Transcranial Phase Correction Using Pulse-Echo Ultrasound and Deep Learning: A 2-D Numerical Study.
Transcranial Phase Correction Using Pulse-Echo Ultrasound and Deep Learning: A 2-D Numerical Study.
复制标题
使用脉冲回波超声和深度学习进行经颅相位校正：二维数值研究。
DOI：
10.1109/tuffc.2023.3340597
复制
发表时间：
2024
期刊：
影响因子：
--
通讯作者：
Han,Aiguo
中科院分区：
文献类型：
--
作者：
Tian,Zixuan;Olmstead,Matthew;Jing,Yun;Han,Aiguo
人体颅骨引起的相位畸变严重影响了经颅超声图像的质量，给经颅超声技术在成人中的实际应用带来了重大挑战。如果颅骨轮廓（即，厚度分布）和声速（SOS）是已知的。然而，由于超声与颅骨之间相互作用的复杂性，使用基于物理学的方法使用超声准确估计颅骨轮廓和SOS具有挑战性。本文提出了一种深度学习方法，用于使用从颅骨反向散射的超声射频（RF）信号来估计颅骨轮廓和SOS。数值研究进行了测试的方法的可行性。在这个数值研究中，从五个离体人类头骨的计算机断层扫描（CT）扫描构建了逼真的数值头骨模型。对3595个颅骨段进行声学模拟，以生成基于阵列的超声背向散射信号。开发并训练深度学习模型，以根据RF通道数据估计颅骨厚度和SOS。训练的模型被证明是高度准确的。厚度估计的平均绝对误差（MAE）为0.15 mm（2%误差），SOS估计的平均绝对误差（MAE）为13 m/s（0.5%误差）。皮尔森相关系数之间的估计值和地面实况值为0.99的厚度和SOS为0.95。使用深度学习估计的颅骨厚度和SOS值进行的像差校正产生了显著改善的光束聚焦（例如，较窄的波束）和经颅成像质量（例如，改进的空间分辨率和减少的伪像）。结果表明，所提出的经颅相位畸变校正方法的可行性。
Phase aberration caused by human skulls severely degrades the quality of transcranial ultrasound images, posing a major challenge in the practical application of transcranial ultrasound techniques in adults. Aberration can be corrected if the skull profile (i.e., thickness distribution) and speed of sound (SOS) are known. However, accurately estimating the skull profile and SOS using ultrasound with a physics-based approach is challenging due to the complexity of the interaction between ultrasound and the skull. A deep learning approach is proposed herein to estimate the skull profile and SOS using ultrasound radiofrequency (RF) signals backscattered from the skull. A numerical study was performed to test the approach’s feasibility. Realistic numerical skull models were constructed from computed tomography (CT) scans of five ex vivo human skulls in this numerical study. Acoustic simulations were performed on 3595 skull segments to generate array-based ultrasound backscattered signals. A deep learning model was developed and trained to estimate skull thickness and SOS from RF channel data. The trained model was shown to be highly accurate. The mean absolute error (MAE) was 0.15 mm (2% error) for thickness estimation and 13 m/s (0.5% error) for SOS estimation. The Pearson correlation coefficient between the estimated and ground-truth values was 0.99 for thickness and 0.95 for SOS. Aberration correction performed using deep-learning-estimated skull thickness and SOS values yielded significantly improved beam focusing (e.g., narrower beams) and transcranial imaging quality (e.g., improved spatial resolution and reduced artifacts) compared with no aberration correction. The results demonstrate the feasibility of the proposed approach for transcranial phase aberration correction.