Time of Flight Transmission Mode Ultrasound Computed Tomography With Expected Gradient and Boundary Optimization

Quantitative time of flight in transmission mode ultrasound computed tomography (TFTM USCT) is a promising, cost-effective, and non-invasive modality, particularly suited for functional imaging. However, TFTM USCT encounters resolution challenges due to path information concentration in specific medium regions and uncertainty in transducer positioning. This study proposes a method to enhance resolution and robustness, focusing on low-frequency TFTM USCT for pulmonary imaging.

The proposed technique improves the orientation of steepest descent algorithm steps, preventing resolution degradation due to path information concentration, while allowing for a posteriori sensor positioning retrieval. Total variation regularization is employed to stabilize the inverse problem, and a modified Barzilai-Borwein method determined the step size in the steepest descent algorithm. The proposed method was validated through simulations of data on healthy and abnormal cross-sections of a human chest using MATLAB's k-Wave toolbox. Additionally, experimental data were collected using a Verasonics Vantage 64 low-frequency system and a ballistic gel torso-mimicking phantom to assess robustness under a more realistic environment, closer to that of a clinical situation.

The results showed that the proposed method significantly improved image quality and successfully retrieved sensor locations from imprecise positioning.

This study is the first to address transducer location uncertainty on a transducer belt in TFTM USCT and to apply an estimated gradient approach. Additionally, low-frequency USCT for lung imaging is quite novel, and this work addresses practical questions that will be important for translational development.