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

Abstract

Background: 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.Methods: 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 highsetting 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. Results: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 oversmoothed artery margins, but only the DLIR-H images provided acceptable scores on all 3 aspects of the qualitative image quality evaluation.Conclusions: 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.

Artificial Intelligence for Radiation Dose Optimization in Pediatric Radiology: A Systematic Review

“…Sixteen articles met the selection criteria and were included in this review. Table 1 shows these study characteristics [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. All but one article were published in the last two years, representing that the AI for dose optimization in pediatric radiology has only just become popular [ 1 , 2 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ].…”

“…Radiology is an indispensable part of modern healthcare. However, most of the medical imaging modalities, such as computed tomography (CT), positron emission tomography (PET), and general radiography, use ionizing radiation for image production [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. Although the radiation dose involved in these imaging modalities is low (<100 mSv), and their real risk is unclear, some epidemiologic and biologic studies have demonstrated that these radiological examinations can cause cancers [ 17 , 18 , 19 , 20 , 21 , 22 , 23 ].…”

“…Since the introduction of digital medical imaging, image processing has played an important role in the radiation dose optimization [ 37 , 38 , 39 ]. However, typical image processing techniques are unable to overcome the tradeoff between image noise and spatial resolution [ 9 , 10 , 11 , 12 ]. For the last few years, artificial intelligence (AI) has been introduced into radiology for radiation dose optimization.…”

“…For the last few years, artificial intelligence (AI) has been introduced into radiology for radiation dose optimization. Studies have demonstrated its ability in pushing the limit, i.e., able to further reduce the radiation dose but without sacrificing image quality, such as noise and spatial resolution [ 1 , 6 , 9 , 10 , 11 , 12 , 15 , 16 ].…”

The influence of a deep learning image reconstruction algorithm on the image quality and auto-analysis of pulmonary nodules at ultra-low dose chest CT: a phantom study

“…With the increased use of machine learning as a subset of artificial intelligence, a deep learning image reconstruction (DLIR) algorithm (TrueFidelity, GE Healthcare) has been introduced and showed great potential in medical imaging (17)(18)(19). Deep learning-based image reconstruction technology in general can suppress image noise while minimizing the change in noise texture or anatomical and pathological structures (20,21).…”