ArticleMedical physics2025
Impact of deep learning reconstructions on image quality and liver lesion detectability in dual-energy CT: An anthropomorphic phantom study.
Article in Medical physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 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
- Impact of automatic exposure control on radiation dose and detectability in dual-source and fast kV switching dual-energy CT.Physical and engineering sciences in medicine · 2026Article
- Feasibility of reducing radiation dose and contrast dose while improving image quality in dual-energy CT pulmonary angiography : Use of low-energy virtual monochromatic images with deep learning image reconstruction algorithm.Radiologie (Heidelberg, Germany) · 2026Article
- Optimization of hypovascular liver lesion detectability in dual-energy CT using deep learning image reconstruction: a phantom study for potential iodine dose reduction.European radiology experimental · 2026Article
- Improving image quality and diagnostic confidence for PRETEXT staging in pediatric hepatoblastoma using thin-slice and low-energy virtual monochromatic images in dual-energy CT with deep learning image reconstruction algorithm.BMC medical imaging · 2026Article
- Artificial intelligence in imaging diagnosis of liver tumors: current status and future prospects.Abdominal radiology (New York) · 2026Review
- Impact of a prototype, dedicated AI-based spectral image reconstruction algorithm on the quality of low-keV virtual monoenergetic images and iodine maps: A phantom study.Medical physics · 2025Article
- Impact of deep learning reconstructions on image quality and liver lesion detectability in dual-energy CT: An anthropomorphic phantom study.Medical physics · 2025Article
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6 authors.
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Abstract
backgroundDeep learning image reconstruction (DLIR) algorithms allow strong noise reduction while preserving noise texture, which may potentially improve hypervascular focal liver lesions. PURPOSE: To assess the impact of DLIR on image quality (IQ) and detectability of simulated hypervascular hepatocellular carcinoma (HCC) in fast kV-switching dual-energy CT (DECT).
methodsAn anthropomorphic phantom of a standard patient morphology (body mass index of 23 kg m
resultsLesion-to-liver contrast significantly increased with decreasing energy level in both AP and PVP (p ≤ 0.042) but was not affected by reconstruction algorithm (p ≥ 0.57). Overall, noise magnitude increased with decreasing energy levels and was the lowest with ASIRV-100 at all energy levels in both AP and PVP (p ≤ 0.01) and significantly lower with DLIR-M and DLIR-H reconstructions compared to ASIRV-50 and DLIR-L (p < 0.001). For all reconstructions, noise texture within the liver tended to get smoother with decreasing energy; f
conclusionsCompared to the routinely used level of iterative reconstruction, DLIR reduces noise without consequential noise texture modification, and may improve the detectability of hypervascular liver lesions while enabling the use of lower energy virtual monoenergetic images. The optimal energy level and DLIR level may depend on the lesion enhancement.
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