Ultrasound Confidence Maps of Intensity and Structure Based on Directed Acyclic Graphs and Artifact Models

TL;DR

This paper introduces a novel method for assessing pixel confidence in ultrasound images using directed acyclic graphs based on physical acoustic properties, improving artifact detection and image analysis reliability.

Contribution

The paper presents a new confidence estimation algorithm leveraging directed acyclic graphs and artifact models, advancing ultrasound image analysis accuracy.

Findings

Effective shadow detection demonstrated

Improved image compounding results

Superior performance over previous confidence algorithms

Abstract

Ultrasound imaging has been improving, but continues to suffer from inherent artifacts that are challenging to model, such as attenuation, shadowing, diffraction, speckle, etc. These artifacts can potentially confuse image analysis algorithms unless an attempt is made to assess the certainty of individual pixel values. Our novel confidence algorithms analyze pixel values using a directed acyclic graph based on acoustic physical properties of ultrasound imaging. We demonstrate unique capabilities of our approach and compare it against previous confidence-measurement algorithms for shadow-detection and image-compounding tasks.