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GPU implementation of photoacoustic short-lag spatial coherence imaging for improved image-guided interventions.
Journal of Biomedical Optics ( IF 3.5 ) Pub Date : 2020-07-01 , DOI: 10.1117/1.jbo.25.7.077002
Eduardo A Gonzalez 1 , Muyinatu A Lediju Bell 1, 2, 3
Affiliation  

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.

中文翻译:

光声短延迟空间相干成像的 GPU 实现,以改进图像引导干预。

意义基于光声的视觉伺服是一种很有前途的技术,用于在介入手术过程中进行手术工具尖端跟踪和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运行的低能量光源获得分割的可靠性。目标我们开发了第一个已知的基于图形处理单元 (GPU) 的用于光声成像的短延迟空间相干 (SLSC) 波束成形的实时实现,并应用这种实时算法来改善基于光声的视觉伺服过程中的信号分割-能量激光器。方法 将一根 1 毫米芯直径的光纤插入离体牛组织中。当光纤通过平移台手动位移时,实现了基于光声的视觉伺服,这提供了光纤位移的地面实况测量。GPU-SLSC 结果与中央处理单元 (CPU)-SLSC 方法和基于幅度的延迟求和 (DAS) 波束成形方法进行了比较。此外,还使用体内心脏数据评估了性能。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 成功恢复了低能量信号(即≤268 μJ),信噪比的平均值±标准偏差为 11.2 ± 2.4(相比之下,传统 DAS 波束成形为 3.5 ± 0.8)。当能量低于皮肤的安全限值(即 394. 6 μJ(对于 900 nm 波长激光),GPU-SLSC 获得的视觉伺服跟踪误差的中值和四分位距(IQR)分别为 0.64 和 0.52 mm(比 DAS 获得的中值和 IQR 低 1.39和 8.45 毫米)。当应用于体内心脏数据时,GPU-SLSC 还降低了失败分割的百分比。结论 使用低能量、小型化激光器在手术室中执行基于 GPU-SLSC 光声的视觉伺服,激光脉冲重复频率高达 41.2 Hz,结果很有希望。当应用于体内心脏数据时,GPU-SLSC 还降低了失败分割的百分比。结论 使用低能量、小型化激光器在手术室中执行基于 GPU-SLSC 光声的视觉伺服,激光脉冲重复频率高达 41.2 Hz,结果很有希望。当应用于体内心脏数据时,GPU-SLSC 还降低了失败分割的百分比。结论 使用低能量、小型化激光器在手术室中执行基于 GPU-SLSC 光声的视觉伺服,激光脉冲重复频率高达 41.2 Hz,结果很有希望。
更新日期:2020-07-01
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