies using simulated data.

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The performance of the Channelized Hotelling Observer (CHO) was compared to that of human observers for determining optimum parameters for the iterative OS-EM image reconstruction method for the task of defect detection in myocardial SPECT images. The optimum parameters were those that maximized defect detectability in the SPECT images. Low noise, parallel SPECT projection data, with and without an anterior, inferior or lateral LV wall defect, were simulated using the Monte Carlo method. Poisson noise was added to generate noisy realizations. Data were reconstructed using OS-EM at 1 & 4 subsets/iteration and at 1, 3, 5, 7 & 9 iterations. Images were converted to 2D short-axis slices with integer pixel values. The CHO used 3 radially-symmetric, 2D channels, with varying levels of internal observer noise. For each parameter setting, 600 defect-present and 600 defect-absent image vectors were used to calculate the detectability index (dA). The human observers rated the likelihood that a defect was present in a specified location. For each parameter setting, the AUC was estimated from 48 defect-present and 48 defect-absent images. The combined human observer results showed the optimum parameter setting could be in the range 5-36 updates ([number of subsets]/iteration × number of iterations). The CHO results showed the optimum parameter setting to be 4-5 updates. The performance of the CHO was much more sensitive to the reconstruction parameter setting than was that of the human observers. The rankings of the CHO detectability values did not change with varying levels of internal noise. The CHO results showed the optimum parameter setting to be 4-5 updates. The performance of the CHO was much more sensitive to the reconstruction parameter setting than was that of the human observers. The rankings of the CHO detectability values did not change with varying levels of internal noise.