Clinical Evaluation of Burns using Spatial Frequency Domain Imaging

项目主任/首席调查员(最后、第一、中间)：Durkin，Anthony J. 摘要 这项为期3年的竞争性R01更新的中心目标是描述和应用一种新的、紧凑的临床- 友好的空间频域成像(SFDI)设备对烧伤进行客观、非侵入性分类 大面积皮肤的严重程度(烧伤等级)。延迟确定烧伤严重程度直接影响患者 治疗计划(包括是否移植的决定)、感染率和疤痕发生率、住院时间 以及最终的医疗费用。目前，确定烧伤严重程度的主要方法仍然是临床。 评估，这是高度主观的。而浅层和全层烧伤都是典型的 根据视觉临床印象很容易诊断，部分深度烧伤很难分类和携带 他们有相当大的并发症的可能性。烧伤严重程度分类的准确性，即使是专家也是如此 只有60%-80%。我们在动物模型中的研究表明，SFDI数据可以成功地用于分类 烧伤严重程度的不同区域。通常情况下，这些差异对于肉眼和 临床医生需要大量的培训和经验才能准确地区分我们的工作 使用研究级别的混合SFDI设备表明，可以使用SFDI提供的客观参数 在伤后24小时内，准确划分烧伤严重程度。具体地说，我们在 猪烧伤模型，研究级SFDI在24小时内表现优于激光散斑成像和热成像 烧伤后数小时，用来预测烧伤是否需要移植。然而，将这些翻译为 结果由于几种设备的限制，临床应用一直很困难。研究级SFDI装置具有 获取时间较慢，可能会导致运动伪影。它对环境光也很敏感，环境光通常是 在临床环境中的问题。此外，SFDI设备生成如此多的不同数据(含氧和 脱氧血红蛋白、水含量、多波长的折合散射系数)，没有 将其呈现给临床用户以快速做出决定的明显方式。为此，我们建议有条不紊地 研究一种改进的下一代SFDI设备，该设备通过使用更亮的LED和 以减少运动伪影且独立于临床的方式快速收集数据的波长更少 照明条件。此外，我们将开发一个基于机器学习的分类框架，它将 为临床提供可操作的诊断信息。本届R01大赛的核心目标是 更新是表征并修改一种新的临床友好型SFDI设备(CLIAFI)，以客观地分类在- 大面积不同烧伤严重程度的活体区域。拟议的研究试图通过 以下具体目标：1)测试和验证临床SFDI工具；2)比较临床SFDI工具与 关于猪长期分级烧伤模型的其他模式，3)发展空间分辨分类 基于SFDI数据的烧伤严重程度地图，4)使用新的 SFDI设备和基于SFDI数据的空间分辨烧伤严重程度分类图。

Program Director/Principal Investigator (Last, First, Middle): Durkin, Anthony J. Abstract The central aim of this 3 year competing R01 renewal is to characterize and apply a new, compact, clinic- friendly Spatial Frequency Domain Imaging (SFDI) device to objectively and non-invasively classify burn severity (burn grade) over a large areas of skin. Delays in determining burn severity directly impacts patient treatment plans (including decisions whether to graft), rates of infection and scarring, duration of hospitalization and ultimately cost of care. Currently, the primary method of determining burn severity continues to be clinical assessment, which is highly subjective. While both superficial thickness and full-thickness burns are typically readily diagnosed based on visual clinical impression, partial thickness burns are difficult to classify and carry with them considerable potential for complications. Burn severity classification accuracy, even by experts, is only 60–80%. Our research in animal models demonstrates that SFDI data can successfully be used to classify different regions of burn severities. Typically, these differences are not apparent to the unaided eye and a great deal of training and experience is required in order for clinicians to accurately differentiate them Our work using a research grade, hybrid-SFDI device suggests that objective parameters provided by SFDI can be used within 24 hours after injury, to accurately classify burn severity. Specifically, we have demonstrated in a porcine burn model that the research grade SFDI outperforms laser speckle imaging and thermal imaging at 24 hours post-burn, in terms of predicting whether a burn will require a graft or not. However, translating these results to the clinic has been difficult due to several device limitations. The research grade SFDI device has slow acquisition times that can result in motion artifacts. It is also sensitive to ambient light which is often an issue in a clinical setting. Additionally, the SFDI device generates so much diverse data (oxygenated and deoxygenated hemoglobin, water fraction, reduced scattering coefficients at multiple wavelengths), there is no obvious way to present it to a clinical user to make a quick decision. To this end, we propose to methodically investigate an improved next generation SFDI device that addresses these issues by using brighter LEDs and fewer wavelengths to rapidly collect data in a way that reduces motion artifacts and is independent of clinical lighting conditions. In addition, we will develop a machine learning based classification framework that will provide the clinical with actionalble diagnostic information. The central aim of this 3 year competing R01 renewal is to characterize and then modify a new clinic-friendly SFDI device (Clarifi) to objectively classify in- vivo regions of different burn severity over large areas. The proposed research seeks to investigate this via the following Specific Aims: 1) Test & Validate Clinical SFDI Instrument, 2) Compare Clinical SFDI Instrument to other Modalities on a Long Term Swine Model of Graded Burns, 3) Develop Spatially Resolved Classification Maps of Burn Severity based on SFDI Data, 4) Conduct Clinical Measurements of Burn Severity using the new SFDI device and Spatially Resolved Burn Severity Classification Maps based on SFDI data.