Regularized full waveform inversion for low frequency ultrasound tomography with a structural similarity EIT prior

Abstract

In previous work by Rueter et al., low frequency (10–750 kHz) ultrasound was shown to penetrate the lung, motivating its use as a nonionizing tomographic technique for pulmonary monitoring. Here, we present a method for regularized full waveform inversion for low-frequency tomographic ultrasound data. A novel structural similarity index metric (SSIM)-based regularization term is introduced that compares the structural correlation of the current sound speed iterate to a prior reconstruction computed by electrical impedance tomography (EIT). Full waveform reconstructions of sound speeds from numerically simulated low frequency ultrasound data with 0.1% additive Gaussian noise are computed, and the results are compared using Tikhonov regularization, total variation regularization, and both terms combined with the SSIM-EIT regularization term. The EIT reconstruction was computed from simulated voltage data on the same phantom using one step of a Newton-Raphson method with a high-pass Gaussian filter as a regularizer. Reconstructions including the SSIM-EIT regularization term converged fastest, and an adaptive regularization parameter provided the most accurate reconstructions. The method is then iterated by computing an EIT reconstruction using the USCT result as a prior, and a subsequent USCT reconstruction is computed using the SSIM-EIT regularization term with the updated EIT reconstruction.