GPU implementation of coherence-based photoacoustic beamforming for autonomous visual servoing

Abstract

Visual servoing is a promising technique for surgical tool tip tracking and automated visualization of photoacoustic targets during interventional procedures. However, visual servoing in photoacoustic imaging is challenged by the trade-off between laser safety limits and the energy needed for reliable segmentation. Short-lag spatial coherence (SLSC) imaging has the potential to overcome this challenge by improving the signal quality of images acquired with low laser energies. This study introduces the first known GPU-based real-time implementation of SLSC imaging for photoacoustic imaging and applies this real-time algorithm to enhance segmentations for visual servoing. Results with ex vivo bovine tissue demonstrate that SLSC imaging recovers signals obtained with low energy (i.e., ≤ 268 µJ) with a signal-to-noise ratio (SNR) of 11.2 ± 2.4, compared to a SNR of 3.5 ± 0.8 with conventional delay-and-sum (DAS) imaging. When energies were lower than the safety limit for skin (i.e., 400 µJ for 900 nm wavelength), real-time SLSC imaging produced lower errors during fiber tracking tasks (i.e., accuracies of 0.67 ± 0.42 mm and 4.91 ± 6.01 mm with SLSC and DAS, respectively). Similarly, for probe centering tests, the tracking error with SLSC and DAS imaging was 0.85 mm ±0.44 and 1.05 ± 0.30 mm, respectively. These results are promising for complicated visual servoing tasks in high-noise environments.