Jiang Zhuoran, Xu Yifei, Sun Leshan, Srinivasan Shreyas, Wu Q Jackie, Xiang Liangzhong, Ren Lei
Stanford University Stanford USA.
University of California Irvine USA.
Precis Radiat Oncol. 2024 Sep 22;8(3):110-118. doi: 10.1002/pro6.1242. eCollection 2024 Sep.
Nanosecond pulsed electric fields (nsPEF)-based electroporation is a new therapy modality potentially synergized with radiation therapy to improve treatment outcomes. To verify its treatment accuracy intraoperatively, electroacoustic tomography (EAT) has been developed to monitor in-vivo electric energy deposition by detecting ultrasound signals generated by nsPEFs in real-time. However, utility of EAT is limited by image distortions due to the limited-angle view of ultrasound transducers.
This study proposed a supervised learning-based workflow to address the ill-conditioning in EAT reconstruction. Electroacoustic signals were detected by a linear array and initially reconstructed into EAT images, which were then fed into a deep learning model for distortion correction. In this study, 56 distinct electroacoustic data sets from nsPEFs of different intensities and geometries were collected experimentally, avoiding simulation-to-real-world variations. Forty-six data were used for model training and 10 for testing. The model was trained using supervised learning, enabled by a custom rotating platform to acquire paired full-view and single-view signals for the same electric field.
The proposed method considerably improved the image quality of linear array-based EAT, generating pressure maps with accurate and clear structures. Quantitatively, the enhanced single-view images achieved a low-intensity error (RMSE: 0.018), high signal-to-noise ratio (PSNR: 35.15), and high structural similarity (SSIM: 0.942) compared to the reference full-view images.
This study represented a pioneering stride in achieving high-quality EAT using a single linear array in an experimental environment, which improves EAT's utility in real-time monitoring for nsPEF-based electroporation therapy.
基于纳秒脉冲电场（nsPEF）的电穿孔是一种新的治疗方式，可能与放射治疗协同作用以改善治疗效果。为了在术中验证其治疗准确性，已开发出电声层析成像（EAT）技术，通过实时检测nsPEF产生的超声信号来监测体内电能沉积。然而，由于超声换能器的有限视角导致图像失真，限制了EAT的实用性。
本研究提出了一种基于监督学习的工作流程来解决EAT重建中的不适定问题。通过线性阵列检测电声信号，并初步重建为EAT图像，然后将其输入深度学习模型进行失真校正。在本研究中，通过实验收集了56个来自不同强度和几何形状的nsPEF的不同电声数据集，避免了模拟与实际情况的差异。46个数据用于模型训练，10个用于测试。该模型使用监督学习进行训练，通过一个定制的旋转平台获取同一电场的成对全视角和单视角信号。
所提出的方法显著提高了基于线性阵列的EAT的图像质量，生成了结构准确清晰的压力图。在定量方面，与参考全视角图像相比，增强后的单视角图像实现了低强度误差（均方根误差：0.018）、高信噪比（峰值信噪比：35.15）和高结构相似性（结构相似性指数：0.942）。
本研究在实验环境中使用单个线性阵列实现高质量EAT方面迈出了开创性的一步，提高了EAT在基于nsPEF的电穿孔治疗实时监测中的实用性。
