ArticleQuantitative imaging in medicine and surgery2021
Improving the image quality of pediatric chest CT angiography with low radiation dose and contrast volume using deep learning image reconstruction.
Article in Quantitative imaging in medicine and surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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24 citing papers in PubMed, 39 citations in OpenAlex.
- Deep learning reconstruction combined with contrast-enhancement boost in dual-low dose CT pulmonary angiography: a two-center prospective trial.European radiology · 2025Trial
- Article
- Improving image quality and diagnostic confidence for PRETEXT staging in pediatric hepatoblastoma using thin-slice and low-energy virtual monochromatic images in dual-energy CT with deep learning image reconstruction algorithm.BMC medical imaging · 2026Article
- Comparison of DLIR-H and ASIR algorithms for image reconstruction in low-dose chest CT of pediatric mycoplasma pneumoniae pneumonia: a cross-sectional study.Translational pediatrics · 2026Article
- Enhancing diagnostic safety with low iodine, low radiation CTPA classification using deep learning.Scientific reports · 2026Article
- Pediatric bronchial Dieulafoy's disease: a biopsy-sparing bronchoscopic approach with selective bronchial artery embolization.Frontiers in pediatrics · 2026Article
- Comparison of DLIR and ASIR-V algorithms for virtual monoenergetic imaging in carotid CTA under a triple-low protocol.Japanese journal of radiology · 2026Article
- Ultra-low 70-kV cardiac CT angiography in neonates: a comparative dose-optimized approach with preserved image quality.Frontiers in cardiovascular medicine · 2026Article
- Application value of abdominal ultrasonography in the diagnosis of pediatric patients aged 3-12 years with acute appendicitis.World journal of gastrointestinal surgery · 2025Article
- AI-Driven Advances in Low-Dose Imaging and Enhancement-A Review.Diagnostics (Basel, Switzerland) · 2025Review
- Patient-derived PixelPrint phantoms for evaluating clinical imaging performance of a deep learning CT reconstruction algorithm.Physics in medicine and biology · 2024Article
- Optimizing image quality and minimizing radiation dose in pediatric abdominal multiphase contrast-enhanced computed tomography: a study on CARE kV and CARE Dose 4D.Quantitative imaging in medicine and surgery · 2024Article
- Ultra-low-dose spectral-detector computed tomography for the accurate quantification of pulmonary nodules: an anthropomorphic chest phantom study.Diagnostic and interventional radiology (Ankara, Turkey) · 2023Article
- Generative Adversarial Network (Generative Artificial Intelligence) in Pediatric Radiology: A Systematic Review.Children (Basel, Switzerland) · 2023Review
- Deep Learning Image Reconstruction for CT: Technical Principles and Clinical Prospects.Radiology · 2023Review
- The value of using a deep learning image reconstruction algorithm of thinner slice thickness to balance the image noise and spatial resolution in low-dose abdominal CT.Quantitative imaging in medicine and surgery · 2023Article
- Radiation dose reduction using deep learning-based image reconstruction for a low-dose chest computed tomography protocol: a phantom study.Quantitative imaging in medicine and surgery · 2023Article
- The Value of Deep Learning Image Reconstruction in Improving the Quality of Low-Dose Chest CT Images.Diagnostics (Basel, Switzerland) · 2022Article
- Artificial Intelligence for Radiation Dose Optimization in Pediatric Radiology: A Systematic Review.Children (Basel, Switzerland) · 2022Review
- Deep learning image reconstruction in pediatric abdominal and chest computed tomography: a comparison of image quality and radiation dose.Quantitative imaging in medicine and surgery · 2022Article
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Authors and funding
7 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundChest 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.
methodsA 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.
resultsThe 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.
conclusionsIt 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.
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