Artificial Intelligence in Quantitative Ultrasound Imaging: A Review

TL;DR

This review paper discusses the integration of artificial intelligence techniques into quantitative ultrasound imaging to enhance image quality, measurement accuracy, and reproducibility, highlighting recent research, challenges, and future prospects.

Contribution

It provides the first comprehensive survey of AI applications in QUS, outlining current methods, challenges, and future directions in this emerging field.

Findings

AI improves imaging quality and measurement reproducibility in QUS.

Recent research shows AI can automate and enhance QUS analysis.

Challenges include variability and limited datasets for AI training.

Abstract

Quantitative ultrasound (QUS) imaging is a reliable, fast and inexpensive technique to extract physically descriptive parameters for assessing pathologies. Despite its safety and efficacy, QUS suffers from several major drawbacks: poor imaging quality, inter- and intra-observer variability which hampers the reproducibility of measurements. Therefore, it is in great need to develop automatic method to improve the imaging quality and aid in measurements in QUS. In recent years, there has been an increasing interest in artificial intelligence (AI) applications in ultrasound imaging. However, no research has been found that surveyed the AI use in QUS. The purpose of this paper is to review recent research into the AI applications in QUS. This review first introduces the AI workflow, and then discusses the various AI applications in QUS. Finally, challenges and future potential AI applications in QUS are discussed.