US Backscatter for Liver Fat Quantification: An AIUM-RSNA QIBA Pulse-Echo Quantitative Ultrasound Initiative
Abstract
Nonalcoholic fatty liver disease (NAFLD) is believed to affect one-third of American adults. Noninvasive methods that enable detection and monitoring of NAFLD have the potential for great public health benefits. Because of its low cost, portability, and noninvasiveness, US is an attractive alternative to both biopsy and MRI in the assessment of liver steatosis. NAFLD is qualitatively associated with enhanced B-mode US echogenicity, but visual measures of B-mode echogenicity are negatively affected by interobserver variability. Alternatively, quantitative backscatter parameters, including the hepatorenal index and backscatter coefficient, are being investigated with the goal of improving US-based characterization of NAFLD. The American Institute of Ultrasound in Medicine and Radiological Society of North America Quantitative Imaging Biomarkers Alliance are working to standardize US acquisition protocols and data analysis methods to improve the diagnostic performance of the backscatter coefficient in liver fat assessment. This review article explains the science and clinical evidence underlying backscatter for liver fat assessment. Recommendations for data collection are discussed, with the aim of minimizing potential confounding effects associated with technical and biologic variables.
Read the paper
Related studies
- Characterization and Analysis of Porosity and Pore Structures
- Practical considerations for conducting ecotoxicity test methods with manufactured nanomaterials: what have we learnt so far?
- Dental Optical Coherence Tomography
- Radiation-shielding concrete: A review of materials, performance, and the impact of radiation on concrete properties
- Testing entanglement of annihilation photons
- Calanus finmarchicus seasonal cycle and diapause in relation to gene expression, physiology, and endogenous clocks
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.