Label-free measurement of blood lipids with hyperspectral short-wave infrared spatial frequency domain imaging to improve cardiovascular disease risk prediction and treatment monitoring

利用高光谱短波红外空间频域成像对血脂进行无标记测量，以改善心血管疾病风险预测和治疗监测

• 批准号: 10042318
• 负责人: Darren Michael Roblyer
• 金额: $ 26.67万
• 依托单位: BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10042318
• 关键词: 3D Print; Address; Affect; Algorithms; Area; Benchmarking; Biological; Biological Markers; Blood; Blood Pressure; Blood Tests; Blood Vessels; Cardiovascular Diseases; Cessation of life; Cholesterol; Classification; Clinical; Clinical Research; Collagen; Custom; Data; Development; Devices; Economics; Event; Fatty acid glycerol esters; Feasibility Studies; Feedback; Funding; Future; Geography; Goals; Gold; Guidelines; High Density Lipoproteins; Home environment; Hydration status; Image; Imaging Techniques; Individual; Infrastructure; Intervention; LDL Cholesterol Lipoproteins; Label; Laboratories; Lasers; Lead; Light; Lighting; Lipids; Lipoproteins; Liquid substance; Low-Density Lipoproteins; Measurement; Measures; Medical; Menstrual cycle; Methodology; Methods; Modeling; Monitor; Near-infrared optical imaging; Optics; Pain; Patient Monitoring; Patient-Focused Outcomes; Patients; Pattern; Performance; Pharmaceutical Preparations; Pharmacologic Substance; Property; Reagent; Research; Resolution; Resources; Risk; Risk stratification; Scanning; Short Waves; Skin; Sleep Apnea Syndromes; Spatial Frequency Domain Imaging; Speed; System; Technology; Test Result; Testing; Thick; Time; Tissue Extracts; Triglycerides; Variant; Water; Work; base; blood lipid; cardiovascular disorder risk; cardiovascular risk factor; circadian; classification algorithm; clinical translation; cost; diet and exercise; digital; economic cost; equipment training; follow-up; heart rhythm; improved; improved outcome; in vivo; innovation; instrumentation; laboratory equipment; negative affect; patient stratification; portability; research clinical testing; screening; simulation; technology development; tool; volunteer

ABSTRACT: Cardiovascular disease (CVD) reportedly causes 31% of global deaths with overwhelming personal and economic costs, the latter estimated as $400 billion annually in the US. Lack of regular screening contributes to severe undertreatment for CVD, with some estimates finding only 1/3 of eligible patients taking preventative medications. One of the most important biomarkers for CVD, elevated blood lipids, requires invasive blood draws followed by lab-based testing. These requirements limit access to screening for many at-risk patients in low resource settings, and make frequent longitudinal monitoring impractical for everyone. Worse still, recent data shows that the temporal dynamics of blood lipids, including postprandial increases, circadian, ultradian (<24 hr cycles), and fluctuations during menstrual cycle, all negatively affect the accuracy of blood lipid test results. Non- invasive methods that more immediately and continuously track blood-lipids would transform CVD monitoring for those at risk. The goal of this project is to develop the first non-invasive optical technology for measuring blood lipids. To accomplish this goal, we will develop a new imaging technique called Short-Wave Infrared Spatial Frequency Domain Imaging (SWIR-SFDI). SWIR-SFDI leverages spectroscopic SWIR patterned illumination combined with model-based analysis to extract tissue optical properties as well as lipid and water concentrations. Compared to both visible (VIS) and near infrared (NIR) imaging, the SWIR wavelength band potentially provides better quantification of lipids and deeper imaging. In this project, we propose to develop SWIR-SFDI instrumentation and processing methodology to demonstrate that triglycerides (TG), cholesterol, LDL-C, and HDL-C, all of which are strong predictors of CVD risk, can be tracked longitudinally with high accuracy. We will fabricate a mobile hyperspectral SWIR-SFDI system with an innovative dual digital micromirror device (DMD) configuration for rapid wavelength tuning and spatial light patterning between 700 – 1600 nm at high speed. Performance benchmarks include: spectral resolution <6 nm FWHM, 10-𝜆 acquisition < 5 sec, μa and μs´ errors <3%, drift (7 hr) <2%. We will also develop a 2-layer skin-model to improve in vivo blood lipid quantification and develop methodology for spectral classification and quantification of TG, cholesterol, LDL-C, and HDL-C and test the accuracy of these algorithms in simulation and by fabricating 3-D printed vascularized phantoms with a range of skin types. Finally, we will conduct a normal volunteer feasibility study to assess the ability of SWIR- SFDI to accurately track postprandial lipids in comparison to traditional laboratory measurement from invasive blood draws. Completion of these aims will enable our team to progress to a larger R01-funded, hypothesis- driven clinical study following the period of this project. Over the longer term, SWIR-SFDI has the potential to transform CVD lipid testing and improve outcomes for patients through: 1.) Better risk-stratification for patients based on frequent measurements, 2.) Unprecedented characterization of circadian and ultradian lipid cycles, 3). More accessible monitoring during therapy, and 4) Development of at-home or wearable blood lipid monitors.

抽象的： 据报道，心血管疾病 (CVD) 导致全球 31% 的死亡，其中个人和 据估计，美国每年因缺乏定期筛查而造成的经济损失高达 4000 亿美元。 CVD 治疗严重不足，一些估计发现只有 1/3 的符合条件的患者采取了预防措施 血脂升高是 CVD 最重要的生物标志物之一，需要进行侵入性抽血。 其次是基于实验室的检测，这些要求限制了许多低危患者的筛查。 资源设置，并使频繁的纵向监测对每个人来说都不切实际。 显示血脂的时间动态，包括餐后升高、昼夜节律、超昼夜节律（<24 小时 周期）和月经周期的波动都会对血脂检测结果的准确性产生负面影响。 更立即、更连续地追踪血脂的侵入性方法将改变 CVD 监测 该项目的目标是开发第一个用于测量血液的非侵入性光学技术。 为了实现这一目标，我们将开发一种称为短波红外空间的新成像技术。 频域成像 (SWIR-SFDI) 利用光谱 SWIR 图案照明。 与基于模型的分析相结合，提取组织光学特性以及脂质和水浓度。 与可见光 (VIS) 和近红外 (NIR) 成像相比，SWIR 波段可能提供 更好地量化脂质和更深入的成像在这个项目中，我们建议开发 SWIR-SFDI。 仪器和处理方法来证明甘油三酯 (TG)、胆固醇、LDL-C 和 HDL-C 都是 CVD 风险的有力预测因子，我们可以对其进行高精度的纵向追踪。 使用创新的双数字微镜器件 (DMD) 制造移动高光谱 SWIR-SFDI 系统 配置用于在 700 – 1600 nm 范围内高速进行快速波长调谐和空间光图案。 性能基准包括：光谱分辨率 <6 nm FWHM、10-𝜆 采集时间 < 5 秒、μa 和 μs´ 误差 <3%，漂移（7 小时）<2% 我们还将开发 2 层皮肤模型，以改善体内血脂定量和分析。 开发 TG、胆固醇、LDL-C 和 HDL-C 的光谱分类和定量方法 在模拟中测试这些算法的准确性，并通过使用 3D 打印的血管模型来测试 最后，我们将进行正常的志愿者可行性研究，以评估 SWIR-的能力。 与传统的侵入性实验室测量相比，SFDI 可准确跟踪餐后血脂 完成这些目标将使我们的团队能够取得更大的 R01 资助的假设- 从长远来看，SWIR-SFDI 有潜力推动该项目之后的临床研究。 通过以下方式改变 CVD 血脂检测并改善患者的治疗结果： 1.) 更好地对患者进行风险分层 基于频繁的测量，2.) 前所未有的昼夜节律和超昼夜脂质循环特征，3)。 治疗期间更容易进行监测，4) 开发家用或可穿戴血脂监测仪。