ABSTRACT
Abstract Introduction For improved efficiency and security in heat application during hyperthermia, it is important to monitor tissue temperature during treatments. Photoacoustic (PA) pressure wave amplitude has a temperature dependence given by the Gruenesein parameter. Consequently, changes in PA signal amplitude carry information about temperature variation in tissue. Therefore, PA has been proposed as an imaging technique to monitor temperature during hyperthermia. However, no studies have compared the performance of different algorithms to generate PA-based thermal images. Methods Here, four methods to estimate variations in PA signal amplitude for thermal image formation were investigated: peak-to-peak, integral of the modulus, autocorrelation of the maximum value, and energy of the signal. Changes in PA signal amplitude were evaluated using a 1-D window moving across the entire image. PA images were acquired for temperatures ranging from 36oC to 41oC using a phantom immersed in a temperature controlled thermal bath. Results The results demonstrated that imaging processing parameters and methods involved in tracking variations in PA signal amplitude drastically affected the sensitivity and accuracy of thermal images formation. The sensitivity fluctuated more than 20% across the different methods and parameters used. After optimizing the parameters to generate the thermal images using an evolutionary genetic algorithm (GA), the percentage of pixels within the acceptable error was improved, in average, by 7.5%. Conclusion Optimization of processing parameters using GA could increase the accuracy of measurement for this experimental setup and improve quality of PA-based thermal images.
ABSTRACT
Abstract Introduction Magneto-motive ultrasound (MMUS) combines magnetism and ultrasound (US) to detect magnetic nanoparticles in soft tissues. One type of MMUS called shear-wave dispersion magneto-motive ultrasound (SDMMUS) analyzes magnetically induced shear waves (SW) to quantify the elasticity and viscosity of the medium. The lack of an established presets or protocols for pre-clinical and clinical studies currently limits the use of MMUS techniques in the clinical setting. Methods This paper proposes a platform to acquire, process, and analyze MMUS and SDMMUS data integrated with a clinical ultrasound equipment. For this purpose, we developed an easy-to-use graphical user interface, written in C++/Qt4, to create an MMUS pulse sequence and collect the ultrasonic data. We designed a graphic interface written in MATLAB to process, display, and analyze the MMUS images. To exemplify how useful the platform is, we conducted two experiments, namely (i) MMUS imaging to detect magnetic particles in the stomach of a rat, and (ii) SDMMUS to estimate the viscoelasticity of a tissue-mimicking phantom containing a spherical target of ferrite. Results The developed software proved to be an easy-to-use platform to automate the acquisition of MMUS/SDMMUS data and image processing. In an in vivo experiment, the MMUS technique detected an area of 6.32 ± 1.32 mm2 where magnetic particles were heterogeneously distributed in the stomach of the rat. The SDMMUS method gave elasticity and viscosity values of 5.05 ± 0.18 kPa and 2.01 ± 0.09 Pa.s, respectively, for a tissue-mimicking phantom. Conclusion Implementation of an MMUS platform with addressed presets and protocols provides a step toward the clinical implementation of MMUS imaging equipment. This platform may help to localize magnetic particles and quantify the elasticity and viscosity of soft tissues, paving a way for its use in pre-clinical and clinical studies.