Cheng, W., Zhang, R., Ciobanu, C., Bi, R., Deán-Ben, X. L., Zesheng, Z., Balasundaram, G., Goh, Y., Olivo, M., Razansky, D., & Fan, Z. (2026). Improving axial resolution uniformity in deep-tissue optoacoustic imaging via entropy-driven design of dual-frequency multi-segment arrays. Ultrasonics, 159, 107875. https://doi.org/10.1016/j.ultras.2025.107875
Abstract:
Dual-frequency or multi-frequency transducers have been proposed to balance deep
penetration and high resolution in optoacoustic (OA) imaging, based on the wellestablished
tradeoff that low frequencies provide deeper penetration, while high frequencies
offer higher resolution. In practice, conventional transducer designs are primarily
guided by the signal’s center frequency and bandwidth, as these parameters fundamentally
constrain spatial resolution. However, such criteria alone are insufficient,
as they overlook the influence of transducer geometry within the array. To address
this limitation, we introduce k-space analysis and a weighted entropy (WE) metric that
links transducer design parameters to directional resolution performance. Simulations
and phantom experiments validated that the dual-frequency multi-segment transducer
array (DF-MSTA), combining 3 and 7.5MHz, achieved more uniform and enhanced
axial resolution (by up to 23.8%), compared to a single-frequency MSTA operating at 7.5MHz. The results align with predictions from the k-space analysis and WE quantification.
This work provides a transducer design strategy that jointly considers frequency
selection and array geometry, along with a quantitative framework to optimize
axial resolution in deep-tissue OA imaging, offering insights beyond conventional approaches.
License type:
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Funding Info:
This research / project is supported by the A*STAR - Manufacturing, Trade, and Connectivity Young Individual Research Grant
Grant Reference no. : M24N8c0099
This research / project is supported by the A*STAR - BMRC Central Research Fund 2024
Grant Reference no. : NA
This research / project is supported by the A*STAR - IEO De-centralised GAP Funds
Grant Reference no. : I24D1AG00