Date of Award

Summer 2014

Document Type


Degree Name

Master of Science (MS)


Biomedical Engineering

First Advisor


Second Advisor

Dutta, Sandeep

Third Advisor

LaDisa, John


This thesis investigates an automated algorithm for selecting the optimal cardiac phase for CCTA reconstruction. Reconstructing a low-motion cardiac phase improves coronary artery visualization in coronary CT angiography (CCTA) exams. Currently, standard end-systole and/or mid-diastole default phases are prescribed or alternatively, quiescent phases are determined by the user. As manual selection may be time-consuming and standard locations may be suboptimal due to patient variability, an automated method is investigated. An automated algorithm was developed to select the optimal phase based on quantitative image quality (IQ) metrics. For each reconstructed slice at each reconstructed phase, an image quality metric was calculated based on measures of circularity and edge strength of through-plane vessels. The image quality metric was aggregated across slices, while a metric of vessel-location consistency was used to ignore slices that did not contain through-plane vessels. A binary metric based on the edge strength of in-plane vessels was calculated to determine if IQ of in-plane vessels was acceptable. The algorithm performance was evaluated using two observer studies. Fourteen single-beat CCTA exams (Revolution CT, GE Healthcare) reconstructed at 2% intervals were evaluated for best systolic (1), diastolic (6), or systolic and diastolic phases (7) by three readers and the algorithm. Inter-reader (RR) and reader-algorithm (RA) agreement was calculated using the mean absolute difference (MAD) and concordance correlation coefficient (CCC). A reader-consensus best phase was determined and compared to the algorithm selected phase. In cases where the algorithm and consensus best phases differed by more than 2%, IQ was scored by three readers using a 5pt Likert scale. There was no significant difference between RR and RA agreement for either MAD or CCC metrics (p>0.2). The algorithm phase was within 2% of the consensus phase in 71% of cases. There was no significant difference (p>0.2) between the IQ of the algorithm phase (4.06±0.73) and the consensus phase (4.11±0.76). The proposed algorithm was statistically equivalent to a reader in selecting an optimal cardiac phase for CCTA exams. When reader and algorithm phases differed by >2%, IQ was statistically equivalent.