Multi-wavelength graph convolutional network for high-performance sparse multispectral optoacoustic tomography

The rapid advancement of multispectral optoacoustic tomography (MSOT) has developed for label-free biomedical imaging by providing anatomical and functional visualization through multi-wavelength laser excitation and ultrasound detection. This technique offers high spatial resolution and deep-tissue imaging capabilities for biological applications. However, the substantial hardware cost and computational demand for high-quality <i>in vivo</i> imaging hinder its extensive development. To overcome these limitations, we propose a multi-wavelength graph convolutional network for sparse MSOT. Our approach solves the ill-conditioned sparse reconstruction problem through a graph learning framework integrated with a multi-wavelength sparse sampling strategy, which can model and leverage the intrinsic correlations in artifact distributions across diverse sparse transducer configurations. Comprehensive <i>in vivo</i> mouse experiments demonstrate that the proposed method provides a flexible and practical solution for high-performance sparse MSOT imaging under sparse conditions (16 transducer elements with the reconstruction SSIM of 0.92 ± 0.01 and PSNR of 27.74 ± 1.27).