Improving needle visibility in LED-based photoacoustic imaging using deep learning with semi-synthetic datasets.

ABSTRACT: Photoacoustic imaging has shown great potential for guiding minimally invasive procedures by accurate identification of critical tissue targets and invasive medical devices (such as metallic needles). The use of light emitting diodes (LEDs) as the excitation light sources accelerates its clinical translation owing to its high affordability and portability. However, needle visibility in LED-based photoacoustic imaging is compromised primarily due to its low optical fluence. In this work, we propose a deep learning framework based on U-Net to improve the visibility of clinical metallic needles with a LED-based photoacoustic and ultrasound imaging system. To address the complexity of capturing ground truth for real data and the poor realism of purely simulated data, this framework included th

SUBMITTER: Shi M 

PROVIDER: S-EPMC9048160 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature