Imaging Techniques for Hepatic Steatosis Assessment

Hepatic steatosis, the abnormal accumulation of triglycerides within hepatocytes, represents the earliest and most prevalent manifestation of nonalcoholic fatty liver disease (NAFLD). Accurate noninvasive assessment of liver fat content is essential for early diagnosis, risk stratification and monitoring of therapeutic interventions. Conventional ultrasound remains widely used for initial screening owing to its low cost and broad availability, yet its semiquantitative nature and operator dependence limit reproducibility, particularly in patients with mild steatosis or high body mass index. Computed tomography (CT) offers objective attenuation measurements on non-contrast scans, while dual-energy CT (DECT) enables material decomposition and generation of virtual non-contrast images to enhance sensitivity and specificity. Magnetic resonance imaging (MRI) methods—including chemical shift imaging, proton density fat fraction (PDFF) mapping and proton magnetic resonance spectroscopy—provide highly accurate, quantitative biomarkers of liver fat without ionising radiation. Transient and shear-wave elastography additionally permit simultaneous evaluation of fibrosis and steatosis via controlled attenuation parameters. Recent advances in deep learning have facilitated automated segmentation and opportunistic screening on routine imaging exams. Together, these modalities underpin a multi-modality paradigm that balances precision, accessibility and safety, thereby addressing the global burden of NAFLD through earlier detection and more effective management.

Research from Nature Portfolio

Recent studies have consolidated the diagnostic value of MRI-based techniques for differentiating simple steatosis from steatohepatitis. A comprehensive analysis of proton magnetic resonance methods demonstrates pooled sensitivity approaching 90% and specificity above 70% for identification of non-alcoholic steatohepatitis, underlining the clinical utility of MRI biomarkers in replacing or complementing liver biopsy. Advances in chemical shift-encoded sequences have further standardised PDFF quantification across scanners, yielding robust, reproducible measures of hepatic fat fraction. Together, these contributions establish MRI-derived metrics as a reference standard for noninvasive fat quantification and guide development of novel imaging protocols in both research and clinical practice.

Imaging Techniques for Hepatic Steatosis Assessment publication trend

The graph below shows the total number of articles in imaging techniques for hepatic steatosis assessment across all publications each year (not limited to Nature Index journals).

Technical terms

Hounsfield unit (HU): A quantitative CT scale for tissue radiodensity, calibrated to water (0 HU) and air (–1000 HU).
Dual-energy CT (DECT): A technique using two distinct X-ray energy spectra to differentiate materials based on their energy-dependent attenuation.
Virtual non-contrast (VNC) image: A synthetic CT dataset approximating a non-contrast acquisition, reconstructed from contrast-enhanced DECT data.
Proton density fat fraction (PDFF): The ratio of mobile protons attributable to fat relative to total mobile protons within tissue, measured by chemical shift MRI.
Transient elastography: An ultrasound-based approach that measures liver stiffness and controlled attenuation parameter values to infer fibrosis and steatosis.
Meta-analysis: A statistical methodology that aggregates data from multiple independent studies to derive combined effect sizes and diagnostic performance metrics.

References

1. Opportunistic assessment of steatotic liver disease in lung cancer screening eligible individuals. Journal of Internal Medicine (2025).
2. Automated hepatic steatosis assessment on dual-energy CT-derived virtual non-contrast images through fully-automated 3D organ segmentation. La radiologia medica (2024).
3. Detection of fatty liver using virtual non-contrast dual-energy CT. Abdominal Radiology (2022).
4. Accuracy of proton magnetic resonance for diagnosing non-alcoholic steatohepatitis: a meta-analysis. Scientific Reports (2019).