Abstract：
BACKGROUND: Subjective surgeon interpretation of near-infrared perfusion video is limited by low inter-observer agreement and poor correlation to clinical outcomes. In contrast, quantification of indocyanine green fluorescence video (Q-ICG) correlates with histologic level of perfusion as well as clinical outcomes. Measuring dye volume over time, however, has limitations, such as it is not on-demand, has poor spatial resolution, and is not easily repeatable. Laser speckle contrast imaging quantification (Q-LSCI) is a real-time, dye-free alternative, but further validation is needed. We hypothesize that Q-LSCI will distinguish ischemic tissue and correlate over a range of perfusion levels equivalent to Q-ICG.
METHODS: Nine sections of intestine in three swine were devascularized. Pairs of indocyanine green fluorescence imaging and laser speckle contrast imaging video were quantified within perfused, watershed, and ischemic regions. Q-ICG used normalized peak inflow slope. Q-LSCI methods were laser speckle perfusion units (LSPU), the base unit of laser speckle imaging, relative perfusion units (RPU), a previously described methodology which utilizes an internal control, and zero-lag normalized cross-correlation (X-Corr), to investigate if the signal deviations convey accurate perfusion information. We determine the ability to distinguish ischemic regions and correlation to Q-ICG over a perfusion gradient.
RESULTS: All modalities distinguished ischemic from perfused regions of interest; Q-ICG values of 0.028 and 0.155 (p < 0.001); RPU values of 0.15 and 0.68 (p < 0.001); and X-corr values of 0.73 and 0.24 (p < 0.001). Over a range of perfusion levels, RPU had the best correlation with Q-ICG (r = 0.79, p < 0.001) compared with LSPU (r = 0.74, p < 0.001) and X-Corr (r = 0.46, p < 0.001).
CONCLUSIONS: These results demonstrate that Q-LSCI discriminates ischemic from perfused tissue and represents similar perfusion information over a broad range of perfusion levels comparable to clinically validated Q-ICG. This suggests that Q-LSCI might offer clinically predictive real-time dye-free quantification of tissue perfusion. Further work should include validation in histologic studies and human clinical trials.
METHODS: Nine sections of intestine in three swine were devascularized. Pairs of indocyanine green fluorescence imaging and laser speckle contrast imaging video were quantified within perfused, watershed, and ischemic regions. Q-ICG used normalized peak inflow slope. Q-LSCI methods were laser speckle perfusion units (LSPU), the base unit of laser speckle imaging, relative perfusion units (RPU), a previously described methodology which utilizes an internal control, and zero-lag normalized cross-correlation (X-Corr), to investigate if the signal deviations convey accurate perfusion information. We determine the ability to distinguish ischemic regions and correlation to Q-ICG over a perfusion gradient.
RESULTS: All modalities distinguished ischemic from perfused regions of interest; Q-ICG values of 0.028 and 0.155 (p < 0.001); RPU values of 0.15 and 0.68 (p < 0.001); and X-corr values of 0.73 and 0.24 (p < 0.001). Over a range of perfusion levels, RPU had the best correlation with Q-ICG (r = 0.79, p < 0.001) compared with LSPU (r = 0.74, p < 0.001) and X-Corr (r = 0.46, p < 0.001).
CONCLUSIONS: These results demonstrate that Q-LSCI discriminates ischemic from perfused tissue and represents similar perfusion information over a broad range of perfusion levels comparable to clinically validated Q-ICG. This suggests that Q-LSCI might offer clinically predictive real-time dye-free quantification of tissue perfusion. Further work should include validation in histologic studies and human clinical trials.
摘要：
背景：外科医生对近红外灌注视频的主观解释受到观察者间一致性低和与临床结果相关性差的限制。相比之下,吲哚菁绿荧光视频(Q-ICG)的定量与组织学灌注水平以及临床结果相关.随时间测量染料体积，然而,有局限性,比如它不是按需的，空间分辨率差，并且不容易重复。激光散斑对比成像量化(Q-LSCI)是一种实时、无染料替代品，但需要进一步验证。我们假设Q-LSCI将区分缺血组织，并在相当于Q-ICG的灌注水平范围内进行关联。
方法：对三只猪的9个肠道进行断流处理。对吲哚菁绿荧光成像和激光散斑对比成像视频进行了量化，分水岭,和缺血区域。Q-ICG使用归一化的峰值流入斜率。Q-LSCI方法是激光散斑灌注单位(LSPU)，激光散斑成像的基本单元，相对灌注单位（RPU），先前描述的利用内部控制的方法，和零滞后归一化互相关(X-Corr)，以调查信号偏差是否传达准确的灌注信息。我们确定在灌注梯度上区分缺血区域和与Q-ICG的相关性的能力。
结果：所有模式区分缺血和灌注感兴趣区域；Q-ICG值为0.028和0.155（p<0.001）；RPU值为0.15和0.68（p<0.001）；X-corr值为0.73和0.24（p<0.001）。在一系列灌注水平上，与LSPU（r=0.74，p<0.001）和X-Corr（r=0.46，p<0.001）相比，RPU与Q-ICG（r=0.79，p<0.001）的相关性最好。
结论：这些结果表明，Q-LSCI可区分缺血与灌注组织，并在与临床验证的Q-ICG相当的广泛灌注水平上表现出相似的灌注信息。这表明Q-LSCI可以提供临床预测的组织灌注的实时无染料定量。进一步的工作应包括组织学研究和人体临床试验的验证。
方法：对三只猪的9个肠道进行断流处理。对吲哚菁绿荧光成像和激光散斑对比成像视频进行了量化，分水岭,和缺血区域。Q-ICG使用归一化的峰值流入斜率。Q-LSCI方法是激光散斑灌注单位(LSPU)，激光散斑成像的基本单元，相对灌注单位（RPU），先前描述的利用内部控制的方法，和零滞后归一化互相关(X-Corr)，以调查信号偏差是否传达准确的灌注信息。我们确定在灌注梯度上区分缺血区域和与Q-ICG的相关性的能力。
结果：所有模式区分缺血和灌注感兴趣区域；Q-ICG值为0.028和0.155（p<0.001）；RPU值为0.15和0.68（p<0.001）；X-corr值为0.73和0.24（p<0.001）。在一系列灌注水平上，与LSPU（r=0.74，p<0.001）和X-Corr（r=0.46，p<0.001）相比，RPU与Q-ICG（r=0.79，p<0.001）的相关性最好。
结论：这些结果表明，Q-LSCI可区分缺血与灌注组织，并在与临床验证的Q-ICG相当的广泛灌注水平上表现出相似的灌注信息。这表明Q-LSCI可以提供临床预测的组织灌注的实时无染料定量。进一步的工作应包括组织学研究和人体临床试验的验证。
