Graphics processing unit accelerating compressed sensing photoacoustic computed tomography with total variation.

Photoacoustic computed tomography with compressed sensing (CS-PACT) is a commonly used imaging strategy for sparse-sampling PACT. However, it is very time-consuming because of the iterative process involved in the image reconstruction. In this paper, we present a graphics processing unit (GPU)-based parallel computation framework for total-variation-based CS-PACT and adapted into a custom-made PACT system. Specifically, five compute-intensive operators are extracted from the iteration algorithm and are redesigned for parallel performance on a GPU. We achieved an image reconstruction speed 24-31 times faster than the CPU performance. We performed in vivo experiments on human hands to verify the feasibility of our developed method.

Quantitative analysis on in vivo tumor-microvascular images from optical-resolution photoacoustic microscopy.

Optical-resolution photoacoustic microscopy (OR-PAM) has been shown to be an excellent imaging modality for monitoring and study of tumor microvasculature. However, previous studies focused mainly on the normal tissues and did not quantify the tumor microvasculature. In this study, we present an in vivo OR-PAM imaging of the melanomas and hepatoma implanted in the mouse ear. We quantify the vessel growth by extracting the skeletons of both dense and thin branches of the tumor microvasculature obtained by Hessian matrix enhancement followed by improved two-step multistencils fast marching method. Compared with the previous methods of using OR-PAM for normal tissues, our method was more effective in extracting the binary vascular network in the tumor images and in obtaining the complete and continuous microvascular skeleton maps. Our demonstration of using OR-PAM in improving microvasculature of tumors and quantification of tumor growth would push deep this technology for the early diagnosis and treatment of cancers.