Automated selection of the optimal cardiac phase for single‐beat coronary CT angiography reconstruction

• Published in: Medical physics (Lancaster) 2016-01, Vol.43 (1), p.324-335
• Main Authors: Stassi, D., Dutta, S., Ma, H., Soderman, A., Pazzani, D., Gros, E., Okerlund, D., Schmidt, T. G.
• Format: Article
• Language: English
• Subjects: ALGORITHMS; Analysis of motion; angiocardiography; Angiography; Automation; Biological material, e.g. blood, urine; Haemocytometers; BIOMEDICAL RADIOGRAPHY; cardiac CT; Cardiac dynamics; Computed tomography; Computerised tomographs; computerised tomography; COMPUTERIZED TOMOGRAPHY; CORONARIES; Coronary Angiography; coronary arteries; CT angiography; Digital computing or data processing equipment or methods, specially adapted for specific applications; Heart; Heart - diagnostic imaging; Humans; Image data processing or generation, in general; IMAGE PROCESSING; Image Processing, Computer-Assisted - methods; image reconstruction; Medical image contrast; medical image processing; Medical image reconstruction; motion artifacts; motion estimation; Phantoms, Imaging; RADIATION PROTECTION AND DOSIMETRY; RADIOLOGY AND NUCLEAR MEDICINE; Reconstruction; SAMPLING; Statistical analysis; Tomography, X-Ray Computed; Vascular system
• ISSN: 0094-2405; 2473-4209
• DOI: 10.1118/1.4938265

Purpose: 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