Motion artifacts in cardiac CT are an obstacle to obtaining diagnostically usable images. Although phase-specific reconstruction can produce images with improved assessability (image quality), this requires that the radiologist spend time and effort evaluating multiple image sets from reconstructions at different phases. In this study, ordinal logistic regression (OLR) and artificial neural network (ANN) models were used to automatically assign assessability to images of coronary calcified plaques obtained using a physical, dynamic cardiac phantom. 350 plaque images of 7 plaques from five data sets (heart rates 60, 60, 70, 80, 90) and ten phases of reconstruction were obtained using standard cardiac CT scanning parameters on a Phillips Brilliance 64-channel clinical CT scanner. Six features of the plaques (velocity, acceleration, edge-based volume, threshold-based volume, sphericity, and standard deviation of intensity) as well as mean feature values and heart rate were used for training the OLR and ANN in a round-robin re-sampling scheme based on training and testing groups with independent plaques. For each image, an ordinal assessability index rating on a 1-5 scale was assigned by a cardiac radiologist (D.B.) for use as a "truth" in training the OLR and ANN. The mean difference between the assessability index truth and model-predicted assessability index values was +0.111 with SD=0.942 for the OLR and +0.143 with SD=0.916 for the ANN. Comparing images from the repeat 60 bpm scans gave concordance correlation coefficients (CCCs) of 0.794 [0.743, 0.837] (value, 95% CI) for the radiologist assigned values, 0.894 [0.856, 0.922] for the OLR, and 0.861 [0.818, 0.895] for the ANN. Thus, the variability of the OLR and ANN assessability index values appear to lie within the variability of the radiologist assigned values.