Improving image quality of triple-low-protocol renal artery CT angiography with deep-learning image reconstruction: a comparative study with standard-dose single-energy and dual-energy CT with adaptive statistical iterative reconstruction.
Clin Radiol
; 79(5): e651-e658, 2024 May.
Article
em En
| MEDLINE
| ID: mdl-38433041
ABSTRACT
AIM:
To investigate the improvement in image quality of triple-low-protocol (low radiation, low contrast medium dose, low injection speed) renal artery computed tomography (CT) angiography (RACTA) using deep-learning image reconstruction (DLIR), in comparison with standard-dose single- and dual-energy CT (DECT) using adaptive statistical iterative reconstruction-Veo (ASIR-V) algorithm. MATERIALS ANDMETHODS:
Ninety patients for RACTA were divided into different groups standard-dose single-energy CT (S group) using ASIR-V at 60% strength (60%ASIR-V), DECT (DE group) with 60%ASIR-V including virtual monochromatic images at 40 keV (DE40 group) and 70 keV (DE70 group), and the triple-low protocol single-energy CT (L group) with DLIR at high level (DLIR-H). The effective dose (ED), contrast medium dose, injection speed, standard deviation (SD), signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of abdominal aorta (AA), and left/right renal artery (LRA, RRA), and subjective scores were compared among the different groups.RESULTS:
The L group significantly reduced ED by 37.6% and 31.2%, contrast medium dose by 33.9% and 30.5%, and injection speed by 30% and 30%, respectively, compared to the S and DE groups. The L group had the lowest SD values for all arteries compared to the other groups (p<0.001). The SNR of RRA and LRA in the L group, and the CNR of all arteries in the DE40 group had highest value compared to others (p<0.05). The L group had the best comprehensive score with good consistency (p<0.05).CONCLUSIONS:
The triple-low protocol RACTA with DLIR-H significantly reduces the ED, contrast medium doses, and injection speed, while providing good comprehensive image quality.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Angiografia por Tomografia Computadorizada
/
Aprendizado Profundo
Limite:
Humans
Idioma:
En
Revista:
Clin Radiol
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
China