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Extractor-attention-predictor network for quantitative photoacoustic tomography.

Zeqi Wang1, Wei Tao1, Hui Zhao1

  • 1School of Sensing Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.

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Summary

We developed the extractor-attention-predictor network (EAPNet) to improve quantitative photoacoustic tomography (qPAT) for accurate chromophore concentration estimation. EAPNet enhances performance and robustness in challenging imaging scenarios.

Keywords:
Absorption coefficient estimationDeep learningImage reconstructionOptical inverse problemQuantitative photoacoustic tomography

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Area of Science:

  • Biomedical Optics
  • Medical Imaging
  • Computational Imaging

Background:

  • Quantitative photoacoustic tomography (qPAT) is crucial for estimating chromophore concentrations.
  • The optical inverse problem in qPAT, recovering absorption coefficients, presents significant challenges.
  • Existing methods struggle with accuracy and robustness in diverse imaging conditions.

Purpose of the Study:

  • To introduce a novel deep learning architecture, EAPNet, for improved qPAT.
  • To enhance the accuracy and reliability of absorption coefficient recovery in qPAT.
  • To develop a robust method applicable to various target properties and imaging conditions.

Main Methods:

  • Proposed an extractor-attention-predictor network architecture (EAPNet) with a contracting-expanding structure.
  • Incorporated a multilayer perceptron for enhanced nonlinear modeling and a spatial attention module.
  • Utilized a balanced loss function to mitigate regional biases during training.

Main Results:

  • EAPNet achieved satisfactory quantitative metrics in both simulated and real-world validations.
  • Demonstrated superior robustness to target properties like size, depth, and absorption intensity.
  • Outperformed the conventional UNet in efficiency and performance with similar complexity.

Conclusions:

  • EAPNet offers a significant advancement in solving the optical inverse problem for qPAT.
  • The proposed method shows broader applicability and reliable performance for challenging targets.
  • EAPNet provides an efficient and effective solution for quantitative absorption coefficient mapping in photoacoustic imaging.