This study examines the effectiveness of the thermal dose model in accurately predicting thermally induced optical property changes of ex vivo chicken breast between 500-1100 nm. The absorption coefficient, μa, and the reduced scattering coefficient, μ's, of samples are measured as a function of thermal dose over the range 50 °C-70 °C. Additionally, the maximum observable changes in μa and μ's are measured as a function of temperature in the range 50 °C-90 °C. Results show that the standard thermal dose model used in the majority of high-intensity focused ultrasound (HIFU) treatments is insufficient for modeling optical property changes, but that the isodose constant may be modified in order to better predict thermally induced changes. Additionally, results are presented that show a temperature dependence on changes in the two coefficients, with an apparent threshold effect occurring between 65 °C-70 °C.
Real-time acousto-optic (AO) sensing has been shown to non-invasively detect changes in ex vivo tissue optical properties during high-intensity focused ultrasound (HIFU) exposures. The technique is particularly appropriate for monitoring non-cavitating lesions that offer minimal acoustic contrast. This work employs a modeling-based approach to improve the AO sensing of lesion formation during HIFU therapy, to develop treatment strategies for the ablation of large volumes, and to assess the technique's viability and robustness in a clinical setting. The angular spectrum method is used to model the acoustic field from the HIFU source. Spatio-temporal temperature elevations induced by the absorption of ultrasound are modeled using a finite-difference time-domain solution to the Pennes bioheat equation. Changes in tissue optical properties are calculated using a thermal dose model, calibrated using experimental data. The diffuse optical field is modeled using an open-source GPU-accelerated Monte Carlo algorithm. The Monte Carlo algorithm is modified to account for light-sound interactions, using the acoustic field from the angular spectrum method, and to account for AO signal detection. AO signals are presented in the context of a photorefractive-crystal-based detection scheme, and are compared to signals obtained using standard optical detectors. [Work supported in part by the Whitaker International Program.]
Real-time acousto-optic (AO) sensing has been shown to noninvasively detect changes in ex vivo tissue optical properties during high-intensity focused ultrasound (HIFU) exposures. The technique is particularly appropriate for monitoring noncavitating lesions that offer minimal acoustic contrast. A numerical model is presented for an AO-guided HIFU system with an illumination wavelength of 1064 nm and an acoustic frequency of 1.1 MHz. To confirm the model’s accuracy, it is compared to previously published experimental data gathered during AO-guided HIFU in chicken breast. The model is used to determine an optimal design for an AO-guided HIFU system, to assess its robustness, and to predict its efficacy for the ablation of large volumes. It was found that a through transmission geometry results in the best performance, and an optical wavelength around 800 nm was optimal as it provided sufficient contrast with low absorption. Finally, it was shown that the strategy employed while treating large volumes with AO guidance has a major impact on the resulting necrotic volume and symmetry.
Real-time acousto-optic (AO) sensing has been shown to non-invasively detect changes in tissue optical properties, a direct indicator of thermal damage, during high-intensity focused ultrasound (HIFU) therapy. In this work, a comprehensive model is developed to describe the AO sensing of lesion formation during HIFU therapy. The angular spectrum method is used to model ultrasound propagation, and the temperature field due to the absorption of ultrasound is modeled using a finite-difference time-domain (FDTD) solution to the Pennes bioheat equation. Thermal damage dependent optical properties are calculated based on a probabilistic and calibrated thermal dose model. To simulate light propagation inside of insonified and optically heterogeneous tissue, an open-source graphics processing unit (GPU) accelerated Monte Carlo algorithm is used. The Monte Carlo algorithm is modified to account for light-sound interactions, using input from the angular spectrum method, and to account for AO signal detection. Results will show how wavelength and illumination/detection configurations affect the detectability of HIFU lesions using AO sensing.
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