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

doi:10.1007/s42967-024-00468-w

Abstract

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.

Key words: Photoacoustic tomography (PAT) reconstruction, Rank minimization, Proximal alternating iterative method, Kudyka-Łojasiewicz theory

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.

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