AI-Driven 2D Electric Field Distribution Estimation in a Human Breast Model Formed by a Line Source
TL;DR
This study addresses the computational challenges of estimating two-dimensional electric field distributions within heterogeneous breast tissue models, a critical task in medical applications such as microwave imaging and hyperthermia, and proposes a deep learning approach that predicts EF distributions directly from dielectric property maps.
Abstract
This study addresses the computational challenges of estimating two-dimensional electric field (EF) distributions within heterogeneous breast tissue models, a critical task in medical applications such as microwave imaging and hyperthermia. Traditional numerical simulation methods are accurate but computationally expensive, often requiring minutes of processing time. To overcome this limitation, we propose a deep learning approach that predicts EF distributions directly from dielectric property maps, specifically electrical conductivity and permittivity, significantly reducing inference time to a matter of seconds. Two convolutional neural network architectures are evaluated: a U-Net model with a ResNeXt50 encoder (R50-UNet) and a standard ResNeXt50 model (R50). Additionally, we introduce a masking-based loss function that emphasizes learning in regions of highest relevance within the domain. Quantitative evaluation demonstrates that the R50-UNet model outperforms the standard R50 model, achieving up to a 14.66 dB improvement in signal-to-noise ratio (SNR). The application of the masking method further enhances performance, with an additional gain of up to 3.24 dB in SNR. Data efficiency analysis reveals that while the R50 model reaches performance saturation with only 25% of the available training data, the R50-UNet architecture combined with masked loss continues to improve as more data are utilized. These findings support the feasibility of using deep learning for fast and accurate EF prediction in biomedical scenarios where computation time and spatial precision are critical.