ArticleBMC medical imaging2025
Image quality improvement in head and neck angiography based on dual-energy CT and deep learning.
Article in BMC medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Deep learning reconstruction for 40-keV virtual monoenergetic CT of colon cancer: evaluation of image quality and edge sharpness.Abdominal radiology (New York) · 2026Article
- Improved visualization of pancreas and tumor boundaries using high-frequency kernels with deep-learning image reconstruction at high-strength level.Abdominal radiology (New York) · 2026Article
- Convolutional neural network analysis of cervical CT images: classification of Kikuchi disease, lymphoma, lymphadenitis, and tuberculosis.Biomedical engineering online · 2026Article
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12 authors.
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Abstract
objectiveCompare the image quality of image reconstructed using deep learning-based image reconstruction (DLIR) and iterative reconstruction algorithms for head and neck dual-energy CT angiography (DECTA).
methodsThis prospective study comprised fifty-eight patients with head and neck DECTA. Images reconstructed by four algorithms (120-kVp-like with ASIR-V40%, 50 keV with ASIR-V40%, 50 keV with DLIR-M, 50 keV with DLIR-H) were compared. CT attenuation, image noise, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were all calculated. Edge rise distance (ERD) and edge-rise slope (ERS) were measured on the right common carotid artery to reflect spatial resolution. Quantitative data are summarized as the mean ± SD. The subjective image quality scores using a 5-point Likert scale were obtained for the following: overall image quality, edge sharpness of vessels, image noise, and artifacts.
resultsThe CT attenuation of all vessels in the 120kVp-like images were lower than the 3 sets of 50 keV images with significant difference (all P < 0.05). In the 50 keV images, both sternocleidomastoid muscle (SCM) and white matter (WM) had a minimum noise in DLIR-H group, and a maximum in ASIR-V40% group with significant difference (all P < 0.001). SNR and CNR in 50 keV images of all vessels had the same results: highest in DLIR-H group and lowest in ASIR-V40% group with significant differences (all P < 0.05). The mean value of ERD showed no significant difference among the four groups (P = 0.082). While the 120kVp-like images had the lowest ERS, which showed statistically significant difference with the other groups (all P < 0.001). In terms of overall image quality, sharpness, and artifacts, the scores of DLIR-M and DLIR-H at 50 keV were not statistically different (all P > 0.05), and were higher than ASIR-V40% at 50 keV images (all P < 0.05), and higher than ASIR-V40% at 120 kVp-like (all P < 0.05). The scores of DLIR-H at 50 keV were highest in terms of noise and average scores.
conclusionDLIR is a potential solution for DECTA reconstruction since it can greatly reduce image noise, improving image quality of head and neck DECTA at 50 keV It is worth considering adopting in routine head and neck CTA applications.
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