Automatic Hepatic Steatosis Quantification using Low-Dose CT with deep learning-based noise reduction and CT Fat Fraction Analysis Software

Sung Joon Youn; Sun Kyung Jeon; Dong Ho Lee; Chulkyun Ahn; Jeong Min Lee

doi:10.1093/bjr/tqag054

March 5, 2026British Journal of Radiology

Automatic Hepatic Steatosis Quantification using Low-Dose CT with deep learning-based noise reduction and CT Fat Fraction Analysis Software

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Key Points

To evaluate the accuracy of deep learning-based methods in quantifying hepatic steatosis using low-dose CT scans.
Utilized low-dose CT for imaging and deep learning algorithms for noise reduction.
Employed CT Fat Fraction Analysis Software to assess steatosis levels.
DLDR significantly enhances CT Fat Fraction accuracy at lower radiation doses.
High diagnostic performance for hepatic steatosis is maintained with reduced radiation exposure.

Implication

Deep learning improves diagnostic performance for hepatic steatosis while reducing radiation exposure.

Abstract

DLDR significantly enhances the accuracy of CDFF accuracy at lower radiation doses, enabling high diagnostic performance for hepatic steatosis while potentially reducing patient radiation exposure.

Discussion

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Cite This Study

Youn et al. (2026) studied this question.

synapsesocial.com/papers/69acc5b032b0ef16a4050394 https://doi.org/10.1093/bjr/tqag054

View at Publisher

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Bookmark

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Automatic Hepatic Steatosis Quantification using Low-Dose CT with deep learning-based noise reduction and CT Fat Fraction Analysis Software

Key Points

Implication

Abstract

Discussion

Cite This Study

Automatic Hepatic Steatosis Quantification using Low-Dose CT with deep learning-based noise reduction and CT Fat Fraction Analysis Software

Key Points

Implication

Abstract

Discussion

Cite This Study

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