Defining Robust Ultrasound Quality Metrics via an Ultrasound Foundation Model

TL;DR

This paper introduces a new ultrasound quality assessment framework using a foundation model that provides task-linked, cross-organ comparable, PSNR-consistent, and clinically useful metrics, bridging the gap between algorithmic quality and diagnostic utility.

Contribution

It proposes a TinyUSFM-based evaluation framework with two novel metrics, TinyUSFM-uLPIPS and TinyUSFM-NRQ, tailored for ultrasound imaging quality assessment.

Findings

Metrics outperform traditional standards in task relevance and cross-organ comparison.

TinyUSFM-NRQ correlates well with traditional fidelity benchmarks like PSNR.

Enhanced prediction of expert preferences and improved super-resolution reconstructions.

Abstract

Clinicians lack a principled framework to quantify diagnostic utility in ultrasound reconstructions. Existing standards like PSNR and VGG-LPIPS are inadequate, failing to account for modality-specific physics or the structural nuances of acoustic imaging. We close this gap with a TinyUSFM-based evaluation framework featuring two distinct metrics: TinyUSFM-uLPIPS, a full-reference perceptual distance based on multi-layer token relations, and TinyUSFM-NRQ, a deployable no-reference quality score utilizing clean-manifold modeling and worst-region aggregation to detect localized harmful artifacts. We demonstrate that the presented metrics have four unique advantages: 1) Task-linked quality, where TinyUSFM-uLPIPS achieves superior calibration with semantic task damage, accurately reflecting Dice-score drops in segmentation where VGG-based metrics fail; 2) Cross-organ comparability, maintaining stable scoring scales and consistent severity rankings across diverse anatomical sites and domain-shifted data; 3) PSNR-consistent sensitivity, with TinyUSFM-NRQ providing a reliable quality score without ground-truth images that remains consistent with traditional fidelity benchmarks (i.e. PSNR); and 4) Clinical utility, improving the prediction of expert preference from 47.2$\%$ to 72.8$\%$ accuracy and producing super-resolution reconstructions preferred by sonographers. By integrating these advantages into a unified assessment and optimization loop, this work establishes a modality-aligned standard that finally bridges the gap between algorithmic performance and diagnostic utility. Our code is available at https://github.com/sextant-fable/US-Metrics.