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Multispectral interlaced sparse sampling photoacoustic tomography based on directional total variation.

Xipan Li1, Jia Ge2, Shuangyang Zhang2

  • 1School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, Guangdong, 510515, China; Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, Henan, 450008, China.

Computer Methods and Programs in Biomedicine
|December 15, 2021
PubMed
Summary

A new directional total variation (dTV) regularization scheme significantly speeds up multispectral Interlaced Sparse Sampling Photoacoustic Tomography (ISS-PAT) image reconstruction. This method achieves high image quality with a 16-fold efficiency improvement, enabling practical applications.

Keywords:
Directional total variationIterative reconstructionPhotoacoustic tomographySpectral un-mixing

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Area of Science:

  • Biomedical Imaging
  • Optical Imaging
  • Photoacoustic Imaging

Background:

  • Photoacoustic Tomography (PAT) provides cross-sectional images of biological tissues based on optical absorption.
  • Multispectral Interlaced Sparse Sampling PAT (ISS-PAT) offers high-quality imaging with sparse transducer coverage but faces computational challenges.
  • Existing ISS-PAT methods have a heavy computation burden, limiting their practical use.

Purpose of the Study:

  • To develop a computationally efficient regularization scheme for ISS-PAT.
  • To improve the practical applicability of ISS-PAT by reducing reconstruction time.
  • To leverage structural information for enhanced PAT image reconstruction.

Main Methods:

  • Proposed a novel directional total variation (dTV) regularization scheme for ISS-PAT.
  • Incorporated anatomical prior image edge position and direction information without requiring segmentation.
  • Utilized a modified alternating direction of multipliers (ADMM) algorithm for efficient solution.

Main Results:

  • Evaluated the dTV scheme using simulations, phantoms, and in vivo small animal experiments.
  • Achieved image quality and spectral unmixing accuracy comparable to non-local means based ISS-PAT.
  • Demonstrated a significant reduction in image reconstruction time, with nearly 16-fold efficiency improvement at a 1/6 sparse sampling rate.

Conclusions:

  • The dTV regularization scheme is feasible and effective for ISS-PAT.
  • The efficient image reconstruction accelerates the potential for hardware realization and practical applications of ISS-PAT.
  • This advancement facilitates wider adoption of ISS-PAT in biomedical research and clinical settings.