Adaptive Edge-Enhanced Markov Chain Monte Carlo Method for Sound Speed Reconstruction in Ultrasound Computed Tomography

Ultrasound computed tomography (USCT) is a noninvasive biomedical imaging modality that can reconstruct the speed of sound (SOS) distributions of biological tissues for disease diagnosis. Image reconstruction methods are crucial for USCT and have been receiving increasing attention. However, despite good accuracy and fast reconstruction speed, state-of-the-art methods fail to quantify the uncertainty of reconstructed SOS distributions. Herein, we propose an adaptive edge-enhanced Markov chain Monte Carlo (AEE-MCMC) method for rendering the SOS distribution with uncertainty estimation in USCT. The performance of the proposed method is thoroughly demonstrated with numerical simulations, phantom experiments, and ex vivo experiments. Results show that the proposed method provides SOS distributions with sharper edges compared to the conventional nonstatistical and maximum a posteriori (MAP)-based statistical methods and successfully quantifies the uncertainty of reconstructed SOS distributions. Moreover, the proposed method precisely distinguishes heterogeneous structures of ex vivo organs and can differentiate abnormal tissues from normal tissues. Overall, the proposed AEE-MCMC method offers the opportunity to provide high-resolution reconstruction of SOS distributions and quantify its uncertainty estimation in USCT, which allows for reliable analysis of achieved results and is critical for lesion examination in the clinic.