School of Mathematics and Computing (Computational Science and Engineering), Seoul 03722, Republic of Korea.
Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame 46556, IN, USA.
Comput Methods Programs Biomed. 2023 Jul;237:107591. doi: 10.1016/j.cmpb.2023.107591. Epub 2023 May 8.
Transcranial focused ultrasound (tFUS) has emerged as a new non-invasive brain stimulation (NIBS) modality, with its exquisite ability to reach deep brain areas at a high spatial resolution. Accurate placement of an acoustic focus to a target region of the brain is crucial during tFUS treatment; however, the distortion of acoustic wave propagation through the intact skull casts challenges. High-resolution numerical simulation allows for monitoring of the acoustic pressure field in the cranium but also demands extensive computational loads. In this study, we adopt a super-resolution residual network technique based on a deep convolution to enhance the prediction quality of the FUS acoustic pressure field in the targeted brain regions.
The training dataset was acquired by numerical simulations performed at low-(1.0 mm) and high-resolutions (0.5mm) on three ex vivo human calvariae. Five different super-resolution (SR) network models were trained by using a multivariable dataset in 3D, which incorporated information on the acoustic pressure field, wave velocity, and localized skull computed tomography (CT) images.
The accuracy of 80.87±4.50% in predicting the focal volume with a substantial improvement of 86.91% in computational cost compared to the conventional high-resolution numerical simulation was achieved. The results suggest that the method can greatly reduce the simulation time without sacrificing accuracy and improve the accuracy further with the use of additional inputs.
In this research, we developed multivariable-incorporating SR neural networks for transcranial focused ultrasound simulation. Our super-resolution technique may contribute to promoting the safety and efficacy of tFUS-mediated NIBS by providing on-site feedback information on the intracranial pressure field to the operator.
经颅聚焦超声（tFUS）作为一种新的非侵入性脑刺激（NIBS）模式出现，其具有将能量精确传递至深部脑区的能力，且空间分辨率较高。在 tFUS 治疗过程中，将声聚焦准确放置到目标脑区至关重要；然而，颅骨完整时声波传播的失真带来了挑战。高分辨率数值模拟可以监测颅骨内的声压场，但也需要大量的计算负荷。在本研究中，我们采用了一种基于深度卷积的超分辨率残差网络技术，以提高靶向脑区 FUS 声压场的预测质量。
通过在三个离体人头骨上进行低分辨率（1.0mm）和高分辨率（0.5mm）的数值模拟获得训练数据集。使用包含声压场、波速和局部颅骨计算机断层扫描（CT）图像信息的三维多变量数据集训练了五个不同的超分辨率（SR）网络模型。
与传统的高分辨率数值模拟相比，该方法在预测焦点体积方面的准确率达到了 80.87%±4.50%，计算成本大幅提高了 86.91%。结果表明，该方法可以在不牺牲准确性的前提下大大减少模拟时间，并且通过使用额外的输入可以进一步提高准确性。
在这项研究中，我们开发了用于经颅聚焦超声模拟的多变量纳入的 SR 神经网络。我们的超分辨率技术可以通过向操作人员提供颅内压力场的现场反馈信息，为 tFUS 介导的 NIBS 的安全性和有效性提供帮助。
