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Phys Med Biol ; 69(7)2024 Mar 14.
Artigo em Inglês | MEDLINE | ID: mdl-38382109

RESUMO

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 vivoandinvivoHIFU 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 usingex vivodatasets demonstrated better generalization performance ininvivoexperiments.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.


Assuntos
Ablação por Ultrassom Focalizado de Alta Intensidade , Redes Neurais de Computação , Ultrassonografia , Processamento de Imagem Assistida por Computador/métodos , Ablação por Ultrassom Focalizado de Alta Intensidade/métodos , Artefatos
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