In cardiaccomputed tomography(CT), important clinical indices, such as the coronary calcium score and the percentage of coronary artery stenosis, are often adversely affected by motion artifacts. As a result, the expert observer must decide whether or not to use these indices during image interpretation. Computerized methods potentially can be used to assist in these decisions. In a previous study, an artificial neural network (ANN) regression model provided assessability (image quality) indices of calcified plaque images from the softwareNCAT phantom that were highly agreeable with those provided by expert observers. The method predicted assessability indices based on computer-extracted features of the plaque. In the current study, the ANN-predicted assessability indices were used to identify calcified plaque images with diagnostic calcium scores (based on mass) from a physical dynamic cardiac phantom. The basic assumption was that better quality images were associated with more accurate calcium scores.Methods:
A 64-channel CT scanner was used to obtain 500 calcified plaque images from a physical dynamic cardiac phantom at different heart rates, cardiac phases, and plaque locations. Two expert observers independently provided separate sets of assessability indices for each of these images. Separate sets of ANN-predicted assessability indices tailored to each observer were then generated within the framework of a bootstrap resampling scheme. For each resampling iteration, the absolute calcium score error between the calcium scores of the motion-contaminated plaque image and its corresponding stationary image served as the ground truth in terms of indicating images with diagnostic calcium scores. The performances of the ANN-predicted and observer-assigned indices in identifying images with diagnostic calcium scores were then evaluated using ROC analysis.Results:
Assessability indices provided by the first observer and the corresponding ANN performed similarly ( vs ) as that of the second observer and the corresponding ANN ( vs ). Moreover, the ANN-predicted indices were generated in a fraction of the time required to obtain the observer-assigned indices.Conclusions:
ANN-predicted assessability indices performed similar to observer-assigned assessability indices in identifying images with diagnostic calcium scores from the physical dynamic cardiac phantom. The results of this study demonstrate the potential of using computerized methods for identifying images with diagnostic clinical indices in cardiacCTimages.
This work was supported in part by the National Institutes of Health Medical Scientist Training Program Grant, National Institutes of Health Grant Nos. EB00225 and EB02765, as well as the Lawrence H. Lanzl Graduate Student Fellowship in Medical Physics (Committee on Medical Physics, The University of Chicago). The authors would like to thank Dr. Michael Vannier for his tremendous help in acquiring the cardiac phantom, Philips Medical Systems for loaning the cardiac phantom, Arkadiusz Wdowiak for the aid that he provided in scanning the phantom, Lorenzo Pesce for helpful discussions regarding the statistical analysis needed for this project, Kenji Suzuki for providing the artificial neural network model, and Li Lan for designing the workstation used by the expert physician observers.
II. MATERIALS AND METHODS
II.A. Dynamic cardiac phantom and calcified plaque model
II.B. Image acquisition
II.C. Ground truths
II.C.1. Assessability indices
II.C.2. Coronary calcium score
II.D. Computerized method for assigning assessability indices
II.D.2. Feature extraction
II.D.3. ANN regression model
II.E. Performance evaluation
II.E.1. Performance metrics
II.E.2. Resampling scheme
II.E.3. Subgroup analysis
III.A. Performance metrics
III.B. Analysis of assessability indices for two phase-correlated image sets
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