Kavli Affiliate: Lihong V. Wang
| First 5 Authors: Jiayun Wang, Jiayun Wang, , ,
| Summary:
Photoacoustic computed tomography (PACT) combines optical contrast with
ultrasonic resolution, achieving deep-tissue imaging beyond the optical
diffusion limit. While three-dimensional PACT systems enable high-resolution
volumetric imaging for applications spanning transcranial to breast imaging,
current implementations require dense transducer arrays and prolonged
acquisition times, limiting clinical translation. We introduce Pano (PACT
imaging neural operator), an end-to-end physics-aware model that directly
learns the inverse acoustic mapping from sensor measurements to volumetric
reconstructions. Unlike existing approaches (e.g. universal back-projection
algorithm), Pano learns both physics and data priors while also being agnostic
to the input data resolution. Pano employs spherical discrete-continuous
convolutions to preserve hemispherical sensor geometry, incorporates Helmholtz
equation constraints to ensure physical consistency and operates
resolutionindependently across varying sensor configurations. We demonstrate
the robustness and efficiency of Pano in reconstructing high-quality images
from both simulated and real experimental data, achieving consistent
performance even with significantly reduced transducer counts and limited-angle
acquisition configurations. The framework maintains reconstruction fidelity
across diverse sparse sampling patterns while enabling real-time volumetric
imaging capabilities. This advancement establishes a practical pathway for
making 3D PACT more accessible and feasible for both preclinical research and
clinical applications, substantially reducing hardware requirements without
compromising image reconstruction quality.
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