1 research outputs found
Automated quantification and evaluation of motion artifact on coronary CT angiography images
Abstract Purpose
This study developed and validated a Motion Artifact Quantification algorithm to automatically quantify the severity of motion artifacts on coronary computed tomography angiography (CCTA) images. The algorithm was then used to develop a Motion IQ Decision method to automatically identify whether a CCTA dataset is of sufficient diagnostic image quality or requires further correction. Method
The developed Motion Artifact Quantification algorithm includes steps to identify the right coronary artery (RCA) regions of interest (ROIs), segment vessel and shading artifacts, and to calculate the motion artifact score (MAS) metric. The segmentation algorithms were verified against groundātruth manual segmentations. The segmentation algorithms were also verified by comparing and analyzing the MAS calculated from groundātruth segmentations and the algorithmāgenerated segmentations. The Motion IQ Decision algorithm first identifies slices with unsatisfactory image quality using a MAS threshold. The algorithm then uses an artifactālength threshold to determine whether the degraded vessel segment is large enough to cause the dataset to be nondiagnostic. An observer study on 30 clinical CCTA datasets was performed to obtain the groundātruth decisions of whether the datasets were of sufficient image quality. A fiveāfold crossāvalidation was used to identify the thresholds and to evaluate the Motion IQ Decision algorithm. Results
The automated segmentation algorithms in the Motion Artifact Quantification algorithm resulted in Dice coefficients of 0.84 for the segmented vessel regions and 0.75 for the segmented shading artifact regions. The MAS calculated using the automated algorithm was within 10% of the values obtained using groundātruth segmentations. The MAS threshold and artifactālength thresholds were determined by the ROC analysis to be 0.6 and 6.25 mm by all folds. The Motion IQ Decision algorithm demonstrated 100% sensitivity, 66.7% Ā± 27.9% specificity, and a total accuracy of 86.7% Ā± 12.5% for identifying datasets in which the RCA required correction. The Motion IQ Decision algorithm demonstrated 91.3% sensitivity, 71.4% specificity, and a total accuracy of 86.7% for identifying CCTA datasets that need correction for any of the three main vessels. Conclusion
The Motion Artifact Quantification algorithm calculated accurate