Multispectral interlaced sparse sampling photoacoustic tomography based on directional total variation

• Published in: Computer methods and programs in biomedicine 2022-02, Vol.214, p.106562-106562, Article 106562
• Main Authors: Li, Xipan, Ge, Jia, Zhang, Shuangyang, Wu, Jian, Qi, Li, Chen, Wufan
• Format: Article
• Language: English
• Subjects: Algorithms; Animals; Directional total variation; Image Processing, Computer-Assisted; Iterative reconstruction; Phantoms, Imaging; Photoacoustic Techniques; Photoacoustic tomography; Spectral un-mixing; Tomography; Tomography, X-Ray Computed
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2021.106562

•An efficient imaging model is proposed for multispectral interlaced sparse sampling PAT.•The method is based on directional total variation (dTV) that introduces structural information from the anatomical prior image.•Multiple experiments show that the dTV method is able to improve image reconstruction efficiency significantly.•The proposed method may facilitate the future development of the ISS-PAT technology. Photoacoustic tomography (PAT) is capable of obtaining cross-sectional images of small animals that represent the optical absorption of biological tissues. The multispectral Interlaced Sparse Sampling PAT, or ISS-PAT, is a previously proposed PAT imaging method that offered high quality images with much sparser transducer angular coverage. Although it provides superior imaging performance, the original ISS-PAT method suffered from a heavy computation burden, which hinders its practical application. Here, we propose a new regularization scheme based on the directional total variation (dTV) for ISS-PAT. This method efficiently imposes the structural information by considering both the edge position and direction information of the anatomical prior image in ISS-PAT. It does not require image segmentation, and can be conveniently solved by a modified alternating direction of multipliers (ADMM) algorithm. We perform simulation, tissue mimicking phantom and in vivo small animal experiments to evaluate the proposed scheme. The reconstructed PAT images showed image quality and spectral un-mixing accuracy close to those obtained by non-local means based ISS-PAT, but with much shorter image reconstruction time. For a 1/6 sparse sampling rate, the average efficiency improvement is nearly 16-folds. The experimental results demonstrate the feasibility of the dTV regularization scheme for ISS-PAT. Its efficient image reconstruction performance facilitates the potential of the hardware realization and practical applications of the ISS-PAT.