An acoustic backscatter-based method for estimating attenuation towards monitoring lesion formation in high intensity focused ultrasound

Rahimian, Siavash; Tavakkoli, Jahan

doi:10.1063/1.4769926

Cited by 7 publications

(9 citation statements)

References 7 publications

(8 reference statements)

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“…[ 24 ] The transient characteristics of tissue attenuation coefficient were investigated before, during and after HIFU treatment. [ 25 ] A common spectral difference attenuation estimation method to measure changing in attenuation coefficient during HIFU exposure, known as the multi-narrow-band (MNB) method used by. [ 11 ] The different segments according to the depth were windowed along an RF A-line from one backscattered RF data frame from the tissue.…”

Section: Methodsmentioning

confidence: 99%

A feed-forward neural network algorithm to detect thermal lesions induced by high intensity focused ultrasound in tissue

et al. 2012

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Non-invasive ultrasound surgeries such as high intensity focused ultrasound have been developed to treat tumors or to stop bleeding. In this technique, incorporation of a suitable imaging modality to monitor and control the treatments is essential so several imaging methods such as X-ray, Magnetic resonance imaging and ultrasound imaging have been proposed to monitor the induced thermal lesions. Currently, the only ultrasound imaging technique that is clinically used for monitoring this treatment is standard pulse-echo B-mode ultrasound imaging. This paper describes a novel method for detecting high intensity focused ultrasound-induced thermal lesions using a feed forward neural-network. This study was carried on in vitro animal tissue samples. Backscattered radio frequency signals were acquired in real-time during treatment in order to detect induced thermal lesions. Changes in various tissue properties including tissue's attenuation coefficient, integrated backscatter, scaling parameter of Nakagami distribution, frequency dependent scatterer amplitudes and tissue vibration derived from the backscattered radio frequency data acquired 10 minutes after treatment regarding to before treatment were used in this study. These estimated parameters were used as features of the neural network. Estimated parameters of two sample tissues including two thermal lesions and their segmented B-mode images were used along with the pathological results as training data for the neural network. The results of the study shows that the trained feed forward neural network could effectively detect thermal lesions in vitro. Comparing the estimated size of the thermal lesion (9.6 mm × 8.5 mm) using neural network with the actual size of that from physical examination (10.1 mm × 9 mm) shows that we could detect high intensity focused ultrasound thermal lesions with the difference of 0.5 mm × 0.5 mm.

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Section: Methodsmentioning

confidence: 99%

A feed-forward neural network algorithm to detect thermal lesions induced by high intensity focused ultrasound in tissue

et al. 2012

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“…During MWA treatment, the hyper-echoic regions in the ultrasonic B-mode images can mainly be attributed to gas bubbles, therefore, changes in attenuation may also be the result of gas bubbles. Rahimian and Tavakkoli [37] used the attenuation parameter to monitor HIFU ablation in an ex vivo bovine liver, and suggested that the rapid increase in Da 0 may be attributed to bubble activity, which can cause abrupt changes in the acoustic properties of the tissue at the HIFU treatment site. Our previous work also indicates that bubble activity has a dominant effect on the dynamic changes in attenuation during thermal ablation [39].…”

Section: Discussionmentioning

confidence: 99%

“…After thermal ablation treatment, changes in acoustic properties, such as backscattering [28], attenuation [29][30][31], Nakagami parameter [32,33] and mean scatterer spacing [34], have been proposed as methods of evaluating thermal lesions. As a quantifiable tissue property, acoustic attenuation was previously reported to be a useful parameter for accurate visualisation of thermal lesions, due to its potential for predicting the position, shape and size of lesions [31,[35][36][37]. Conventional ultrasonic B-mode images have been clinically used for guiding and monitoring MWA with the aid of the hyper-echoic regions in the ultrasound image [17,38].…”

Section: Introductionmentioning

confidence: 99%

“…In addition to the attenuation coefficient, the feasibility of using another attenuation parameter to monitor HIFU treatments, the attenuation coefficient intercept ða 0 Þ, has been investigated. The attenuation coefficient intercept ða 0 Þ is defined as the attenuation coefficient in dB/cm at the centre frequency of the transmitted ultrasound pulse [37]. Rahimian and Tavakkoli [36,37] previously studied the characteristics of the attenuation coefficient intercept in ex vivo porcine muscle tissue during and after HIFU treatments, and suggested that the dynamic changes in the attenuation coefficient intercept parameter may be used to evaluate HIFU-induced lesions.…”

Section: Introductionmentioning

confidence: 99%

“…The attenuation coefficient intercept ða 0 Þ is defined as the attenuation coefficient in dB/cm at the centre frequency of the transmitted ultrasound pulse [37]. Rahimian and Tavakkoli [36,37] previously studied the characteristics of the attenuation coefficient intercept in ex vivo porcine muscle tissue during and after HIFU treatments, and suggested that the dynamic changes in the attenuation coefficient intercept parameter may be used to evaluate HIFU-induced lesions. The ultrasonic differential attenuation coefficient intercept (Da 0 ) is estimated by subtracting the initial value of a 0 from values calculated during MWA, and represents the variation in the attenuation coefficient intercept within a predefined area of tissue.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

In vivo monitoring of microwave ablation in a porcine model using ultrasonic differential attenuation coefficient intercept imaging

Zhang

Shang

et al. 2018

International Journal of Hyperthermia

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In this study, the feasibility of using ultrasonic differential attenuation coefficient intercept (Δα) imaging to evaluate thermal lesions induced by microwave ablation (MWA) was explored using an in vivo porcine model. The attenuation coefficient intercept (Δα is estimated by subtracting an initial value of Δα images. Receiver operating characteristic (ROC) curves and the area under ROC curve (AUC) were employed to statistically assess the predictability of ultrasonic imaging. Ultrasonic Δα values were approximately 0.13 dB/cm and 0.16 dB/cm in a normal liver and kidney, respectively, increasing to 2.9 dB/cm and 2.55 dB/cm in ablated regions after MWA. The CNR values of the ultrasonic Δα images (0.9 dB and 0.6 dB in the liver and kidney, respectively) were significantly higher (p < 0.05) than the values of B-mode images (0.6 dB and 0.3 dB). The AUC value of the ultrasonic Δα image was higher than the B-mode image value, 0.95 compared with 0.88. This in vivo study suggests that ultrasonic Δα imaging has the potential to evaluate thermal lesions with high accuracy and better image contrast for monitoring MWA.

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Suppressing HIFU interference in ultrasound images using 1D U-Net-based neural networks

Yang,

Li,

Liu

et al. 2024

Phys. Med. Biol.

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Objective. One big challenge with high-intensity focused ultrasound (HIFU) is that the intense acoustic interference generated by HIFU irradiation overwhelms the B-mode monitoring images, compromising monitoring effectiveness. This study aims to overcome this problem using a one-dimensional (1D) deep convolutional neural network. Approach. U-Net-based networks have been proven to be effective in image reconstruction and denoising, and the two-dimensional (2D) U-Net has already been investigated for suppressing HIFU interference in ultrasound monitoring images. In this study, we propose that the one-dimensional (1D) convolution in U-Net-based networks is more suitable for removing HIFU artifacts and can better recover the contaminated B-mode images compared to 2D convolution. Ex-vivo and in-vivo HIFU experiments were performed on a clinically equivalent ultrasound-guided HIFU platform to collect image data, and the 1D convolution in U-Net, Attention U-Net, U-Net++, and FUS-Net was applied to verify our proposal. Main results. All 1D U-Net-based networks were more effective in suppressing HIFU interference than their 2D counterparts, with over 30% improvement in terms of structural similarity (SSIM) to the uncontaminated B-mode images. Additionally, 1D U-Nets trained using ex-vivo datasets demonstrated better generalization performance in in-vivo experiments. Significance. These findings indicate that the utilization of 1D convolution in U-Net-based networks offers great potential in addressing the challenges of monitoring in ultrasound-guided HIFU systems.

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An acoustic backscatter-based method for estimating attenuation towards monitoring lesion formation in high intensity focused ultrasound

Cited by 7 publications

References 7 publications

A feed-forward neural network algorithm to detect thermal lesions induced by high intensity focused ultrasound in tissue

A feed-forward neural network algorithm to detect thermal lesions induced by high intensity focused ultrasound in tissue

In vivo monitoring of microwave ablation in a porcine model using ultrasonic differential attenuation coefficient intercept imaging

Suppressing HIFU interference in ultrasound images using 1D U-Net-based neural networks

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