Assessment of ultrasound image quality

The method and system for ultrasound quality assurance using simulated targets in computational analysis address the inefficiencies of current phantom designs, offering flexible and cost-effective quality assurance by calculating LSNR and gCNR, enhancing image quality evaluation and reducing variability.

WO2026128386A1 Publication Date: 2026-06-18SUN NUCLEAR CORP
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Patent Information

Authority / Receiving Office
WO · WO
Patent Type
Applications
Current Assignee / Owner
SUN NUCLEAR CORP
Filing Date
2025-12-08
Publication Date
2026-06-18

AI Technical Summary

⚠Technical Problem

Current ultrasound imaging systems face challenges in quality assurance due to the need for costly and time-consuming phantom designs that are specific to spatial resolution, lacking flexibility, and are operator-dependent, leading to inefficiencies in calibration and assessment.

⚗Method used

A method and system for ultrasound quality assurance using a background phantom with uniform Rayleigh scatterers and a target phantom for in-air scans, enabling the creation of simulated targets through computational analysis to calculate quality assurance parameters like LSNR and gCNR, reducing reliance on physical phantoms and technician skill.

🎯Benefits of technology

Provides flexible, cost-effective, and consistent quality assurance by generating simulated targets for assessing ultrasound system performance, improving image quality evaluation and reducing variability, while maintaining high diagnostic standards.

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Abstract

Methods and systems of ultrasound quality assurance analysis include acquiring a first ultrasound image of a first phantom and acquiring a second ultrasound image of a second phantom. Spatial resolution parameters are determined from the first ultrasound image. Fill-in distances are calculated for simulated targets based on the spatial resolution parameters. Simulated targets are defined based upon a lesion radius and the fill-in distances. A grid of simulated targets is formed across the first ultrasound image. A quality assurance parameter is calculated for each simulated target in the grid using data from both the first ultrasound image and the second ultrasound image.
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