Steatotic liver disease progresses silently, with pathological changes developing. Although ultrasound imaging is widely used, its diagnostic performance remains limited by inconsistent results and suboptimal accuracy.

To overcome these challenges, a research team led by Professor Chulhong KIM (EE, CiTE, ME, GSCST) and Prof. Yong Joo AHN (CiTE, GSCST) focused on subtle microvascular changes during the disease progression. By 3D-visualizing the liver’s internal vascular network, the team developed an ultrasound-based system to monitor this progression. The research was published in Nature Communications.

The core of this system is ultrafast Doppler imaging (UFD). UFD captures thousands of frames per second to capture blood flow in microvessels. By combining this with conventional ultrasound techniques, the team developed a 3D multiparametric ultrasound imaging system that simultaneously analyzes vascular and tissue information.

Using this system, the team tracked the disease over eight weeks, successfully visualizing 3D changes in both hepatic microvasculature and tissue. The system demonstrated high reproducibility and robustness, and captured the restoration of indices during recovery. Additionally, the team used machine learning to integrate various ultrasound markers into a single score, classifying the disease grades with an average accuracy of 92%.

Prof. Kim explained, “Ultrafast ultrasound blood flow imaging has significant clinical value because it directly reflects microvascular changes, moving beyond conventional diagnostics based on tissues.” Prof. Ahn added, “By detecting and utilizing early microvascular changes, we have opened up new possibilities for precision medicine in other liver diseases.”