ArticleMedical physics2025
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.
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 3 papers.
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3 citing papers in PubMed.
- Optimization of spectral photon-counting CT reconstruction parameters for improved detection of peritoneal metastases compared with energy-integrating detector dual-energy CT: a phantom study.European radiology experimental · 2026Article
- Two Deep Image Reconstructions for a 320-Row CT: Review of Clinical ApplicationsCurrent medical imaging · 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
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7 authors.
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Abstract
backgroundRecently, CT manufacturers have developed deep-learning-based reconstruction (DLR) algorithms for conventional and spectral CT images. On dual-energy CT platforms, DLR algorithms have been developed in particular to reduce image noise in virtual monoenergetic images (VMIs), and to improve the accuracy of iodine concentrations on iodine maps. PURPOSE: To assess the impact of a prototype, dedicated Spectral AI Reconstruction (SAIR) algorithm (Spectral Precise Image; Philips Healthcare) compared with a hybrid iterative reconstruction (HIR) algorithm ("Spectral", Philips Healthcare) on the quality of low-keV VMIs and iodine maps in abdomen-pelvis CT conditions.
methodsA spectral phantom was scanned on the dual-layer spectral CT system at 120 kVp and a CTDI
resultsFor all keV, noise magnitude values were higher than with HIR using the Sharp (11.9 ± 5.6%; p > 0.05) and Sharper (39.3 ± 7.3%; p = 0.004) levels and lower than HIR for the other SAIR levels, decreasing from Standard (-15.3 ± 3.9%; p > 0.05) to Smooth (-42.3 ± 2.2%; p = 0.004) and Smoother levels (-70.5 ± 1.1%; p < 0.001). For all energy levels, f
conclusionCompared to the HIR algorithm used in clinical practice for low-keV VMIs, Standard, Smooth and Smoother levels of SAIR reduced image noise and improved the detectability of contrast-enhanced lesions. Better noise texture was found only with the Standard and Smooth levels compared to HIR.
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