Daniel Stassi, Presenter: Research funded, General Electric Company

Sandeep Dutta PhD, Abstract Co-Author: Employee, General Electric Company

Ann Soderman, Abstract Co-Author: Employee, General Electric company

Dave Pazzani, Abstract Co-Author: Employee, General Electric company

Darin R. Okerlund MS, Abstract Co-Author: Employee, General Electric Company

Taly Gilat Schmidt PhD, Abstract Co-Author: Research funded, General Electric Company

The purpose of this study was to investigate 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.

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. 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>0.1). The algorithm phase was within 2% of the consensus phase in 76% of cases. There was no significant difference (p>0.1) 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.

By detecting the optimal phase for CCTA reconstruction, the proposed algorithm can improve coronary artery visualization in CCTA exams.

Stassi, D, Dutta, S, Soderman, A, Pazzani, D, Okerlund, D, Schmidt, T, Automated Selection of the Optimal Cardiac Phase for Single-beat Coronary CT Angiography.  Radiological Society of North America 2014 Scientific Assembly and Annual Meeting, - ,Chicago IL. http://archive.rsna.org/2014/14010717.html