GSR: EIT再構成のためのガウシアン・スプラッティングベースのフレームワーク

This paper introduces 2D Gaussian Splatting (GS) to Electrical Impedance Tomography (EIT), marking its first application in this field. Initially developed for computer vision tasks such as scene reconstruction, GS enables continuous representation and efficient rendering of high-resolution images. Building on these capabilities, we propose a novel GS-based EIT reconstruction framework that models conductivity distributions as a set of Gaussian kernels. These kernels act as localized basis functions, dynamically adjusting their parameters (e.g., position, covariance, and amplitude) to enhance representation accuracy. To ensure regularization and physical constraints, we integrate a threshold-adjusted ReLU activation function to filter out insignificant components and a Sigmoid function to constrain conductivity values within a valid physical range. Experimental results on both simulated and real datasets demonstrate that our approach outperforms traditional model-driven methods and is competitive with conventional neural network-based methods in reconstruction quality. Furthermore, systematic ablation studies confirm the effectiveness of the key components of our framework. This work opens new possibilities for integrating advanced rendering techniques into EIT and inverse problem solving, bridging the gap between computer vision and biomedical imaging.