Reconstruction algorithm for photoacoustic tomography based on the L-alternating direction method of multipliers...

Area of Science:

• Biomedical Imaging
• Medical Physics
• Image Reconstruction

Background:

• Photoacoustic tomography (PAT) is an emerging biomedical imaging modality with high contrast and resolution.
• Existing PAT reconstruction methods suffer from instability and artifacts due to improper regularization parameter settings.
• There is a need for improved algorithms to enhance PAT image quality for clinical applications.

Purpose of the Study:

• To develop a novel reconstruction algorithm for photoacoustic tomography (PAT) to overcome limitations of existing methods.
• To improve the stability and reduce artifacts in PAT image reconstruction.
• To enhance the clinical applicability of PAT through improved image quality.

Main Methods:

• A nonconvex L1-L2 norm was introduced into the variational model for PAT reconstruction.
• The L-alternating direction method of multipliers (ADMM) was employed to decompose the optimization problem into solvable subproblems.
• A preconditioned conjugate gradient (PCG) method was integrated to accelerate the solution of linear systems, enhancing computational efficiency.

Main Results:

• The proposed L-ADMM framework with adaptive weighted L1-L2 regularization achieved stable, high-quality reconstruction under sparse sampling.
• Experiments demonstrated significant improvements in peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) compared to other methods.
• The method showed high performance even with a limited number of transducers (64).

Conclusions:

• The L-ADMM-based reconstruction algorithm offers a feasible solution for high-quality PAT imaging.
• The integration of adaptive regularization and efficient optimization significantly enhances PAT image quality under sparse sampling.
• The proposed method shows strong potential for clinical translation in medical imaging.