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使用心电图耦合的时间分辨动态光学相干断层扫描进行心脏-视网膜时间分析。

Heart-retina time analysis using electrocardiogram-coupled time-resolved dynamic optical coherence tomography.

作者信息

Valmaggia Philippe, Wolleb Julia, Bieder Florentin, Scholl Hendrik P N, Cattin Philippe C, Maloca Peter M

机构信息

Department of Biomedical Engineering, University of Basel, Hegenheimermattweg 167b/c, 4123, Allschwil, Switzerland.

Institute of Molecular and Clinical Ophthalmology Basel (IOB), Mittlere Strasse 91, 4031, Basel, Switzerland.

出版信息

Sci Rep. 2025 Jan 2;15(1):385. doi: 10.1038/s41598-024-84417-w.

DOI:10.1038/s41598-024-84417-w
PMID:39748081
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11697082/
Abstract

The eye and the heart are two closely interlinked organs, and many diseases affecting the cardiovascular system manifest in the eye. To contribute to the understanding of blood flow propagation towards the retina, we developed a method to acquire electrocardiogram (ECG) coupled time-resolved dynamic optical coherence tomography (OCT) images. This method allows for continuous synchronised monitoring of the cardiac cycle and retinal blood flow dynamics. The dynamic OCT measurements were used to calculate time-resolved blood flow profiles using fringe washout analysis. The relative fringe washout was computed to generate the flow velocity profiles within arterioles at the optic nerve head rim. We found that the blood column between the heart and the retina propagates within one cardiac cycle, denoting the arrival time as the heart-retina time (HRT). In a group of healthy subjects, the HRT was 144 ± 19 ms (mean ± SD). The HRT could provide a novel potential biomarker for cardiovascular health in direct relation to retinal perfusion.

摘要

眼睛和心脏是两个紧密相连的器官,许多影响心血管系统的疾病会在眼睛中表现出来。为了有助于理解血液向视网膜的流动传播,我们开发了一种获取心电图(ECG)耦合时间分辨动态光学相干断层扫描(OCT)图像的方法。这种方法能够对心动周期和视网膜血流动力学进行连续同步监测。动态OCT测量用于通过条纹消退分析计算时间分辨血流剖面。计算相对条纹消退以生成视神经乳头边缘小动脉内的流速剖面。我们发现心脏和视网膜之间的血柱在一个心动周期内传播,将到达时间定义为心脏-视网膜时间(HRT)。在一组健康受试者中,HRT为144±19毫秒(平均值±标准差)。HRT可为与视网膜灌注直接相关的心血管健康提供一种新的潜在生物标志物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1696/11697082/fbb9c1247873/41598_2024_84417_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1696/11697082/5c4e730949be/41598_2024_84417_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1696/11697082/0f8243425d83/41598_2024_84417_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1696/11697082/b48bf6a13b88/41598_2024_84417_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1696/11697082/fbb9c1247873/41598_2024_84417_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1696/11697082/5c4e730949be/41598_2024_84417_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1696/11697082/1dab97e25982/41598_2024_84417_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1696/11697082/c4274a713e68/41598_2024_84417_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1696/11697082/0f8243425d83/41598_2024_84417_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1696/11697082/b48bf6a13b88/41598_2024_84417_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1696/11697082/fbb9c1247873/41598_2024_84417_Fig6_HTML.jpg

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Biomed Opt Express. 2023 May 16;14(6):2658-2677. doi: 10.1364/BOE.488103. eCollection 2023 Jun 1.
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AutoMorph: Automated Retinal Vascular Morphology Quantification Via a Deep Learning Pipeline.AutoMorph:通过深度学习管道实现自动化视网膜血管形态定量分析。
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