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Related Experiment Video

Updated: Jan 12, 2026

Two-Dimensional Super-Resolution Visualization of Rat Brain Microvasculature Using Ultrasound Localization Microscopy

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Ultrasound super-resolved hemodynamic estimation in microvessel using physics-informed neural networks and data

Meiling Liang¹, Jiacheng Liu¹, Hao Wang¹

¹The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Department of Biomedical Engineering, School of life Science and Technology, Xi'an Jiaotong University, Xi'an, China.

Computer Methods and Programs in Biomedicine

|November 7, 2025

Summary

This study introduces a novel method using physics-informed neural networks (PINN) and data assimilation for enhanced ultrasound super-resolution imaging (SRI). The technique reconstructs microvascular velocity and pressure fields, improving diagnostic potential for vascular diseases.

Keywords:

Data matching and fusion Hemodynamic parameter Physics-Informed neural networks Super-resolution imaging

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Imaging and Quantification of the Hepatic Vasculature of Mice Using Ultrafast Doppler Ultrasound

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Published on: July 19, 2024

Area of Science:

Biomedical Engineering
Medical Imaging
Computational Fluid Dynamics

Background:

Ultrasound super-resolution imaging (SRI) visualizes microvascular structure and velocity.
Challenges exist in enhancing spatial resolution of instantaneous velocity and capturing pressure fields simultaneously.

Purpose of the Study:

To develop a method combining physics-informed neural networks (PINN) with data assimilation for microvascular 2D super-resolution velocity and pressure reconstruction in SRI.
To enhance spatial information from sparse SRI velocity data and infer reliable pressure fields.

Main Methods:

Decomposition of long-time SRI velocity data into short-time subsets to enhance spatial information.
Data assimilation to fuse SRI-derived structure and flow information with hemodynamic simulations.
Optimization of PINN encoded with 2D Navier-Stokes equations for super-resolution velocity and pressure reconstruction.

Main Results:

In vitro experiments validated the method, increasing spatial vectors by 6.48 times.
Accurate super-resolution hemodynamic parameter reconstructions in rat brain and liver tumor microvessels.
Achieved high resolutions (e.g., 0.46 μm radial, 5.9 μm axial for rat brain vessels) with low relative errors (1.85% and 4.89%).

Conclusions:

The method successfully reconstructs super-resolution microvascular velocity and pressure from sparse 2D SRI data.
Demonstrates significant potential for aiding clinical diagnosis of microvascular diseases.
ClinicalTrials.gov identifier: NCT06018142.