ArticleMedical physics2026
A single-view-based electroacoustic tomography imaging using deep learning for electroporation monitoring.
Article in Medical physics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundElectroacoustic tomography (EAT) is an emerging imaging technique with potential for guiding electroporation therapy. However, in clinical applications, EAT scan is typically limited to a single view acquisition, leading to severe artifacts in the reconstruction. PURPOSE: This study aims to develop novel deep-learning models to substantially enhance the image quality of the single-view EAT reconstruction.
methodsWe proposed a two-stage deep learning model (EXPP-Net) to address the limited view problem in EAT image reconstruction. The two-stage model comprises extrapolation and post-processing sub-models. The extrapolation sub-model receives a single-view sinogram u
resultsThe quality of EAT images reconstructed from single-view acquisition was substantially improved by EXPP-Net, showing good agreement with full-view images (linear-array probe study: RMSE: 0.0033 ± 0.0023, SSIM: 0.9968 ± 0.0059, median DICE ≥ 0.9450; convex-array probe study: RMSE: 0.0083 ± 0.0034, SSIM: 0.9958 ± 0.0038, median DICE ≥ 0.8837). In vivo results demonstrated substantial distortion correction. Comparison with the prior method on RMSE, SSIM, and DICE yielded large effect sizes (|β/σ| > 0.8), with all comparisons reaching statistical significance (p < 0.05).
conclusionsThe proposed model generated full-view EAT imaging from a single-view sinogram, substantially improving the quality and precision of EAT. These results suggest its potential for real-time electroporation verification.
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