{"doi":"10.1002/pro6.1242","title":"Enhanced Electroacoustic Tomography with Supervised Learning for Real‐time Electroporation Monitoring","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.","journal":"Precision Radiation Oncology","year":2024,"id":437268,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":11,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9542,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1079578,"name":"Yifei Xu","orcid":"0000-0002-0693-4184","position":1,"is_corresponding":false},{"id":876450,"name":"Leshan Sun","orcid":"0009-0008-1312-2388","position":2,"is_corresponding":false},{"id":1246719,"name":"Shreyas Srinivasan","orcid":null,"position":3,"is_corresponding":false},{"id":345265,"name":"Qiuwen Wu","orcid":"0000-0003-0748-7280","position":4,"is_corresponding":false},{"id":397909,"name":"Liangzhong Xiang","orcid":"0000-0002-7228-0510","position":5,"is_corresponding":false},{"id":364160,"name":"Lei Ren","orcid":"0000-0002-6136-3505","position":6,"is_corresponding":false},{"id":364327,"name":"Zhuoran Jiang","orcid":"0000-0002-7637-1102","position":0,"is_corresponding":true}],"reference_count":37,"raw_metadata":null,"created_at":"2026-07-19T02:00:29.872940Z","pmid":"40336975","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}