Image quality of automatic coronary CT angiography reconstruction for patients with HR ≥ 75 bpm using an AI-assisted 16-cm z-coverage CT scanner.
BMC Med Imaging
; 21(1): 24, 2021 02 11.
Article
en En
| MEDLINE
| ID: mdl-33573625
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.Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Enfermedad de la Arteria Coronaria
/
Interpretación de Imagen Radiográfica Asistida por Computador
/
Vasos Coronarios
/
Angiografía por Tomografía Computarizada
/
Frecuencia Cardíaca
Tipo de estudio:
Guideline
/
Observational_studies
/
Prognostic_studies
Límite:
Aged
/
Female
/
Humans
/
Male
/
Middle aged
Idioma:
En
Año:
2021
Tipo del documento:
Article