Enhancing sparse-view photoacoustic tomography with combined virtually parallel projecting and spatially adaptive filtering.

Yihan Wang,Jiao Li,Feng Gao,Limin Zhang,Jingying Jiang,Wenjuan Ma,Wenbo Wan,Huijuan Zhao,Zhongxing Zhou,Tong Lu

doi:10.1364/boe.9.004569

Abstract

To fully realize the potential of photoacoustic tomography (PAT) in preclinical and clinical applications, rapid measurements and robust reconstructions are needed. Sparse-view measurements have been adopted effectively to accelerate the data acquisition. However, since the reconstruction from the sparse-view sampling data is challenging, both the effective measurement and the appropriate reconstruction should be taken into account. In this study, we present an iterative sparse-view PAT reconstruction scheme, where a concept of virtual parallel-projection matching the measurement condition is introduced to aid the “compressive sensing” in the reconstruction procedure, and meanwhile, the non-local spatially adaptive filtering exploring the a priori information of the mutual similarities in natural images is adopted to recover the unknowns in the transformed sparse domain. Consequently, the reconstructed images with the proposed sparse-view scheme can be evidently improved in comparison to those with the universal back-projection method, for the cases of same sparse views. The proposed approach has been validated by the simulations and ex vivo experiments, which exhibits desirable performances in image fidelity even from a small number of measuring positions.

Highlights

Photoacoustic tomography (PAT) is emerging as a powerful technique for providing deeptissue structural, functional and molecular information in both small animal [1,2,3] and human imaging studies [4,5,6]
For the inverse radon transform (IRT)-Block-matching and 3D filtering (BM3D) approach, first, we have proposed the “virtual parallel-projection” measurement condition
Similar to the parallelbeam Computed Tomography (CT) reconstruction, following the central-slice theorem, the measured signals can be transformed to the 2D Fourier domain where the P0 image can be sparsely represented, and the transformed signals can be directly acted as the partial Fourier spectrum of the P0 image

Summary

Introduction

Photoacoustic tomography (PAT) is emerging as a powerful technique for providing deeptissue structural, functional and molecular information in both small animal [1,2,3] and human imaging studies [4,5,6]. Using one or several single-element transducers for circularly scanning the imaging object is the usual choice in many experimental setups in view of its comparative cost-effectiveness and high measurement flexibility [7, 8] These schemes normally require hundreds of scanning steps to acquire full-view PAT projections and usually take several minutes. Meng et al have proposed a reconstruction framework of “compressed sensing with partially known support”, which uses a small part of the known nonzero-signals’ locations in the transformed sparse domain as a priori information [20] She has developed a principal component-analysis-based PAT to reduce the scale of the reconstruction [21]. The proposed approach has been validated by the simulation and ex vivo experiments, exhibiting promising performances in imaging fidelity even from a small number of measuring positions

Virtual parallel-projection

Iterative reconstruction process

Vascular imaging for mouse intestinal tissue