Premium
Computerized method for evaluating diagnostic image quality of calcified plaque images in cardiac CT: Validation on a physical dynamic cardiac phantom
Author(s) -
King Martin,
Rodgers Zachary,
Giger Maryellen L.,
Bardo Dianna M. E.,
Patel Amit R.
Publication year - 2010
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.3495684
Subject(s) - imaging phantom , cardiac imaging , image quality , artificial intelligence , resampling , scanner , medicine , computer science , nuclear medicine , computer vision , radiology , image (mathematics)
Purpose: In cardiac computed 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 software NCAT 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 (AUC OBS 1 = 0.80[ 0.73 , 0.86 ]vsAUC ANN 1 = 0.88[ 0.82 , 0.92 ] ) as that of the second observer and the corresponding ANN (AUC OBS 2 = 0.87[ 0.83 , 0.91 ]vsAUC ANN 2 = 0.90[ 0.85 , 0.94 ] ). 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 cardiac CT images.