Vavrek Sarah, Kayaalp-Nalbant Elif, Konopek Nicholas, Bou-Ghanem Ghazi, Fawzi Amani A, Mieler William F, Kang-Mieler Jennifer J, Tichauer Kenneth M
Biomedical Engineering, Illinois Institute of Technology, Chicago, USA 60616.
Department of Ophthalmology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA 60611.
Proc SPIE Int Soc Opt Eng. 2023 Jan-Feb;12360. doi: 10.1117/12.2650304. Epub 2023 Mar 14.
Fluorescein video angiographies (FVAs) are a diagnostic tool for eye diseases, such as diabetic retinopathy (DR). Currently, kinetic tracer model methods based on indicator-dilutions theory use FVAs to extract biomarkers (e.g., volumetric blood flow and retinal vascular permeability) via pixel mapping using two-step non-linear least square fitting. Prior to biomarker extraction, the FVAs must attain optimal quality. The objective of this research is to create a program to remove all frames experiencing signal drops (causes include blinking, squinting, and head movement). 15 FVAs (6 healthy control subjects, 6 diabetes mellitus no DR (DMnoDR) subjects, and 3 mild non-proliferative DR (NPDR) subjects) were analyzed for low quality frames. The average signal of each frame was analyzed as top, middle, and bottom thirds. The frame with maximum average signal up to the final frame of a created "Gold Standard" was compared with the raw AVI's frame with maximum average signal and subsequent frames. All frames before maximum average signal and any remaining frames were compared with the previous good-quality raw frame to determine if the frame of interest was of good quality. All remaining frames were subsequently re-evaluated and flagged if they had a local minimum prominence of 10% of the maximum average signal. The flagged frames', as well as former and subsequent frames', quality were subjectively determined. The AVI quality was subsequently tested via pre-DTKM processing and biomarker extraction via DTKM methods. Results displayed that the semi-automated frame removal process provides sufficient quality AVIs.
荧光素视频血管造影（FVA）是一种用于诊断眼部疾病（如糖尿病视网膜病变（DR））的工具。目前，基于指示剂稀释理论的动力学示踪模型方法利用FVA，通过两步非线性最小二乘拟合的像素映射来提取生物标志物（如容积血流量和视网膜血管通透性）。在提取生物标志物之前，FVA必须达到最佳质量。本研究的目的是创建一个程序，以去除所有出现信号下降的帧（原因包括眨眼、眯眼和头部移动）。对15例FVA（6例健康对照受试者、6例无糖尿病视网膜病变（DMnoDR）的糖尿病患者和3例轻度非增殖性糖尿病视网膜病变（NPDR）患者）的低质量帧进行了分析。将每一帧的平均信号分为顶部、中部和底部三分之一进行分析。将创建的“金标准”最后一帧之前平均信号最大的帧与原始AVI中平均信号最大的帧及其后续帧进行比较。将平均信号最大之前的所有帧以及任何剩余帧与之前的高质量原始帧进行比较，以确定感兴趣的帧质量是否良好。所有剩余帧随后重新评估，如果其局部最小值突出度为最大平均信号的10%，则进行标记。标记帧以及前后帧的质量通过主观判断确定。随后通过DTKM预处理测试AVI质量，并通过DTKM方法提取生物标志物。结果显示，半自动帧去除过程可提供足够质量的AVI。
