title: Automated selection of the optimal cardiac phase for single-beat coronary CT angiography reconstruction.
abstract: OBJECTIVE
  Reconstructing a low-motion cardiac phase is expected to improve coronary artery visualization in coronary computed tomography angiography (CCTA) exams. This study developed an automated algorithm for selecting the optimal cardiac phase for CCTA reconstruction. The algorithm uses prospectively gated, single-beat, multiphase data made possible by wide cone-beam imaging. The proposed algorithm differs from previous approaches because the optimal phase is identified based on vessel image quality (IQ) directly, compared to previous approaches that included motion estimation and interphase processing. Because there is no processing of interphase information, the algorithm can be applied to any sampling of image phases, making it suited for prospectively gated studies where only a subset of phases are available.
  
  
  METHODS
  An automated algorithm was developed to select the optimal phase based on quantitative 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 cardiac CT exams (Revolution CT, GE Healthcare, Chalfont St. Giles, UK) reconstructed at 2% intervals were evaluated for best systolic (1), diastolic (6), or systolic and diastolic phases (7) by three readers and the algorithm. Pairwise inter-reader and reader-algorithm agreement was evaluated using the mean absolute difference (MAD) and concordance correlation coefficient (CCC) between the reader and algorithm-selected phases. 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 five point Likert scale.
  
  
  RESULTS
  There was no statistically significant difference between inter-reader and reader-algorithm agreement for either MAD or CCC metrics (p > 0.1). The algorithm phase was within 2% of the consensus phase in 15/21 of cases. The average absolute difference between consensus and algorithm best phases was 2.29% ± 2.47%, with a maximum difference of 8%. Average image quality scores for the algorithm chosen best phase were 4.01 ± 0.65 overall, 3.33 ± 1.27 for right coronary artery (RCA), 4.50 ± 0.35 for left anterior descending (LAD) artery, and 4.50 ± 0.35 for left circumflex artery (LCX). Average image quality scores for the consensus best phase were 4.11 ± 0.54 overall, 3.44 ± 1.03 for RCA, 4.39 ± 0.39 for LAD, and 4.50 ± 0.18 for LCX. There was no statistically significant difference (p > 0.1) between the image quality scores of the algorithm phase and the consensus phase.
  
  
  CONCLUSIONS
  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%, image quality as rated by blinded readers was statistically equivalent. By detecting the optimal phase for CCTA reconstruction, the proposed algorithm is expected to improve coronary artery visualization in CCTA exams.
authors.affiliation: Department of Biomedical Engineering, Marquette University, Milwaukee, Wisconsin 53201.
authors.forename: D
authors.initials: D
authors.lastname: Stassi
authors.affiliation: GE Healthcare, Waukesha, Wisconsin 53188.
authors.forename: S
authors.initials: S
authors.lastname: Dutta
authors.affiliation: Department of Biomedical Engineering, Marquette University, Milwaukee, Wisconsin 53201.
authors.forename: H
authors.initials: H
authors.lastname: Ma
authors.affiliation: GE Healthcare, Waukesha, Wisconsin 53188.
authors.forename: A
authors.initials: A
authors.lastname: Soderman
authors.affiliation: GE Healthcare, Waukesha, Wisconsin 53188.
authors.forename: D
authors.initials: D
authors.lastname: Pazzani
authors.affiliation: GE Healthcare, Waukesha, Wisconsin 53188.
authors.forename: E
authors.initials: E
authors.lastname: Gros
authors.affiliation: GE Healthcare, Waukesha, Wisconsin 53188.
authors.forename: D
authors.initials: D
authors.lastname: Okerlund
authors.affiliation: Department of Biomedical Engineering, Marquette University, Milwaukee, Wisconsin 53201.
authors.forename: T G
authors.initials: TG
authors.lastname: Schmidt
mesh_terms: D000465:Algorithms; D001331:Automation; D017023:Coronary Angiography; D006321:Heart / Q000000981:diagnostic imaging*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D019047:Phantoms, Imaging; D014057:Tomography, X-Ray Computed