Improving the image quality of pediatric chest CT angiography with low radiation dose and contrast volume using deep learning image reconstruction.

Abstract

Chest CT angiography (CTA) is a common clinical examination technique for children. Iterative reconstruction algorithms are often used to reduce image noise but encounter limitations under low dose conditions. Deep learning-based image reconstruction algorithms have been developed to overcome these limitations. We assessed the quantitative and qualitative image quality of thin-slice chest CTA in children acquired with low radiation dose and contrast volume by using a deep learning image reconstruction (DLIR) algorithm. A total of 33 children underwent chest CTA with 70 kVp and automatic tube current modulation for noise indices of 11-15 based on their age and contrast volume of 0.8-1.2 mL/kg. Images were reconstructed with 50% and 100% adaptive statistical iterative reconstruction-V (ASIR-V) and high-setting DLIR (DLIR-H) at 0.625 mm slice thickness. Two radiologists evaluated images in consensus for overall image noise, artery margin, and artery contrast separately on a 5-point scale (5, excellent; 4, good; 3, acceptable; 2, sub-acceptable, and 1, not acceptable). The CT value and image noise of the descending aorta and back muscle were measured. Radiation dose and contrast volume was recorded. The volume CT dose index, dose length product, and contrast volume were 1.37±0.29 mGy, 35.43±10.59 mGy·cm, and 25.43±13.32 mL, respectively. The image noises (in HU) of the aorta with DLIR-H (19.24±5.77) and 100% ASIR-V (20.45±6.93) were not significantly different (P>0.05) and were substantially lower than 50% ASIR-V (29.45±7.59) (P<0.001). The 100% ASIR-V images had over-smoothed artery margins, but only the DLIR-H images provided acceptable scores on all 3 aspects of the qualitative image quality evaluation. It is feasible to improve the image quality of a low radiation dose and contrast volume chest CTA in children using the high-setting DLIR algorithm.