Showing papers by "Michael W. Vannier published in 2008"

Chord-based image reconstruction from clinical projection data

Abstract: Chord-based algorithms can eliminate cone-beam artifacts in images reconstructed from a clinical computed tomography (CT) scanner. The feasibility of using chord-based reconstruction algorithms was evaluated with three clinical CT projection data sets. The first projection data set was acquired using a clinical 64-channel CT scanner (Philips Brilliance 64) that consisted of an axial scan from a quality assurance phantom. Images were reconstructed using (1) a full-scan FDK algorithm, (2) a short-scan FDK algorithm, and (3) the chord-based backprojection filtration algorithm (BPF) using full-scan data. The BPF algorithm was capable of reproducing the morphology of the phantom quite well, but exhibited significantly less noise than the two FDK reconstructions as well as the reconstruction obtained from the clinical scanner. The second and third data sets were obtained from scans of a head phantom and a patient's thorax. For both of these data sets, the BPF reconstructions were comparable to the short-scan FDK reconstructions in terms of image quality, although sharper features were indistinct in the BPF reconstructions. This research demonstrates the feasibility of chord-based algorithms for reconstructing images from clinical CT projection data sets and provides a framework for implementing and testing algorithmic innovations.

Computerized assessment of coronary calcified plaques in CT images of a dynamic cardiac phantom

Abstract: 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.