Tissue-Mimicking Phantom for Liver Fat Quantification

Metabolic dysfunction-associated fatty liver disease (MAFLD) is now the most common cause of chronic liver disease worldwide, affecting approximately 30% of the U.S. population and one billion people globally. However, MAFLD often goes undiagnosed due to the lack of reliable clinical indicators and effective screening methods. The current gold standard for diagnosing and assessing MAFLD is through invasive liver biopsy, which is impractical for widespread screening or frequent monitoring. MASLD typically manifests as “macrovesicular steatosis,” characterized by a single large fat vacuole (30-50 µm) in each hepatocyte, representing a more benign and indolent form. However, approximately 20% of MASLD cases exhibit hepatocytes with numerous smaller fat vacuoles (1-15 µm), known as “microvesicular steatosis,” which is linked to a higher risk of steatohepatitis and progression to cirrhosis and cancer. Although ultrasound (US) imaging shows promise as a non-invasive method to assess liver fat content, traditional grayscale features are not sensitive enough to detect mild liver fat or small changes over time. Novel quantitative US-liver fat quantification (QUS-LFQ) tools that analyze acoustic attenuation and backscatter are emerging, with several techniques now available on the market. However, the sensitivity of current QUS-LFQ methods to variations in fat vacuole size remains unknown. While imaging phantoms can help validate these quantitative tools, there is currently no phantom model available that can link US measurements to the severity of steatosis. Existing techniques for modifying material properties such as attenuation, backscatter, and speed of sound do not account for the macro-structure of steatosis, where the size and distribution of scatterers (such as lipid vacuoles) likely influence acoustic wave interactions and thus affect quantitative US liver fat quantification (QUS-LFQ). We developed agar-based phantoms to simulate the excessive fat accumulation and presence of lipid droplets seen in steatotic hepatocytes, using stable micron-sized peanut oil droplets as stand-ins for these lipid vacuoles. Oil microparticles in the 5-20 µm and 20-60 µm size ranges were successfully produced and their stability confirmed. Microscopy and US imaging revealed a uniform distribution of phantom constituents, including oil microparticles, throughout the agar. US-LFQ measurements were accurate, with fat content closely matching theoretical values and those obtained by MRI. Spectral CT measurements demonstrated the expected linear decrease in X-ray attenuation as the lipid content in the phantoms increased. Herein we describe our methods for microparticle formulation and validation.