Control of treatment size in cavitation-enhanced high-intensity focused ultrasound using radio-frequency echo signals

Self Cite

Acoustic cavitation bubbles are known to enhance the heating effect in high-intensity focused ultrasound (HIFU) treatment. The detection of cavitation bubbles with high sensitivity and selectivity is required to predict the therapeutic and side effects of cavitation, and ensure the efficacy and safety of the treatment. A pulse inversion (PI) technique has been widely used for imaging microbubbles through enhancing the second-harmonic component of echo signals. However, it has difficulty in separating the nonlinear response of microbubbles from that due to nonlinear propagation. In this study, a triplet pulse (3P) method was investigated to specifically image cavitation bubbles by extracting the 1.5th fractional harmonic component. The proposed 3P method depicted cavitation bubbles with a contrast ratio significantly higher than those in conventional imaging methods with and without PI. The results suggest that the 3P method is effective for specifically detecting microbubbles in cavitation-enhanced HIFU treatment.

Section: Discussionmentioning

confidence: 99%

Selective detection of cavitation bubbles by triplet pulse sequence in high-intensity focused ultrasound treatment

Iwasaki

Nagaoka

Yoshizawa

et al. 2018

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“…The investigation of bubble imaging and evaluation of the acoustic field have become very important in recent years, and the role of cavitation bubbles in these studies has been examined. 21,22) In addition, we found that the hydrophone can be used for measurements in high-intensity acoustic fields even at 20 kHz frequencies. This hydrophone had a wide-frequency range receiving capability from 20 kHz up to 10 MHz.…”

Section: Introductionmentioning

confidence: 83%

Relationship between acoustic cavitation bubble behavior and output signal from tough hydrophone using high-speed camera in high-frequency and low-frequency acoustic fields

Okada

Shiiba

Yamauchi

et al. 2018

In previous works, our developed tough hydrophone was resistant to high-pressure fields (15 MPa). During the experiment, acoustic cavitation bubbles were generated around the tough hydrophone tip, because the tip was slightly larger than the wavelength and was flat. In this study, the influence of the tough hydrophone on high-intensity 1 MHz and 22 kHz acoustic fields was investigated by visualizing bubbles around the hydrophone and recording the waveform from the hydrophone at the same time. As a result, the hydrophone with a flat tip can be used for measurements in 1 MHz and 22 kHz high-intensity acoustic fields under certain conditions where acoustic bubble cloud generation on the hydrophone tip is avoided. The change in output waveforms from the hydrophone was negligibly small, even when collision and sticking of the acoustic bubble to the hydrophone tip occurred.

“…We have been focusing research on the ultrasound imaging for HIFU treatment. [12][13][14][15] One of the problems with ultrasound imaging during HIFU treatment is that the therapeutic ultrasound components (HIFU noise) interfere with the diagnostic ultrasound components, making it impossible to monitor the tissue changes during HIFU exposure. In our previous study, [16][17][18][19] we proposed a method to remove only therapeutic ultrasound components while retaining the diagnostic ultrasound components.…”

Section: Introductionmentioning

confidence: 99%

Noise reduction technique using deep learning for ultrasound imaging during high-intensity focused ultrasound treatment

Takagi

Koseki

2022

Self Cite

One of the problems with ultrasound imaging during high intensity focused ultrasound (HIFU) treatment is that the therapeutic ultrasound components interfere with the diagnostic ultrasound components, making it impossible to monitor the tissue changes during HIFU exposure. In this study, a convolutional neural network (CNN) framework was applied to the reconstructed ultrasound images with HIFU noise to remove the therapeutic ultrasound components while the diagnostic ultrasound components remain intact. In the experiments, the chicken breast was used as a tissue sample and exposed to HIFU in the water tank. The ultrasound images with and without noise were acquired during and intermission period of HIFU exposure and the noise reduced images was predicted using the proposed multi-layer regression CNN model through the training process. As a result, ultrasound images with sufficient spatial resolution to detect the thermal lesion were acquired.