Ultrasound (US) imaging is commonly used to guide minimally invasive surgeries but has poor contrast of the invasive devices such as clinical needles. Photoacoustic (PA) imaging promises to be efficient for visualising needles. Elastomeric coatings can also be applied on the needle surface to improve its visibility, however, strong signals generated from the highly absorbing coatings sometimes introduce image artefacts which affect needle identification. In this work, we developed a deep learning-based method to enhance the needle visualisation by removing the artefacts. We anticipated that the proposed methods could be useful for guiding percutaneous needle insertions.
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