ORIGINAL PAPERS

Rank Minimization-Based Regularization Method for Sparse-View Photoacoustic Image Reconstruction

  • Shuo Wang ,
  • Yumei Huang
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  • School of Mathematics and Statistics, Lanzhou University, Lanzhou, 730000, Gansu, China

Received date: 2023-11-21

  Revised date: 2024-02-22

  Accepted date: 2024-04-05

  Online published: 2025-02-20

Supported by

This work is supported by the National Natural Science Foundation of China (No. 11971215), the Science and Technology Project of Gansu Province of China (No. 22JR5RA391), the Center for Data Science of Lanzhou University, China, and the Key Laboratory of Applied Mathematics and Complex Systems of Lanzhou University, China.

Abstract

The photoacoustic tomography (PAT) is a new biomedical imaging modality. It has great advantages in early diagnosis of the human disease and accurate monitoring of disease progression. In photoacoustic imaging, when a beam of short-pulsed laser illuminates the biological tissue, the photoacoustic effect leads to the emergence of acoustic waves in the tissue. The initial acoustic pressure in the tissue reveals the structures of the tissue. The purpose of the PAT reconstruction problem is to obtain the initial acoustic pressure in the tissue from the collected photoacoustic signal information. In this paper, we propose a rank minimization-based regularization model for the sparse-view photoacoustic image reconstruction problem. We design a proximal alternating iterative algorithm to solve the model and the convergence of the algorithm is demonstrated by utilizing the Kudyka-Łojasiewicz theory. The experimental results show that the proposed method is competitive with the existing state-of-the-art PAT reconstruction methods in terms of both reconstructed quantities and visual effects for the sparse-view PAT reconstruction problem.

Cite this article

Shuo Wang , Yumei Huang . Rank Minimization-Based Regularization Method for Sparse-View Photoacoustic Image Reconstruction[J]. Communications on Applied Mathematics and Computation, 2025 , 7(5) : 1861 -1879 . DOI: 10.1007/s42967-024-00468-w

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