Tri-Modal Joint Inversion Based on Disentangled Variational Autoencoder for Human Thorax Imaging

Zhichao Lin, Rui Guo, Ke Zhang, Haolin Zhang, Maokun Li, Fan Yang, Shenheng Xu, Aria Abubakar

Abstract: In this work, we present a joint inversion framework that enables simultaneous reconstruction of electrical impedance tomography (EIT), microwave tomography (MWT), and ultrasound tomography (UST). The structural and value features of the three modality images are decoupled and encoded into structure code and value code by the proposed disentangled variational autoencoder (DVAE). The inversion is then performed at the feature level, with the structure code and value code treated as unknown parameters to be inverted by the Gauss-Newton method. The interaction among the three modalities is achieved through structure code fusion, where the structure codes of the three modalities are averaged in the inversion as the fused structure code, thus ensuring the consistency of the structures in the three modalities. Two schemes are proposed to utilize the fused structure code. The initial code scheme (ICS) employs it as the initial structure code for the Gauss-Newton update of each modality. Meanwhile, the reference code scheme (RCS) designates it as the reference structure code. Numerical examples of human thorax imaging verify the effectiveness of the two schemes and demonstrate the advantages of the proposed joint inversion framework over separate inversions.