Bionics Research Center, Biomedical Research Division, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea; Division of Bio-Medical Science and Technology, KIST School, Korea University of Science and Technology, Seoul 02792, Republic of Korea.
Bionics Research Center, Biomedical Research Division, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea.
Neuroimage. 2023 Nov 15;282:120411. doi: 10.1016/j.neuroimage.2023.120411. Epub 2023 Oct 14.
Transcranial focused ultrasound (tFUS), in which acoustic energy is focused on a small region in the brain through the skull, is a non-invasive therapeutic method with high spatial resolution and depth penetration. Image-guided navigation has been widely utilized to visualize the location of acoustic focus in the cranial cavity. However, this system is often inaccurate because of the significant aberrations caused by the skull. Therefore, acoustic simulations using a numerical solver have been widely adopted to compensate for this inaccuracy. Although the simulation can predict the intracranial acoustic pressure field, real-time application during tFUS treatment is almost impossible due to the high computational cost. In this study, we propose a neural network-based real-time acoustic simulation framework and test its feasibility by implementing a simulation-guided navigation (SGN) system. Real-time acoustic simulation is performed using a 3D conditional generative adversarial network (3D-cGAN) model featuring residual blocks and multiple loss functions. This network was trained by the conventional numerical acoustic simulation program (i.e., k-Wave). The SGN system is then implemented by integrating real-time acoustic simulation with a conventional image-guided navigation system. The proposed system can provide simulation results with a frame rate of 5 Hz (i.e., about 0.2 s), including all processing times. In numerical validation (3D-cGAN vs. k-Wave), the average peak intracranial pressure error was 6.8 ± 5.5%, and the average acoustic focus position error was 5.3 ± 7.7 mm. In experimental validation using a skull phantom (3D-cGAN vs. actual measurement), the average peak intracranial pressure error was 4.5%, and the average acoustic focus position error was 6.6 mm. These results demonstrate that the SGN system can predict the intracranial acoustic field according to transducer placement in real-time.
经颅聚焦超声（tFUS）通过颅骨将声能聚焦在大脑的一个小区域，是一种具有高空间分辨率和深度穿透性的非侵入性治疗方法。图像引导导航已广泛用于可视化颅腔内声聚焦的位置。然而，由于颅骨引起的显著像差，该系统通常不够精确。因此，广泛采用声学模拟使用数值求解器来补偿这种不准确性。尽管模拟可以预测颅内声压场，但由于计算成本高，几乎不可能在 tFUS 治疗过程中实时应用。在这项研究中，我们提出了一种基于神经网络的实时声学模拟框架，并通过实现模拟引导导航（SGN）系统来测试其可行性。使用具有残差块和多个损失函数的三维条件生成对抗网络（3D-cGAN）模型进行实时声学模拟。该网络由传统的数值声学模拟程序（即 k-Wave）进行训练。然后，通过将实时声学模拟与传统的图像引导导航系统集成，实现 SGN 系统。所提出的系统可以以 5 Hz 的帧率（即约 0.2 s）提供模拟结果，包括所有处理时间。在数值验证（3D-cGAN 与 k-Wave）中，颅内最大压力的平均误差为 6.8±5.5%，声聚焦位置的平均误差为 5.3±7.7mm。在颅骨仿体的实验验证中（3D-cGAN 与实际测量），颅内最大压力的平均误差为 4.5%，声聚焦位置的平均误差为 6.6mm。这些结果表明，SGN 系统可以根据换能器的放置实时预测颅内声场。
