Real-time monitoring of focused ultrasound therapy using intelligence-based thermography: A feasibility study

Moslem Sadeghi-Goughari; Sang-Wook Han; Hyock-Ju Kwon

doi:10.1016/j.ultras.2023.107100

Real-time monitoring of focused ultrasound therapy using intelligence-based thermography: A feasibility study

Ultrasonics. 2023 Sep:134:107100. doi: 10.1016/j.ultras.2023.107100. Epub 2023 Jul 1.

Authors

Moslem Sadeghi-Goughari¹, Sang-Wook Han², Hyock-Ju Kwon³

Affiliations

¹ Department of Mechanical and Mechatronics Engineering, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1, Canada. Electronic address: m8sadegh@uwaterloo.ca.
² Department of Automotive Engineering, Shinhan University, 95 Hoam-ro, Uijeongbu, Gyeonggi-do 480-701, Republic of Korea.
³ Department of Mechanical and Mechatronics Engineering, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1, Canada.

PMID: 37421699
DOI: 10.1016/j.ultras.2023.107100

Abstract

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.

Keywords: Artificial Intelligence (AI); Artificial neural network (ANN); Breast cancer; Focused ultrasound (FUS); Thermography.

MeSH terms

Artificial Intelligence
Feasibility Studies
Intelligence
Thermography* / methods
Ultrasonic Therapy* / methods