A Comparative Study of Four Total Variational Regularization Reconstruction Algorithms for Sparse-View Photoacoustic
Xueyan Liu1, Limei Zhang1, Yining Zhang1
1Department of Mathematics Science, Liaocheng University, Shandong 252000, China.
Computational and Mathematical Methods in Medicine
|October 28, 2021
Summary
This study compares compressed sensing (CS) algorithms for photoacoustic imaging (PAI) reconstruction. The TVAL3 algorithm effectively balances image quality and reconstruction speed, offering guidance for PAI development.
Area of Science:
- Biomedical Imaging
- Computational Imaging
- Signal Processing
Background:
- Photoacoustic imaging (PAI) reconstructs tissue optical absorption using nonionizing, noninvasive methods.
- Compressed sensing (CS) enables accurate PAI image reconstruction from sparse data, reducing scan times.
Purpose of the Study:
- To comparatively analyze various CS-based total variation (TV) regularization algorithms for PAI image reconstruction.
- To identify an optimal algorithm balancing reconstruction quality and computational efficiency.
Main Methods:
- Evaluation of four TV regularization algorithms: TVAL3, FPC, ISTA, and ADMM.
- Performance assessment using sparse numerical simulation and agar phantom data.
- Metrics included signal-to-noise ratio (SNR), normalized mean absolute error (NMAE), and CPU time.
Main Results:
- The TVAL3 algorithm demonstrated superior performance in balancing PAI image quality (SNR, NMAE) and reconstruction speed (CPU time).
- Comparative analysis highlighted trade-offs between different CS reconstruction algorithms.
Conclusions:
- TVAL3 is a promising algorithm for efficient and high-quality PAI image reconstruction.
- Findings provide valuable insights for developing advanced PAI sparse reconstruction algorithms.
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