ArticleInsights into imaging2022
Deep learning image reconstruction algorithm for carotid dual-energy computed tomography angiography: evaluation of image quality and diagnostic performance.
Article in Insights into imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Literature Review of 3D Deep Learning Techniques in Computed Tomography Reconstruction.Tomography (Ann Arbor, Mich.) · 2023Pooled it
- Convolutional neural network analysis of cervical CT images: classification of Kikuchi disease, lymphoma, lymphadenitis, and tuberculosis.Biomedical engineering online · 2026Article
- Optimizing CT pulmonary angiography with patient-adaptive triggering-a novel approach for a "one-stop-shop" evaluation of pulmonary and aortic vasculature.European radiology · 2026Article
- Comparison of deep learning reconstruction and iterative reconstruction algorithms for virtual monoenergetic image quality in overweight and obese patients with triple-low scan protocol dual-energy carotid computed tomography angiography.Quantitative imaging in medicine and surgery · 2026Article
- Arterial and venous phase evaluation in fast kV-switching dual-energy CT for detecting acute small bowel ischemia caused by small bowel obstruction.Japanese journal of radiology · 2026Article
- Two Deep Image Reconstructions for a 320-Row CT: Review of Clinical ApplicationsCurrent medical imaging · 2026Review
- Comparison of DLIR and ASIR-V algorithms for virtual monoenergetic imaging in carotid CTA under a triple-low protocol.Japanese journal of radiology · 2026Article
- Optimization of image quality and diagnostic accuracy for vascular stenosis in computed tomography perfusion-reconstructed cerebral computed tomography angiography using deep learning reconstruction.Journal of clinical imaging science · 2026Article
- Comparison of image quality in 40 keV virtual monoenergetic images of dual-energy CT pulmonary angiography using deep learning and iterative reconstruction algorithms under optimized low dose scanning protocols.Quantitative imaging in medicine and surgery · 2025Article
- Impact of deep learning based reconstruction algorithms on CT radiomic features of carotid plaques.Journal of applied clinical medical physics · 2025Article
- Comparing two deep learning spectral reconstruction levels for abdominal evaluation using a rapid-kVp-switching dual-energy CT scanner.Abdominal radiology (New York) · 2025Article
- Improved CTA imaging for stroke evaluation - deep learning and iterative reconstruction comparative study.Neuroradiology · 2025Article
- Image quality improvement in head and neck angiography based on dual-energy CT and deep learning.BMC medical imaging · 2025Article
- Comparison of image quality in carotid dual-energy computed tomography angiography at 55 keV virtual monoenergetic imaging using deep learning and adaptive iterative reconstruction algorithm.Journal of clinical imaging science · 2025Article
- Article
- Systematic Review on Learning-based Spectral CT.ArXiv · 2024Article
- Deep learning image reconstruction generates thinner slice iodine maps with improved image quality to increase diagnostic acceptance and lesion conspicuity: a prospective study on abdominal dual-energy CT.BMC medical imaging · 2024Article
- CT image denoising methods for image quality improvement and radiation dose reduction.Journal of applied clinical medical physics · 2024Review
- Application of deep learning image reconstruction-high algorithm in one-stop coronary and carotid-cerebrovascular CT angiography with low radiation and contrast doses.Quantitative imaging in medicine and surgery · 2024Article
- Systematic Review on Learning-based Spectral CT.IEEE transactions on radiation and plasma medical sciences · 2024Article
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Authors and funding
6 authors.
Funding
Abstract
objectivesTo evaluate image quality and diagnostic performance of carotid dual-energy computed tomography angiography (DECTA) using deep learning image reconstruction (DLIR) compared with images using adaptive statistical iterative reconstruction-Veo (ASIR-V).
methodsCarotid DECTA datasets of 28 consecutive patients were reconstructed at 50 keV using DLIR at low, medium, and high levels (DLIR-L, DLIR-M, and DLIR-H) and 80% ASIR-V algorithms. Mean attenuation, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) at different levels of arteries were measured and calculated. Image quality for noise and texture, depiction of arteries, and diagnostic performance toward carotid plaques were assessed subjectively by two radiologists. Quantitative and qualitative parameters were compared between the ASIR-V, DLIR-L, DLIR-M, and DLIR-H groups.
resultsThe image noise at aorta and common carotid artery, SNR, and CNR at all level arteries of DLIR-H images were significantly higher than those of ASIR-V images (p = 0.000-0.040). The quantitative analysis of DLIR-L and DLIR-M showed comparable denoise capability with ASIR-V. The overall image quality (p = 0.000) and image noise (p = 0.000-0.014) were significantly better in the DLIR-M and DLIR-H images. The image texture was improved by DLR at all level compared to ASIR-V images (p = 0.000-0.008). Depictions of head and neck arteries and diagnostic performance were comparable between four groups (p > 0.05).
conclusionsCompared with 80% ASIR-V, we recommend DLIR-H for clinical carotid DECTA reconstruction, which can significantly improve the image quality of carotid DECTA at 50 keV but maintain a desirable diagnostic performance and arterial depiction.
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