ArticleRadiology advances2025
Liver fat quantification using deep silicon photon-counting CT: an
Article in Radiology advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Optimizing energy settings in CdTe, CZT, and Si photon-counting CT for material separation and detection.Physics in medicine and biology · 2026Article
- Development of a customizable model for spectral photon-counting detector CT.Medical physics · 2025Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Accurate liver fat quantification is essential for early diagnosis and effective management of fatty liver disease. Purpose: To investigate the potential clinical utility of a deep silicon-based photon-counting CT (dSi-PCCT), currently in development, for liver fat quantification using human models in an Materials and methods: dSi-PCCT is a cutting-edge photon-counting CT (GE HealthCare), with several investigational systems installed globally, used under IRB approval for imaging animals and human volunteers to support FDA clearance. We developed a dSi-PCCT simulator and benchmarked its imaging performance with respect to a prototype. We imaged a computational Gammex phantom with fat fractions (FF) ranging from 0% to 100%, along with five XCAT human models with liver FF ranging from 1% to 50%, using an abdominal CT protocol. The resulting spectral sinograms were processed using a material decomposition (MD) technique. We calculated HU-based Proton Density Fat Fraction (PDFF) from single-energy images in XCAT models and compared it against the MD-derived FF. The MD-derived FF of both datasets was assessed against the digitally defined ground truth values. Results: We observed a strong correlation ( Conclusion: The results indicate that dSi-PCCT could enable accurate liver fat quantification across a wide range of FFs in multiple objects. These findings suggest that the potential utility of dSi-PCCT for accurate liver fat assessment should be explored
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