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

• Published in: IEEE transactions on biomedical engineering Vol. 72; no. 9; pp. 2720 - 2731
• Main Authors: Ceccato, Roberto C., Pigatto, Andre V., Aster, Richard C., Pai, Chi-Nan, Mueller, Jennifer L., Furuie, Sergio S.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Biomedical engineering; boundary optimization; Computed tomography; Computer Simulation; Delay effects; Descent; Humans; Image Processing, Computer-Assisted - methods; Image quality; Image reconstruction; Imaging; Inverse problems; Lung - diagnostic imaging; Lungs; Phantoms, Imaging; Positioning; Receivers; Regularization; Robustness; time of flight transmission mode; Tomography; Tomography, X-Ray Computed - methods; Torso; Transducers; Transmitters; Ultrasonic imaging; Ultrasonography - methods; Ultrasound; Ultrasound computed tomography; Uncertainty
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2025.3550823

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