Shi, Mengjie;
Zhao, Tianrui;
West, Simeon J;
Desjardins, Adrien E;
Vercauteren, Tom;
Xia, Wenfeng;
(2022)
Improving needle visibility in LED-based photoacoustic imaging using deep learning with semi-synthetic datasets.
Photoacoustics
, 26
, Article 100351. 10.1016/j.pacs.2022.100351.
Preview |
Text
Desjardins_Improving needle visibility in LED-based photoacoustic imaging using deep learning with semi-synthetic datasets_VoR.pdf - Published Version Download (2MB) | Preview |
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 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 terms of signal-to-noise ratio and the modified Hausdorff distance, respectively. Thus, the proposed framework could be helpful for reducing complications during percutaneous needle insertions by accurate identification of clinical needles in photoacoustic imaging.
| Type: | Article |
|---|---|
| Title: | Improving needle visibility in LED-based photoacoustic imaging using deep learning with semi-synthetic datasets |
| Location: | Germany |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.pacs.2022.100351 |
| Publisher version: | https://doi.org/10.1016/j.pacs.2022.100351 |
| Language: | English |
| Additional information: | © 2022 The Authors. Published by Elsevier GmbH under a Creative Commons license (https://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Photoacoustic imaging, Needle visibility, Light emitting diodes, Deep learning, Minimally invasive procedures |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10157611 |
Archive Staff Only
 |
View Item |
