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Three-dimensional Optical-resolution Photoacoustic Microscopy
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A photoacoustic image reconstruction method using total variation and nonconvex optimization.

Chen Zhang, Yan Zhang, Yuanyuan Wang1

  • 1Department of Electronic Engineering, Fudan University, Shanghai 200433, China. yywang@fudan.edu.cn.

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This study introduces a new algorithm for faster, higher-quality photoacoustic imaging (PAI) from sparse data. The developed method improves image reconstruction accuracy and reduces computational time, making PAI more practical.

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Area of Science:

  • Medical Imaging
  • Biomedical Engineering
  • Computational Imaging

Background:

  • Photoacoustic imaging (PAI) faces challenges with long scanning times, limiting practical applications.
  • Reconstructing PAI images from sparse-view data is a common strategy but often degrades image quality.
  • Enhancing the quality of sparse-view reconstructed PAI images is crucial for clinical utility.

Purpose of the Study:

  • To develop and validate a novel image reconstruction algorithm for PAI using sparse-view data.
  • To improve the accuracy and reduce the computational cost of PAI image reconstruction.
  • To enhance the practical applicability of PAI by addressing reconstruction quality issues.

Main Methods:

  • Proposed a joint total variation and Lp-norm (TV-Lp) based image reconstruction algorithm for PAI.
  • Employed an operator-splitting framework to decrease computational expense.
  • Utilized the Barzilai-Borwein step size selection method for accelerated convergence.

Main Results:

  • The TV-Lp algorithm demonstrated superior accuracy compared to existing PAI reconstruction methods in numerical simulations.
  • The algorithm achieved faster convergence and reduced computational cost.
  • In-vitro experiments confirmed the algorithm's effectiveness in enhancing reconstructed image quality.

Conclusions:

  • The proposed TV-Lp algorithm effectively enhances PAI image quality from sparse-view data.
  • The algorithm offers faster computation and improved convergence compared to conventional methods.
  • Validation through simulations and in-vitro experiments confirms its practical utility and robustness.