Proceedings Article | 17 March 2008
KEYWORDS: Computed tomography, Beam propagation method, Heart, Image quality, Image segmentation, Motion models, Current controlled current source, Data modeling, Arteries, Artificial neural networks
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.
