ArticleEuropean radiology experimental2023
Comparison of image quality of two versions of deep-learning image reconstruction algorithm on a rapid kV-switching CT: a phantom study.
Article in European radiology experimental, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 27 citations in OpenAlex.
- Phantom-based performance comparison of two commercial deep learning CT reconstruction algorithms with super- and normal-resolution settings.European radiology experimental · 2026Article
- Comparing two deep learning spectral reconstruction levels for abdominal evaluation using a rapid-kVp-switching dual-energy CT scanner.Abdominal radiology (New York) · 2025Article
- Automated Task-Transfer Function Measurement for CT Image Quality Assessment Based on AAPM TG 233.Journal of imaging · 2025Article
- Performance Evaluation of Image Segmentation Using Dual-Energy Spectral CT Images with Deep Learning Image Reconstruction: A Phantom Study.Tomography (Ann Arbor, Mich.) · 2025Article
- Photon-counting versus energy-integrating CT of abdomen-pelvis: a phantom study on the potential for reducing iodine contrast media.European radiology experimental · 2025Article
- Multivariate signal-to-noise ratio as a metric for characterizing spectral computed tomography.Physics in medicine and biology · 2024Article
- Enhancing image quality in computed tomography angiography follow-ups after endovascular aneurysm repair: a comparative study of reconstruction techniques.BMC medical imaging · 2024Article
- What to Expect (and What Not) from Dual-Energy CT Imaging Now and in the Future?Journal of imaging · 2024Review
- Determining the Optimal Energy Level of Virtual Monoenergetic Images in Dual-Source CT for Diagnosis of Bowel Obstruction and Colitis.Diagnostics (Basel, Switzerland) · 2023Article
- Ultra-High-Resolution CT of the Head and Neck with Deep Learning Reconstruction-Assessment of Image Quality and Radiation Exposure and Intraindividual Comparison with Normal-Resolution CT.Diagnostics (Basel, Switzerland) · 2023Article
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo assess the impact of the new version of a deep learning (DL) spectral reconstruction on image quality of virtual monoenergetic images (VMIs) for contrast-enhanced abdominal computed tomography in the rapid kV-switching platform.
methodsTwo phantoms were scanned with a rapid kV-switching CT using abdomen-pelvic CT examination parameters at dose of 12.6 mGy. Images were reconstructed using two versions of DL spectral reconstruction algorithms (DLSR V1 and V2) for three reconstruction levels. The noise power spectrum (NSP) and task-based transfer function at 50% (TTF
resultsThe noise magnitude was significantly lower with DLSR V2 compared to DLSR V1 for energy levels between 40 and 60 keV by -36.5% ± 1.4% (mean ± standard deviation) for the standard level. The average NPS frequencies increased significantly with DLSR V2 by 23.7% ± 4.2% for the standard level. The highest difference in TTF
conclusionsThe DLSR V2 improves image quality and detectability of low iodine concentrations in VMIs compared to DLSR V1. This suggests a great potential of DLSR V2 to reduce iodined contrast doses.
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