Research On Sparse Sampling Based Photoacoustic Microscopy

In recent years,more and more clinical and industrial attentions have been paid to Photoacoustic Imaging(PAI)for its high optical contrast and large ultrasound penetration depth.It has emerged as one of the fastest growing medical imaging technology in biomedical applications.With its unique focus-imaging mode,Photoacoustic Microscopy(PAM)can achieve high-resolution imaging of a single capillary.It is suitable for research on microcirculation system deseases.However,as a scanning-imaging technology,the time-resolution of conventional PAM is not high enough.Therefore,the imaging speed is so low that it is difficult to satisfy the requirements of real-time imaging in biological research.In addition,the larger of data acquisition,the higher the system hardware requirements,which will seriously hindered the technology evolution to the clinical application.In order to solve the problems such as slow imaging speed,large amount of data and instability caused by point-to-point mechanical scanning in conventional PAM system,a non-uniform sparse sampling scheme has been proposed to improve the time-resolution of the system and realize the fast data acquisition.By analyzing the inhomogeneity of tumor and blood vessel PAM images,a more suitable non-uniform sampling scheme is proposed,which limits the scanning points to the region of interest or principal component to further reduce the total sampling points while ensuring a certain PAM image signal-to-noise ratio.Specifically,the necessity for non-uniform sampling is discussed on tumor and blood vessel PAM images and the images are classified into two types: PAM images with concentrated region of interest and non-concentrated regions of interest.A non-uniform sampling scheme based on the edge expander theory has been proposed for PAM images with concentrated region of interest.More sampling points are focused on the region of interest.Meanwhile,a non-uniform sampling scheme based on low-rank matrix approximation has been proposed for PAM images with non-concentrated region of interest.More sampling points are focused on principal component.Both two non-uniform sampling schemes can further improve the completeness of the PAM data and reduce the number of sampling points,which will accelerate the sampling process and better recover the PAM images.In order to achieve the fast and accurate recovery of PAM images,the image recovery model has been studied.Firstly,a fast image recovery algorithm based on Go Dec is designed according to low-rank matrix approximation theory.Further,the relationship between the sparse sampling mask and image recovery algorithm is analyzed based on real PAM data,and the empirical formulas are fitted to guide the parameter selection of the image recovery algorithm.Secondly,the low-rank matrix recovery has been convex relaxation as nuclear norm minimization problem,and the optimization problem is solved in the ADMM framework.The disadvantages of the image recovery algorithm with low-rank constraint,such as poor edge preservation and artifacts,are analyzed.Finally,in order to improve the precision of the low-rank recovery algorithm and recover the details of the image more accurately,a sparse constraint which is considered as a priori information of image detail and structure is combined to form a low-rank and sparse matrix recovery problem.The multi-constrained matrix recovery problem is solved with a generalized inexact ADMM method to achieve high-resolution PAM image recovery.Based on the study of the non-uniform sparse sampling scheme and the corresponding image recovery algorithms,a fast scanning photoacoustic microscopy system has been set up.Specifically,based on the analysis of conventional optical-resolution PAM,a two-dimensional optical scanner is introduced to realize the optical scanning mode and overcome the instability caused by the mechanical scanning scheme.Besides,the sparse sampling of photoacoustic data is achieved by the reasonable control of optical scanner and the synchronous control of each part of the system without increasing the system cost.With a fast data acquisition,the time resolution is improved.Finally,with the fast scanning PAM system,real PAM experiments are designed for tumor and blood vessel targets and verify the effectiveness of the sparse sampling schemes and the image recovery algorithms.The newly built fast scanning PAM system is employed to achieve the visualization of microcirculatory vascular diseases.In order to exhaustive quantify the blood vessel property of the PAM image and provide quantitative parameters which indicate blood vessel disease,a quantification and segmantation method based on multi-scale Hessian filter is proposed.In the method,a multi-scale Hessian filter based segmentation method is proposed to obtain accurate segmentation results for multi-scale features of blood vessles.On the basis of segmented images,four quantification parameters,including vessel density,vessel length fraction,vessel diameter and fractal dimension,are calculated to quantify the location and morphology information.Further,a small area quantification method has been proposed to provide quantification color maps and realize local quantification of blood vessel.Finally,the quantification method is combined with the fast scanning PAM system to achieve blood vessel imaging and quantification to further verify the effectiveness of the system.