Image quality of automatic coronary CT angiography reconstruction for patients with HR ≥ 75 bpm using an AI-assisted 16-cm z-coverage CT scanner

Abstract

Background Coronary CT angiography (CCTA) is a complicated CT exam in comparison to other CT protocols. Exam success highly depends on image assessment of experienced radiologist and the procedure is often time-consuming. This study aims to evaluate feasibility of automatic CCTA reconstruction in 0.25 s rotation time, 16 cm coverage CT scanner with best phase selection and AI-assisted motion correction. Methods CCTA exams of 90 patients with heart rates higher than 75 bpm were included in this study. Two image series were reconstructed—one at automatically selected phase and another with additional motion correction. All reconstructions were performed without manual interaction of radiologist. A four-point Likert scale rating system was used to evaluate the image quality of coronary artery segment by two experienced radiologists, according to the 18-segment model. Analysis was done on per-segment basis. Results Total 1194 out of the 1620 segments were identified for quality evaluation in 90 patients. After automatic best phase selection, 1172 segments (98.3%) were rated as having diagnostic image quality (scores 2–4) and the average score is 3.64 ± 0.55. When motion corrections were applied, diagnostic segment number increases to 1192 (99.8%) and the average score is 3.85 ± 0.37. Conclusions With the help of 0.25 s rotation speed, 16-cm z-coverage and AI-assisted motion correction algorithm, CCTA exam reconstruction could be performed with minimum radiologist involvement and still meet image quality requirement.

Refining the radiation and contrast medium dose in weight‐grouped scanning protocols for coronary CT angiography

“…After scan, an artificial intelligence (AI)‐based software algorithm (CardioCapture; United Imaging Healthcare; Shanghai; China) was used for motion correction 12 . Next, all CT images were reconstructed with a routinely available hybrid iterative algorithm (Karl 3D; United Imaging Healthcare; Shanghai; China), with a 512 × 512 pixels matrix, 0.5 mm slice thickness, and 0.5 mm slice interval.…”

“…[8][9][10] In addition to the hardware, software solutions by means of motion correction in image post processing have also become a common choice, although the principle of various correction algorithms as well as the scanners may both largely differ. [11][12][13] Efforts have also been made in optimizing the vessel enhancement in order to improve the image quality of CCTA. [14][15][16] Mihl et al evaluated a body weight adapted protocol for contrast injection, where a more homogeneous enhancement pattern was achieved between different weight groups while the volume of contrast media could be reduced for the majority of patients.…”

“…While applying extra heart rate control via medication and implementing the so‐called retrospective acquisition mode are both efficient measures, the use of a high‐speed wide‐detector CT scanner certainly has its advantage for the ability of acquiring all necessary data within one single heartbeat and less than one rotation 8–10 . In addition to the hardware, software solutions by means of motion correction in image post processing have also become a common choice, although the principle of various correction algorithms as well as the scanners may both largely differ 11–13 …”

Improvement in Image Quality and Visibility of Coronary Arteries, Stents, and Valve Structures on CT Angiography by Deep Learning Reconstruction

“…The radiologists were blinded to the image reconstruction algorithms and randomly evaluated the CTA images; the results were averaged for subjective analysis. A five-point Likert scale was used for image analysis in the following six aspects: overall image quality, image noise, and appearance of stent, vessel, and aortic and tricuspid valve apparatus (including annulus, leaflets, papillary muscles, and chordae tendineae) ( Table 1 ) [ 12 13 ].…”

Effect of 320-row CT reconstruction technology on fractional flow reserve derived from coronary CT angiography based on machine learning: single- versus multiple-cardiac periodic images

“…Image quality was evaluated using a four-point grading system comprising four levels: poor, medium, good, and excellent (12,13). Only images of good or excellent quality in both single-and multiple-cardiac periodic images were included.…”