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Learning-based sound speed estimation and aberration correction for linear-array photoacoustic imaging.

Mengjie Shi1, Tom Vercauteren1, Wenfeng Xia1

  • 1School of Biomedical Engineering and Imaging Sciences, King's College London, London, SE1 7EH, United Kingdom.

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|December 13, 2024
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Summary

This study introduces a deep learning framework to accurately estimate the speed of sound (SoS) for improved photoacoustic (PA) imaging. The method corrects aberrations, enhancing image quality in clinical settings.

Keywords:
Aberration correctionDeep learningImage reconstructionPhotoacoustic imagingSpeed of sound estimationUltrasound imaging

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Area of Science:

  • Biomedical Imaging
  • Medical Physics
  • Artificial Intelligence

Background:

  • Photoacoustic (PA) image reconstruction requires accurate speed of sound (SoS) data, often assumed homogeneous (1540 m/s) in soft tissues.
  • Heterogeneous SoS distribution causes aberration artifacts, degrading PA image quality and hindering clinical use.
  • Existing SoS correction methods often require complex hardware or lengthy algorithms, limiting clinical translation.

Purpose of the Study:

  • To develop a deep learning framework for accurate SoS estimation and aberration correction in dual-modal PA/US imaging.
  • To integrate estimated SoS distribution into PA image reconstruction for enhanced image quality.
  • To utilize a clinical ultrasound probe within the proposed framework.

Main Methods:

  • A deep neural network was employed for SoS estimation using ultrasound channel data.
  • The framework involved pre-training on digital phantoms and transfer learning with physical phantom data.
  • Co-registered PA and US images from a dual-modal system were utilized.

Main Results:

  • Achieved SoS estimation accuracy with root mean square errors of 10.2 m/s (digital) and 15.2 m/s (physical) phantoms.
  • Demonstrated significant improvement in PA image reconstruction quality, with structural similarity index measures up to 0.88 compared to 0.69.
  • Reported up to 1.2 times improvement in PA image signal-to-noise ratio in human volunteer studies.

Conclusions:

  • The proposed deep learning framework effectively estimates SoS and corrects aberrations for superior PA image reconstruction.
  • This approach offers a valuable tool for enhancing PA imaging in diverse clinical and preclinical applications.
  • The framework's reliance on clinical ultrasound probes facilitates potential clinical translation.