Real-time assessment of high-intensity focused ultrasound heating and cavitation with hybrid optoacoustic ultrasound imaging

Optimization of ultrasound heating with Pickering droplets using core-shell scattering theory

Nanoparticles find widespread application in various medical contexts, including targeted nanomedicine and enhancing therapeutic efficacy. Moreover, they are employed to stabilize emulsions, giving rise to stabilized droplets known as Pickering droplets. Among the various methods to improve anti-cancer treatment, ultrasound hyperthermia stands out as an efficient approach. This research proposes Pickering droplets as promising sonosensitizer candidates, to enhance the attenuation of ultrasound with simultaneous potential to act as drug carriers. The enhanced ultrasound energy dissipation could be, therefore, optimized by changing the parameters of Pickering droplets. The ultrasound scattering theory, based on the core-shell model, was employed to calculate theoretical ultrasound properties such as attenuation and velocity. Additionally, computer simulations, based on a bioheat transfer model, were utilized to compute heat generation in agar-based phantoms of tissues under different ultrasound wave frequencies. Two types of phantoms were simulated: a pure agar phantom and an agar phantom incorporating spherical inclusions. The spherical inclusions, with a diameter of 10 mm, were doped with various sizes of Pickering droplets, considering their core radius and shell thickness. Computer simulation of these spherical inclusions incorporated within agar phantom resulted in different enhancement of achieved temperature elevation, which depending on the core radius, shell thickness, and the material properties of the system. Notably, spherical inclusions doped with Pickering droplets stabilized by magnetite nanoparticles exhibited a higher temperature rise compared to droplets stabilized by silica nanoparticles. Moreover, nanodroplets with a core radius below 400 nm demonstrated better heating performance compared to microdroplets. Furthermore, Pickering droplets incorporated into agar phantom could allow obtaining a similar effect of local heating as sophisticated focused ultrasound devices.

Experimental and Computational Analysis of High-Intensity Focused Ultrasound Thermal Ablation in Breast Cancer Cells: Monolayers vs. Spheroids

High-intensity focused ultrasound (HIFU) is a non-invasive therapeutic modality that uses precise acoustic energy to ablate cancerous tissues through coagulative necrosis. In this context, we investigate the efficacy of HIFU ablation in two distinct cellular configurations, namely 2D monolayers and 3D spheroids of epithelial breast cancer cell lines (MDA-MB 231 and MCF7). The primary objective is to compare the response of these two in vitro models to HIFU while measuring their ablation percentages and temperature elevation levels. HIFU was systematically applied to the cell cultures, varying ultrasound intensity and duty cycle during different sonication sessions. The results indicate that the degree of ablation is highly influenced by the duty cycle, with higher duty cycles resulting in greater ablation percentages, while sonication duration has a minimal impact. Numerical simulations validate experimental observations, highlighting a significant disparity in the response of 2D monolayers and 3D spheroids to HIFU treatment. Specifically, tumor spheroids require lower temperature elevations for effective ablation, and their ablation percentage significantly increases with elevated duty cycles. This study contributes to a comprehensive understanding of acoustic energy conversion within the biological system during HIFU treatment for 2D versus 3D ablation targets, holding potential implications for refining and personalizing breast cancer therapeutic strategies.

Shedding light on ultrasound in action: Optical and optoacoustic monitoring of ultrasound brain interventions

Monitoring brain responses to ultrasonic interventions is becoming an important pillar of a growing number of applications employing acoustic waves to actuate and cure the brain. Optical interrogation of living tissues provides a unique means for retrieving functional and molecular information related to brain activity and disease-specific biomarkers. The hybrid optoacoustic imaging methods have further enabled deep-tissue imaging with optical contrast at high spatial and temporal resolution. The marriage between light and sound thus brings together the highly complementary advantages of both modalities toward high precision interrogation, stimulation, and therapy of the brain with strong impact in the fields of ultrasound neuromodulation, gene and drug delivery, or noninvasive treatments of neurological and neurodegenerative disorders. In this review, we elaborate on current advances in optical and optoacoustic monitoring of ultrasound interventions. We describe the main principles and mechanisms underlying each method before diving into the corresponding biomedical applications. We identify areas of improvement as well as promising approaches with clinical translation potential.

Deep-Learning-Based High-Intensity Focused Ultrasound Lesion Segmentation in Multi-Wavelength Photoacoustic Imaging

Photoacoustic (PA) imaging can be used to monitor high-intensity focused ultrasound (HIFU) therapies because ablation changes the optical absorption spectrum of the tissue, and this change can be detected with PA imaging. Multi-wavelength photoacoustic (MWPA) imaging makes this change easier to detect by repeating PA imaging at multiple optical wavelengths and sampling the optical absorption spectrum more thoroughly. Real-time pixel-wise classification in MWPA imaging can assist clinicians in monitoring HIFU lesion formation and will be a crucial milestone towards full HIFU therapy automation based on artificial intelligence. In this paper, we present a deep-learning-based approach to segment HIFU lesions in MWPA images. Ex vivo bovine tissue is ablated with HIFU and imaged via MWPA imaging. The acquired MWPA images are then used to train and test a convolutional neural network (CNN) for lesion segmentation. Traditional machine learning algorithms are also trained and tested to compare with the CNN, and the results show that the performance of the CNN significantly exceeds traditional machine learning algorithms. Feature selection is conducted to reduce the number of wavelengths to facilitate real-time implementation while retaining good segmentation performance. This study demonstrates the feasibility and high performance of the deep-learning-based lesion segmentation method in MWPA imaging to monitor HIFU lesion formation and the potential to implement this method in real time.