Author: Hisham Assi, Lexi Dowdy, Muhannad N. Fadhel, Michael C. Kolios, Carl Kumaradas, Rasha S Makkia, Tanay N Mannikar 👨🔬
Affiliation: Toronto Metropolitan University, Augusta University, Institute for Biomedical Engineering, Science and Technology (iBEST), Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Northwestern University, Medical College of Georgia, Augusta University 🌍
Introduction: Hyperthermia is an emerging strategy for cancer treatment due to its few side effects and low cost. Hyperthermia is used in conjunction with other treatments, such as radiation therapy, chemotherapy, and heat-activated drug-release liposomes [cite review paper]. There is potential of photoacoustic (PA) thermometry from a commercially available PA imaging system to regulate the temperature during mediated thermal therapies [1]. Photoacoustic imaging's capacity to deliver both structural and functional information simultaneously has prompted its exploration in various applications, including the monitoring of tissue temperature
Purpose: Current methods of thermal imaging localization and control of the temperature can be invasive [1]. The goal of the study is to use a spectral slope method with the ratio of PA power spectra at two different time points to identify temperature changes non-invasively and in real-time
Methods: In this study, surface heating and deep tissue heating of the hind leg of mice were performed in vivo. Fluoroptic thermometers were used to monitor and control temperature changes in real-time. Code developed in MATLAB performs a frequency analysis on the beamformed data and applies a Fourier transform to the signal. It computes the spectral slope, comparing signals from different frames or experimental conditions (such as varying temperature). The code normalizes and smooths the data for plotting and visualizes frequency-domain differences between signals. The spectral slope is computed as a function of the temperature over time.
Results: This approach is efficient, does not require prior tissue knowledge, and leverages the frequency-domain characteristics of PA signals to detect temperature changes for calibration [2].
Conclusion: The results of this method show the success of using the spectral slope analysis technique to extract real-time temperature data throughout the experiment with the implications of providing a non-invasive method of PA thermometry in effectively controlling thermal therapies.