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使用深度学习进行相控阵超声治疗的经颅自适应像差校正

Transcranial adaptive aberration correction using deep learning for phased-array ultrasound therapy.

作者信息

Zhang Quan, Sun Weihao, Deng Jie, Qi Tingting, Wan Mingxi, Lu Mingzhu

机构信息

The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi' an Jiaotong University, Xi'an 710049, China.

The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi' an Jiaotong University, Xi'an 710049, China.

出版信息

Ultrasonics. 2025 Aug;152:107641. doi: 10.1016/j.ultras.2025.107641. Epub 2025 Mar 14.

Abstract

This study aims to explore the feasibility of a deep learning approach to correct the distortion caused by the skull, thereby developing a transcranial adaptive focusing method for safe ultrasonic treatment in opening of the blood-brain barrier (BBB). However, aberration correction often requires significant computing power and time to ensure the accuracy of phase correction. This is due to the need to solve the evolution procedure of the sound field represented by numerous discretized grids. A combined method is proposed to train the phase prediction model for correcting the phase accurately and quickly. The method comprises pre-segmentation, k-Wave simulation, and a 3D U-net-based network. We use the k-Wave toolbox to construct a nonlinear simulation environment consisting of a 256-element phased array, a small piece of skull, and water. The skull sound speed sample combining with the phase delay serves as input for the model training. The focus volume and grating lobe level obtained by the proposed approach were the closest to those obtained by the time reversal method in all relevant approaches. Furthermore, the mean peak value obtained by the proposed approach was no less than 77% of that of the time reversal method. In this study, the computational cost of each sample's phase delay was no more than 0.05 s, which was 1/200th of the time reversal method. The proposed method eliminates the complexity of numerical calculation processes requiring consideration of more acoustic parameters, while circumventing the substantial computational resource demands and time-consuming challenges to traditional numerical approaches. The proposed method enables rapid, precise, and adaptive transcranial aberration correction on the 3D skull-based conditions, overcoming the potential inaccuracies in predicting the focal position or the acoustic energy distribution from 2D simulations. These results show the possibility of the proposed approach enabling near-real-time correction of skull-induced phase aberrations to achieve transcranial focus, thereby offering a novel option for treating brain diseases through temporary BBB opening.

摘要

本研究旨在探索一种深度学习方法来校正颅骨引起的畸变,从而开发一种用于安全超声治疗打开血脑屏障(BBB)的经颅自适应聚焦方法。然而,像差校正通常需要大量的计算能力和时间来确保相位校正的准确性。这是由于需要求解由大量离散网格表示的声场的演化过程。提出了一种组合方法来训练相位预测模型,以准确快速地校正相位。该方法包括预分割、k-Wave模拟和基于3D U-net的网络。我们使用k-Wave工具箱构建一个由256元相控阵、一小块颅骨和水组成的非线性模拟环境。结合相位延迟的颅骨声速样本用作模型训练的输入。在所提出的方法中获得的聚焦体积和旁瓣水平在所有相关方法中最接近通过时间反转方法获得的结果。此外,所提出的方法获得的平均峰值不低于时间反转方法的77%。在本研究中,每个样本相位延迟的计算成本不超过0.05秒,这是时间反转方法的1/200。所提出的方法消除了需要考虑更多声学参数的数值计算过程的复杂性,同时规避了传统数值方法对大量计算资源的需求和耗时的挑战。所提出的方法能够在基于3D颅骨的条件下进行快速、精确和自适应的经颅像差校正,克服了从2D模拟预测焦点位置或声能分布时的潜在不准确性。这些结果表明所提出的方法能够近乎实时地校正颅骨引起的相位像差以实现经颅聚焦的可能性,从而为通过临时打开BBB治疗脑部疾病提供了一种新的选择。

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