ArticleEuropean radiology2023
Improving lesion conspicuity in abdominal dual-energy CT with deep learning image reconstruction: a prospective study with five readers.
Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Evaluation of gastric cancer using artificial intelligence iterative reconstruction on abdominal CT: image quality and diagnostic accuracy.Abdominal radiology (New York) · 2026Article
- Deep learning image reconstruction improves visualization of arterial phase hyperenhancement and washout appearance on dual-energy CT for hepatocellular carcinoma: a non-inferiority study.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
- Photon-Counting Detector CT Allows Abdominal Virtual Monoenergetic Imaging at Lower Kiloelectron Volt Level with Lower Noise Using Lower Radiation Dose: A Prospective Matched Study Compared to Energy-Integrating Detector CT.Journal of imaging informatics in medicine · 2026Article
- Two Deep Image Reconstructions for a 320-Row CT: Review of Clinical ApplicationsCurrent medical imaging · 2026Review
- 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
- Artificial intelligence (AI) and CT in abdominal imaging: image reconstruction and beyond.Abdominal radiology (New York) · 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
- Ultra-High-Resolution Photon-Counting Detector CT Benefits Visualization of Abdominal Arteries: A Comparison to Standard-Reconstruction.Journal of imaging informatics in medicine · 2025Article
- Impact of deep learning reconstructions on image quality and liver lesion detectability in dual-energy CT: An anthropomorphic phantom study.Medical physics · 2025Article
- A paradigm shift in oncology imaging: a prospective cross-sectional study to assess low-dose deep learning image reconstruction versus standard-dose iterative reconstruction for comprehensive lesion detection in dual-energy computed tomography.Quantitative imaging in medicine and surgery · 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
- Pancreatic cancer detection with dual-energy CT: diagnostic performance of 40 keV and 70 keV virtual monoenergetic images.La Radiologia medica · 2024Article
- Just give the contrast? Appraisal of guidelines on intravenous iodinated contrast media use in patients with kidney disease.Insights into imaging · 2024Article
- Impacts of Adaptive Statistical Iterative Reconstruction-V and Deep Learning Image Reconstruction Algorithms on Robustness of CT Radiomics Features: Opportunity for Minimizing Radiomics Variability Among Scans of Different Dose Levels.Journal of imaging informatics in medicine · 2024Article
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14 authors.
Funding
Abstract
objectivesTo evaluate image quality, diagnostic acceptability, and lesion conspicuity in abdominal dual-energy CT (DECT) using deep learning image reconstruction (DLIR) compared to those using adaptive statistical iterative reconstruction-V (Asir-V) at 50% blending (AV-50), and to identify potential factors impacting lesion conspicuity.
methodsThe portal-venous phase scans in abdominal DECT of 47 participants with 84 lesions were prospectively included. The raw data were reconstructed to virtual monoenergetic image (VMI) at 50 keV using filtered back-projection (FBP), AV-50, and DLIR at low (DLIR-L), medium (DLIR-M), and high strength (DLIR-H). A noise power spectrum (NPS) was generated. CT number and standard deviation values of eight anatomical sites were measured. Signal-to-noise (SNR), and contrast-to-noise ratio (CNR) values were calculated. Five radiologists assessed image quality in terms of image contrast, image noise, image sharpness, artificial sensation, and diagnostic acceptability, and evaluated the lesion conspicuity.
resultsDLIR further reduced image noise (p < 0.001) compared to AV-50 while better preserved the average NPS frequency (p < 0.001). DLIR maintained CT number values (p > 0.99) and improved SNR and CNR values compared to AV-50 (p < 0.001). DLIR-H and DLIR-M showed higher ratings in all image quality analyses than AV-50 (p < 0.001). DLIR-H provided significantly better lesion conspicuity than AV-50 and DLIR-M regardless of lesion size, relative CT attenuation to surrounding tissue, or clinical purpose (p < 0.05).
conclusionsDLIR-H could be safely recommended for routine low-keV VMI reconstruction in daily contrast-enhanced abdominal DECT to improve image quality, diagnostic acceptability, and lesion conspicuity. KEY POINTS: • DLIR is superior to AV-50 in noise reduction, with less shifts of the average spatial frequency of NPS towards low frequency, and larger improvements of NPS noise, noise peak, SNR, and CNR values. • DLIR-M and DLIR-H generate better image quality in terms of image contrast, noise, sharpness, artificial sensation, and diagnostic acceptability than AV-50, while DLIR-H provides better lesion conspicuity than AV-50 and DLIR-M. • DLIR-H could be safely recommended as a new standard for routine low-keV VMI reconstruction in contrast-enhanced abdominal DECT to provide better lesion conspicuity and better image quality than the standard AV-50.
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