GPU implementation of photoacoustic short-lag spatial coherence imaging for improved image-guided interventions

Abstract

Significance: Photoacoustic-based visual servoing is a promising technique for surgical tool tip tracking and automated visualization of photoacoustic targets during interventional procedures. However, one outstanding challenge has been the reliability of obtaining segmentations using low-energy light sources that operate within existing laser safety limits. Aim: We developed the first known graphical processing unit (GPU)-based real-time implementation of short-lag spatial coherence (SLSC) beamforming for photoacoustic imaging and applied this real-time algorithm to improve signal segmentation during photoacoustic-based visual servoing with low-energy lasers. Approach: A 1-mm-core-diameter optical fiber was inserted into ex vivo bovine tissue. Photoacoustic-based visual servoing was implemented as the fiber was manually displaced by a translation stage, which provided ground truth measurements of the fiber displacement. GPU-SLSC results were compared with a central processing unit (CPU)-SLSC approach and an amplitude-based delay-and-sum (DAS) beamforming approach. Performance was additionally evaluated with in vivo cardiac data. Results: The GPU-SLSC implementation achieved frame rates up to 41.2 Hz, representing a factor of 348 speedup when compared with offline CPU-SLSC. In addition, GPU-SLSC successfully recovered low-energy signals (i.e., ≤268 μJ) with mean ± standard deviation of signal-to-noise ratios of 11.2 AE 2.4 (compared with 3.5 AE 0.8 with conventional DAS beamforming). When energies were lower than the safety limit for skin (i.e., 394.6 μJ for 900-nm wavelength laser light), the median and interquartile range (IQR) of visual servoing tracking errors obtained with GPU-SLSC were 0.64 and 0.52 mm, respectively (which were lower than the median and IQR obtained with DAS by 1.39 and 8.45 mm, respectively). GPU-SLSC additionally reduced the percentage of failed segmentations when applied to in vivo cardiac data. Conclusions: Results are promising for the use of low-energy, miniaturized lasers to perform GPU-SLSC photoacoustic-based visual servoing in the operating room with laser pulse repetition frequencies as high as 41.2 Hz.

Generalized spatial coherence reconstruction for photoacoustic computed tomography

“…Hence, to achieve realtime imaging using GSC, FDMAS, or SLSC, it is necessary to employ parallel beamforming using a graphical processing unit (GPU). 42 For a 512 × 512 reconstruction grid, our achieved frame rate is ∼10 Hz using Nvidia RTX2080Ti GPU. To conclude, we mathematically generalized FDMAS and SLSC beamformers into a single beamformer equation, the GSC.…”

LightSpeed: A Compact, High-Speed Optical-Link-Based 3D Optoacoustic Imager

“…Real-time preview of optoacoustic images during hand-held scans is of particular importance. In this respect, the analytical back-projection inverse algorithms [34], [41] or other coherence-based beamforming methods [42], [43], [44] are suitable for real-time image rendering, in particular when implemented in a graphics processing unit (GPU) [45], [46]. Image quality and quantification can be further improved by accounting for common experimental factors [47], [48], [49] or by employing more accurate model-based reconstruction algorithms [50], albeit at the expense of a slower computational speed.…”

Photoacoustic-Guided Laparoscopic and Open Hysterectomy Procedures Demonstrated With Human Cadavers

“…8 and 9 leverages these differences in optical absorption properties. In addition, a fasttuning laser system that is programmed to switch between two wavelengths [58], coupled with GPU-based photoacoustic SLSC imaging [59], would enable the dual-wavelength display to be created in real time and used intraoperatively for surgical guidance and automatic ROI selection for distance measurements.…”