Steatotic liver disease precisely assessed using three-dimensional ultrafast vascular ultrasound

Steatotic liver disease (commonly called fatty liver disease) progresses silently. Even in the absence of noticeable symptoms, changes begin to unfold inside the liver. While hepatic fat accumulation remains a defining feature of the disease, steatotic liver disease is increasingly recognized as a multifactorial condition involving metabolic dysfunction and other interacting pathological processes. The key challenge lies in how early and how accurately these changes can be detected. Recently, researchers at POSTECH (Pohang University of Science and Technology) have successfully visualized the liver’s internal vascular network using ultrasound, opening a new avenue for earlier and more precise assessment of steatotic liver disease.

Steatotic liver disease is the most common chronic liver disease worldwide. It typically begins with fat accumulation and can progress to inflammation, liver fibrosis, cirrhosis, and ultimately hepatocellular carcinoma, underscoring the importance of early detection and longitudinal monitoring. Conventional ultrasound, widely used in clinical practice, offers the advantage of conveniently assessing hepatic fat accumulation. However, its diagnostic performance is limited by operator dependency and reduced accuracy compared with magnetic resonance imaging (MRI).

To overcome these limitations, the POSTECH research team led by Professors Chulhong Kim and Yong-Joo Ahn focused on subtle microvascular changes that emerge during the progression of steatotic liver disease. The team developed an ultrasound-based technology capable of three-dimensionally visualizing the liver’s vascular architecture. Much like observing urban traffic flow from a satellite, this approach enables real-time visualization of vascular alterations, including vessel obstruction and structural distortion.

At the core of this technology is ultrafast Doppler imaging (UFD), which acquires thousands of ultrasound frames per second to precisely capture blood flow even within vessels thinner than a human hair. This was combined with established ultrasound techniques for evaluating hepatic fat accumulation and tissue structure, including attenuation imaging (ATI) and acoustic structure quantification (ASQ). Together, these components form a three-dimensional, multiparametric ultrasound imaging system that simultaneously integrates vascular and tissue information.

Using this system, the researchers longitudinally tracked steatotic liver disease progression over an eight-week period. They successfully visualized three-dimensional changes in both hepatic tissue and microvasculature, demonstrating high reproducibility and robustness. Notably, the system also captured the restoration of vascular and tissue indices during disease recovery, highlighting its potential utility for evaluating therapeutic response and predicting prognosis.

Quantitative analysis revealed a strong correlation between vascular indices and the degree of hepatic steatosis. By integrating multiple ultrasound-derived parameters using machine learning techniques, the researchers derived a comprehensive ultrasound score that classified steatotic liver disease severity with an average accuracy of 92%.

Professor Chulhong Kim stated, “Ultrafast Doppler-based ultrasound imaging extends beyond conventional tissue-centered diagnosis by directly incorporating microvascular changes into clinical assessment, offering substantial diagnostic value.” Professor YongJoo Ahn added, “By enabling early detection and utilization of microvascular alterations, this approach opens new possibilities for precision medicine and holds promise for broader application across various liver diseases.”

This study was conducted by research teams led by Professor Chulhong Kim (Departments of Electrical Engineering, IT Convergence Engineering, Mechanical Engineering, and the Graduate School of Convergence Science and Technology) and Professor Yong-Joo Ahn (Department of IT Convergence Engineering and the Graduate School of Convergence Science and Technology) at POSTECH. The findings were recently published in the international journal Nature Communications. This work was supported by the Ministry of Education, the Ministry of Science and ICT, and the Ministry of Health and Welfare of Korea.