Enhanced Electroacoustic Tomography with Supervised Learning for Real‐time Electroporation Monitoring

Abstract

Abstract Background 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. Methods 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. Results 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. Conclusions 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.

Keywords

• electroacoustic tomography
• electroporation
• interventional therapy
• limited‐angle reconstruction
• supervised learning