  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 the generation of semi-synthetic
training datasets combining both simulated data to represent features from the
needles and in vivo measurements for tissue background. Evaluation of the
trained neural network was performed with needle insertions into
blood-vessel-mimicking phantoms, pork joint tissue ex vivo and measurements on
human volunteers. This deep learning-based framework substantially improved the
needle visibility in photoacoustic imaging in vivo compared to conventional
reconstruction by suppressing background noise and image artefacts, achieving
5.8 and 4.5 times improvements in signal-to-noise ratio (SNR) and the modified
Hausdorff distance (MHD) respectively. Thus, the proposed framework could be
helpful for reducing complications during percutaneous needle insertions by
accurate identification of clinical needles in photoacoustic imaging.
