| Citation: |
Shuo Wang, Li Sun, Yumei Huang. A MODULUS ITERATION METHOD FOR NONNEGATIVELY CONSTRAINED PHOTOACOUSTIC IMAGE RECONSTRUCTION[J]. Journal of Applied Analysis & Computation, 2025, 15(5): 3185-3206. doi: 10.11948/20240453
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A MODULUS ITERATION METHOD FOR NONNEGATIVELY CONSTRAINED PHOTOACOUSTIC IMAGE RECONSTRUCTION
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Abstract
The photoacoustic tomography (PAT) is a new biomedical imaging modality. Its assistance in early clinical diagnosis has become more and more important in the medical field. In the PAT imaging system, when a beam of short-pulsed laser irradiates the biological tissue, the photoacoustic effect results in the generation of the acoustic waves in the tissue. The initial acoustic pressure appearing in the tissue reveals the structures of the tissue. The PAT reconstruction problem aims to obtain the initial acoustic pressure in the tissue from the collected photoacoustic signal informations. In this paper, we propose a nonnegatively constrained PAT reconstruction model regularized by a hybrid Gaussian-Laplacian mixture term. The model can be reformulated as a nonnegatively constrained quadratic programming problem with a positive definite coefficient matrix and it is shown to be equivalent to a linear complementarity problem. We apply a modulus iteration method to solve the linear complementarity problem and its convergence is also demonstrated. Numerical results illustrate that the proposed method is competitive with the existing efficient methods for the PAT reconstruction problem.
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References
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Shuo Wang, Li Sun, Yumei Huang. A MODULUS ITERATION METHOD FOR NONNEGATIVELY CONSTRAINED PHOTOACOUSTIC IMAGE RECONSTRUCTION[J]. Journal of Applied Analysis & Computation, 2025, 15(5): 3185-3206. doi: 10.11948/20240453
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Figure 1.
The "Simulation" (left), "Breast" (middle) and "Tumor" (right) phantoms.
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Figure 2.
Experimental results for "Simulation" image. From the first to the second line, reconstructed images for noise levels:
$ \sigma=10, 20 $ -
Figure 3.
Experimental results for "Breast" image. From the first to the second line, reconstructed images for noise levels:
$ \sigma=10, 20 $ -
Figure 4.
Experimental results for "Tumor" image. From the first to the second line, reconstructed images for noise levels:
$ \sigma=10, 20 $ -
Figure 5.
"Re" versus "CPU" for "Simulation", when (a)
$ \sigma=10 $ $ \sigma=20 $ -
Figure 6.
"Re" versus "CPU" for "Breast", when (a) σ = 10; (b) σ = 20.
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Figure 7.
"Re" versus "CPU" for "Tumor", when (a)
$ \sigma=10 $ $ \sigma=20 $ -
Figure 8.
"PSNR" versus "CPU" for ‘Simulation", when (a)
$ \sigma=10 $ $ \sigma=20 $ -
Figure 9.
"PSNR" versus "CPU" for "Breast", when (a) σ = 10; (b) σ = 20.
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Figure 10.
"PSNR" versus "CPU" for "Tumor", when (a)
$ \sigma=10 $ $ \sigma=20 $