Artificial Intelligence-Assisted Contrast-Enhanced Ultrasound for Perfusion Evaluation: State-of-the-Art Review.
Authors
Affiliations (4)
Affiliations (4)
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China.
- Department of Medicine, Faculty of Health Sciences, Queen's University, Kingston, Ontario, Canada.
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China; The Cancer Center of the Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, China.
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China. Electronic address: [email protected].
Abstract
Accurate perfusion evaluation is crucial for clinical diagnosis and treatment, such as lesion characterization and risk stratification of carotid atherosclerotic plaques. Contrast-enhanced ultrasound (CEUS) examination, which enhances microvascular visualization with contrast agents, offers the advantages of being non-invasive, real time and free of ionizing radiation, making it widely applicable for perfusion evaluation. However, traditional CEUS analysis may have several limitations, in part due to operator dependence. A single CEUS examination generates massive dynamic cine-loop sequences, so manual frame-by-frame analysis is not only time consuming and labor intensive, but also prone to missing key information. It also struggles to obtain quantitative perfusion parameters and has the problem of inter-observer variability. Artificial intelligence (AI) can efficiently process high-dimensional CEUS data through automated data pre-processing, intelligent segmentation of regions of interest, standardized feature extraction and accurate decision support. AI improves the diagnostic efficiency and consistency of CEUS analysis, thus becoming an ideal tool to address these limitations. In this review, we summarize the mechanism, clinical value and limitations of CEUS examination for perfusion evaluation and introduce the core AI techniques for CEUS analysis and the technical workflow of AI-assisted CEUS. We then elaborate on the status of applying AI-assisted CEUS across multiple organ systems. Finally, we discuss the current challenges and future directions of this technology, aiming to promote its clinical application.