Novel deep learning architecture for optical fluence dependent photoacoustic target localization

Photoacoustic imaging shows great promise for clinical environments where real-time position feedback is critical, including the guiding of minimally invasive surgery, drug delivery, stem cell transplantation, and the placement of metal implants such as stents, needles, staples, and brachytherapy seeds. Photoacoustic imaging techniques generate high contrast, label-free images of human vasculature, leveraging the high optical absorption characteristics of hemoglobin to generate measurable longitudinal pressure waves. However, the depth-dependent decrease in optical fluence and lateral resolution affects the visibility of deeper vessels or other absorbing targets. This poses a problem when the precise locations of vessels are critical for the application at hand, such as navigational tasks during minimally invasive surgery. To address this issue, a novel deep neural network was designed, developed, and trained to predict the location of circular chromophore targets in tissue mimicking a strong scattering background, given measurements of photoacoustic signals from a linear array of ultrasound elements. The network was trained on 16,240 samples of simulated sensor data and tested on a separate set of 4,060 samples. Both our training and test sets consisted of optical fluence-dependent photoacoustic signal measurements from point sources at varying locations. Our network was able to predict the location of point sources with a mean axial error of 4.3 mu m and a mean lateral error of 5.8 mu m.