As photoacoustic imaging (PAI) technology matures, computational modeling will increasingly represent a critical tool for facilitating clinical translation through predictive simulation of real-world performance under a wide range of device and biological conditions. While modeling currently offers a rapid, inexpensive tool for device development and prediction of fundamental image quality metrics (e.g., spatial resolution and contrast ratio), rigorous verification and validation will be required of models used to provide regulatory-grade data that effectively complements and/or replaces in vivo testing. To address methods for establishing model credibility, we developed an integrated computational model of PAI by coupling a previously developed three-dimensional Monte Carlo model of tissue light transport with a two-dimensional (2D) acoustic wave propagation model implemented in the well-known k-Wave toolbox. We then evaluated ability of the model to predict basic image quality metrics by applying standardized verification and validation principles for computational models. The model was verified against published simulation data and validated against phantom experiments using a custom PAI system. Furthermore, we used the model to conduct a parametric study of optical and acoustic design parameters. Results suggest that computationally economical 2D acoustic models can adequately predict spatial resolution, but metrics such as signal-to-noise ratio and penetration depth were difficult to replicate due to challenges in modeling strong clutter observed in experimental images. Parametric studies provided quantitative insight into complex relationships between transducer characteristics and image quality as well as optimal selection of optical beam geometry to ensure adequate image uniformity. Multidomain PAI simulation tools provide high-quality tools to aid device development and prediction of real-world performance, but further work is needed to improve model fidelity, especially in reproducing image noise and clutter.
The goal of this study is to investigate whether reduced breast compression in digital breast tomosynthesis (DBT) exams causes larger internal breast motion that would adversely affect DBT image quality. We designed an experiment to collect real-time breast motion data from patients using ultrasound under three levels of DBT compression (full, medium and half). The ultrasound RF data had a pixel size of 21.5 μm and 150 μm in the axial and lateral directions of the probe, allowing the tracking of very fine movement of internal structure. We have successfully collected data from six human subjects and continue to recruit patients. The data were analyzed using speckle-tracking techniques to extract internal tissue movement trajectories pixel by pixel at multiple locations. Initial data analysis showed that internal breast tissue movement is highly correlated with breathing. Based on the first four patient datasets we have processed, the internal motion magnitudes on average were smaller than 1 mm under the full and reduced compression levels. The statistical distributions of the motion magnitudes among the three compression levels were similar, indicating that the internal breast motion may not necessarily increase even when compression is reduced by half. However, more data will be collected and analyzed to strengthen this study for more solid conclusions.