Robust automated three-dimensional speed-of-sound compensation for hemispherical photoacoustic computed tomography.

Three-dimensional hemispherical photoacoustic computed tomography provides vital structural information for non-invasive preclinical research. However, speed-of-sound heterogeneity between the coupling water and biological tissue induces wavefront aberrations, resulting in tangential blurring and ghosting artifacts. Traditional compensation typically relies on simplistic ellipsoidal fitting, which fails to capture irregular tissue boundaries, or utilizes complex multimodal ultrasound hardware that significantly increases system cost.To address these limitations, we introduce an automated, low-cost signal-domain sensing framework for a 1,024-element array that achieves high-fidelity three-dimensional boundary extraction without manual intervention or auxiliary hardware. By establishing a robust Median/Median Absolute Deviation based statistical noise baseline and a channel-specific, self-adaptive strong signal threshold, the framework maps the irregular acoustic interface directly from raw data, effectively isolating biological signals from intense scattering spikes and high-amplitude optical diffuser-induced artifact arcs. This signal-centric approach establishes an equivalent dual-speed-of-sound model through temporal compression, realigning distorted wavefronts with minimal computational overhead.Validated through three-dimensional simulations, irregular phantoms, and in vivo mouse imaging, the algorithm significantly restored the morphological fidelity of hepatic microvasculature. Furthermore, stress tests confirmed exceptional resilience even when the signal-to-noise ratio was artificially degraded to 30.9 dB. This robust architecture enables high-fidelity three-dimensional imaging in acoustically challenging and high-interference biological environments.