GPU implementation of photoacoustic short-lag spatial coherence imaging for improved image-guided interventions
GPU implementation of photoacoustic short-lag spatial coherence imaging for improved image-guided interventions
复制标题
DOI：
10.1117/1.jbo.25.7.077002
复制
发表时间：
2020-07
影响因子：
3.5
通讯作者：
Eduardo A. Gonzalez;M. Bell
中科院分区：
文献类型：
--
作者：
Eduardo A. Gonzalez;M. Bell
抽象的。重要性：基于光声的视觉伺服是一种很有前途的技术，用于手术工具尖端跟踪和介入手术过程中光声目标的自动可视化。然而，一个突出的挑战是使用在现有激光安全限制内操作的低能量光源获得分割的可靠性。目的：我们开发了第一个已知的基于图形处理单元（GPU）的实时实现的短滞后空间相干（SLSC）波束形成的光声成像和应用这种实时算法，以改善信号分割在基于光声的视觉伺服与低能量激光。方法：将1 mm芯直径的光纤插入离体牛组织中。基于光声的视觉伺服被实施为通过平移台手动移位光纤，其提供光纤位移的地面实况测量。将GPU-SLSC结果与中央处理器（CPU）-SLSC方法和基于幅度的延迟求和（DAS）波束形成方法进行了比较。此外，还使用体内心脏数据对性能进行了评价。结果：GPU-SLSC实现的帧速率高达41.2 Hz，与离线CPU-SLSC相比，加速了348倍。此外，GPU-SLSC成功地恢复了低能量信号（即，≤268 μJ），信噪比的平均值±标准差为11.2 ± 2.4（传统DAS波束成形为3.5 ± 0.8）。当能量低于皮肤的安全极限时（即，394.6 μJ（900 nm波长激光），使用GPU-SLSC获得的视觉伺服跟踪误差的中位数和四分位距（IQR）分别为0.64和0.52 mm（分别比DAS获得的中位数和IQR低1.39和8.45 mm）。当应用于体内心脏数据时，GPU-SLSC还降低了分割失败的百分比。结论：结果是有希望的低能量，小型化的激光器，在手术室中进行基于GPU-SLSC光声的视觉伺服与激光脉冲重复频率高达41.2 Hz。
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 ± 2.4 (compared with 3.5 ± 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.