We propose a novel method by combining the total variation (TV) with the high-degree TV (HDTV) to improve the reconstruction quality of sparse-view sampling photoacoustic imaging (PAI). A weighing function is adaptively updated in an iterative way to combine the solutions of the TV and HDTV minimizations. The fast iterative shrinkage/thresholding algorithm is implemented to solve both the TV and the HDTV minimizations with better convergence rate. Numerical results demonstrate the superiority and efficiency of the proposed method on sparse-view PAI. In vitro experiments also illustrate that the method can be used in practical sparse-view PAI.

A new photoacoustic (PA) signal sampling and image reconstruction method, called compressive sampling PA tomography (CSPAT), is recently proposed to make low sampling rate and high-resolution PA tomography possible. A key problem within the CSPAT framework is the design of optic masks. We propose to use edge expander codes-based masks instead of the conventional random distribution masks, and efficient total variation (TV) regularization-based model to formulate the associated problem. The edge expander codes-based masks, corresponding to non-uniform sampling schemes, are validated by both theoretical analysis and results from computer simulations. The proposed method is expected to enhance the capability of CSPAT for reducing the number of measurements and fast data acquisition.

Combining photoacoustic (PA) imaging with laser speckle (LS) imaging (LSI) can simultaneously determine total hemoglobin concentration (HbT), hemoglobin oxygen saturation (SO2), and blood flow rates. Thus, the co-registration of PA and LS images is important in physiological studies and pathological diagnosis. This letter presents a co-registration algorithm combining mutual information with the maximum between-class variance segmentation method (Otsu method). The mutual information and Otsu method are used to provide the registration measure criterion and image feature recognition, respectively. The evaluation results show that the registration function possesses a single maximum peak and high smoothness across the global co-registration district, indicating a robust behavior. Moreover, this method has good registration accuracy, and the fusion result simultaneously visualizes the separate functional information of two kinds of images.

The photoacoustic tomography (PAT) method, based on compressive sensing (CS) theory, requires that, for the CS reconstruction, the desired image should have a sparse representation in a known transform domain. However, the sparsity of photoacoustic signals is destroyed because noises always exist. Therefore, the original sparse signal cannot be effectively recovered using the general reconstruction algorithm. In this study, Bayesian compressive sensing (BCS) is employed to obtain highly sparse representations of photoacoustic images based on a set of noisy CS measurements. Results of simulation demonstrate that the BCS-reconstructed image can achieve superior performance than other state-of-the-art CS-reconstruction algorithms.