arXiv subjects

Haohan Sun

Publications and source records attributed to Haohan Sun.

3 recordsLinked to original sources

arXiv2026

Physics-informed neural networks for quantitative assessment of cancellous bone microstructure from photoacoustic signals

Artificial intelligence (AI) empowers innovative diagnostic tools for common diseases, yet its clinical application in skeletal health evaluation is constrained by unsatisfactory accuracy, owing to the inherent porous and poroelastic biophysical features of bone. To address such bottlenecks amid global population aging, this study targets skeletal health and develops a reliable AI framework for precise bone microstructural characterization. We proposed Biot-PINN, a physics-informed neural network embedded with Biot's poroelasticity theory to characterize mechanical responses and wave propagation in poroelastic bone tissues. By decoding photoacoustic signals encoding bone mineral and microstructural features, the framework enables automatic bone microstructural grading. Experimental results reveal that Biot-PINN reaches an accuracy of 97%, markedly surpassing traditional data-driven approaches and providing a robust solution for early skeletal health diagnosis.

physics.med-ph↗

arXiv2026

A Physics-Constrained Learning Framework for Wave Propagation in Complex Poroelastic Multilayered Media

Wave propagation through complex poroelastic multilayered media is difficult to model and invert because pronounced heterogeneity, scattering, mode conversion and fluid-solid coupling jointly distort acoustic signals during propagation. Here we present Physics-Constrained Learning for Complex Multilayered Media (PCL-CMM), a general framework that integrates Biot's poroelastic theory with the elastic wave equation to bridge the gap between physically rigorous wave modelling and data-driven learning. PCL-CMM constructs a high-fidelity digital twin that dynamically computes an effective acoustic stiffness tensor for forward wave modelling and incorporates the resulting physical constraint as a loss term to regularize the training of deep neural networks. We demonstrate PCL-CMM on transcranial photoacoustic imaging, where skull-induced acoustic distortions severely degrade image formation. Across simulations and ex vivo experiments, PCL-CMM effectively compensates for these distortions and improves SSIM by more than 0.06 compared with purely data-driven neural networks. This work establishes a physics-constrained learning framework for acoustic wave modelling in complex poroelastic multilayered media.

physics.med-ph↗

arXiv2023

Theoretical and experimental study of attenuation in cancellous bone

Photoacoustic (PA) technology can provide information on both the physical structure and chemical composition of bone, showing great potential in bone assessment. However, due to the complex composition and porous structure of cancellous bone, the PA signals generated and propagated in cancellous bone are complex and difficult to be directly used in cancellous bone analysis. In this paper, a photoacoustic differential attenuation spectrum (PA-DAS) method is proposed. By eliminating the PA spectrum of the optical absorption sources, the propagation attenuation characteristics of cancellous bone are studied theoretically and experimentally. An analytical solution for the propagation attenuation of broadband ultrasound waves in cancellous bone is given by applying high-frequency and viscous corrections to Biot's theory. An experimental system of PA-DAS with an eccentric excitation differential detection system is established to obtain the PA-DAS of cancellous bone and its acoustic propagation characteristic on the rabbit osteoporosis model. The PA-DAS quantization parameter slope is further extracted to quantify the attenuation of high and low frequency components. The results show that the PA-DAS can distinguish osteoporotic bone from normal bone, enabling quantitative assessment of bone mineral density and the diagnosis of osteoporosis.

physics.med-ph↗