ArticleBMC medical imaging2024
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.
Article in BMC medical imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Enhancing mesenteric vascular imaging with dual-energy CTA: a comparison of DLIR and ASIR-V using low contrast agent and low radiation dose protocols.BMC medical imaging · 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
- 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
- Ultra-High-Resolution Photon-Counting Detector CT Benefits Visualization of Abdominal Arteries: A Comparison to Standard-Reconstruction.Journal of imaging informatics in medicine · 2025Article
- Photon-counting detector computed tomography: iodine density versus virtual monoenergetic imaging of pancreatic ductal adenocarcinoma.Abdominal radiology (New York) · 2025Article
- Research Progress of Dual-energy CT in Diagnosis and Evaluation of Curative Effect of Liver Cancer: A ReviewCurrent medical imaging · 2025Review
- Segmentation for mammography classification utilizing deep convolutional neural network.BMC medical imaging · 2024Article
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Authors and funding
13 authors.
Funding
Abstract
backgroundTo assess the improvement of image quality and diagnostic acceptance of thinner slice iodine maps enabled by deep learning image reconstruction (DLIR) in abdominal dual-energy CT (DECT).
methodsThis study prospectively included 104 participants with 136 lesions. Four series of iodine maps were generated based on portal-venous scans of contrast-enhanced abdominal DECT: 5-mm and 1.25-mm using adaptive statistical iterative reconstruction-V (Asir-V) with 50% blending (AV-50), and 1.25-mm using DLIR with medium (DLIR-M), and high strength (DLIR-H). The iodine concentrations (IC) and their standard deviations of nine anatomical sites were measured, and the corresponding coefficient of variations (CV) were calculated. Noise-power-spectrum (NPS) and edge-rise-slope (ERS) were measured. Five radiologists rated image quality in terms of image noise, contrast, sharpness, texture, and small structure visibility, and evaluated overall diagnostic acceptability of images and lesion conspicuity.
resultsThe four reconstructions maintained the IC values unchanged in nine anatomical sites (all p > 0.999). Compared to 1.25-mm AV-50, 1.25-mm DLIR-M and DLIR-H significantly reduced CV values (all p < 0.001) and presented lower noise and noise peak (both p < 0.001). Compared to 5-mm AV-50, 1.25-mm images had higher ERS (all p < 0.001). The difference of the peak and average spatial frequency among the four reconstructions was relatively small but statistically significant (both p < 0.001). The 1.25-mm DLIR-M images were rated higher than the 5-mm and 1.25-mm AV-50 images for diagnostic acceptability and lesion conspicuity (all P < 0.001).
conclusionsDLIR may facilitate the thinner slice thickness iodine maps in abdominal DECT for improvement of image quality, diagnostic acceptability, and lesion conspicuity.
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