Magnetic Resonance Imaging-Guided Electrical Impedance Tomography for Breast Tumor Imaging

Electrical impedance tomography (EIT) is highly sensitive to tissue conductivity variations, making it particularly effective in distinguishing malignant from normal tissues. This capability highlights its potential for early tumor screening. This article proposes an innovative magnetic resonance imaging (MRI)-guided group sparsity (IGGS) algorithm and integrates it with EIT to form an MRI-guided EIT imaging method. Within the EIT and Diffuse Optical Reconstruction Software (EIDORS) framework, the IGGS algorithm solves both the forward and inverse EIT problems while incorporating MRI priors to enhance the image reconstruction quality. Experimental results demonstrate that, compared to conventional single-mimetic algorithms, such as the Gauss-Newton (GN) method, gradient descent (GD), and the alternating direction method of multipliers (ADMMs), the IGGS algorithm achieves a mean squared error (mse) of 0.3025 and a contrast-to-noise variance (CNV) of 0.7741, significantly improving reconstruction accuracy and sensitivity. In early breast cancer detection, this method exhibits superior accuracy and reliability, offering a more precise and efficient solution for early screening and enhancing its clinical diagnostic value.