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In this paper, we investigate the use of a numerical observer to optimize ordered-subset expectation maximization (OSEM) reconstructions for the detection of coronary artery disease (CAD). The parameters optimized were the iteration number and the full-width at half-maximum of three-dimensional Gaussian postfiltering. The numerical observer employed in the optimization was the channelized Hotelling observer (CHO). The CHO had been used previously to rank tumor detection accuracy for different reconstruction strategies in Ga-67 images, showing good agreement with the rankings of human observers. The intent of this paper was to determine if this CHO could also be employed for the detection of CAD. Results indicate that when grayscale (quantized) images are used, the CHO optimization results correlate well with human observers. On the other hand, when the CHO was used with floating-point images, it provided very good detection performance even when the images were excessively filtered. This result was at odds with the human-observer performance which showed a decrease in detection accuracy with highly smoothed images. This reflects the need to better model the detection task of the human observers who usually view and rank grayscale images and by appropriately modeling the image noise that quantization introduces, we show that the CHO can better match human-observer detection performance.