Search

A low cost, quasi real-time elastography system, displacement-gradient elastography (DGE), was developed by applying digital image correlation (DIC) method and smoothing algorithm to B-mode ultrasound images. In order to achieve quasi real-time elastogram display, a new fast pattern matching algorithm, decoupled cross-correlation (DCC), was proposed and validated. By applying the DGE to various phantoms, elastograms were generated to identify the lesion with wide variations of stiffness ratio and applied strain. The performance of DGE was qualitatively compared with those from a high-end ultrasound scanner using the elastograms of a commercial elastography breast phantom. DGE was also applied to the ultrasound images of human breast lesions in various BI-RADS categories. This study suggests that DGE may have comparable performance to conventional elastography in detecting breast cancer, while it can be easily implemented onto conventional ultrasound scanners. [ABSTRACT FROM AUTHOR]

• A novel method for monitoring focused ultrasound (FUS) is proposed. • Temperature rise at the focused region can be detected by the breast thermography. • The inverse analysis of thermal images can lead to quantitative measurement of FUS's thermal parameters. • Intelligent thermography can be used as a real-time method for monitoring FUS. Focused ultrasound (FUS) therapy has been widely studied for breast cancer treatment due to its potential as a fully non-invasive method to improve cosmetic and oncologic results. However, real-time imaging and monitoring of the therapeutic ultrasound delivered to the target area remain challenges for precision breast cancer therapy. The main objective of this study is to propose and evaluate a novel intelligence-based thermography (IT) method that can monitor and control FUS treatment using thermal imaging with the fusion of artificial intelligence (AI) and advanced heat transfer modeling. In the proposed method, a thermal camera is integrated into FUS system for thermal imaging of the breast surface, and an AI model is employed for the inverse analysis of the surface thermal monitoring, thereby estimating the features of the focal region. This paper presents experimental and computational studies conducted to assess the feasibility and efficiency of IT-guided FUS (ITgFUS). Tissue phantoms, designed to mimic the properties of breast tissue, were used in the experiments to investigate detectability and the impact of temperature rise at the focal region on the tissue surface. Additionally, an AI computational analysis employing an artificial neural network (ANN) and FUS simulation was carried out to provide a quantitative estimation of the temperature rise at the focal region. This estimation was based on the observed temperature profile on the breast model's surface. The results proved that the effects of temperature rise at the focused area could be detected by the thermal images acquired with thermography. Moreover, it was demonstrated that the AI analysis of the surface temperature measurement could result in near real-time monitoring of FUS by quantitative estimation of the temporal and spatial temperature rise profiles at the focal region. [ABSTRACT FROM AUTHOR]