June 2022
Volume 63, Issue 7
Open Access
ARVO Annual Meeting Abstract  |   June 2022
Laser speckle contrast imaging derived retinal hemodynamics abnormalities in Alzheimer's disease
Author Affiliations & Notes
  • zi jin
    Peking University Shenzhen Graduate School, Shenzhen, Guangdong, China
  • chunxia jiang
    Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
  • yueting yang
    Hebei University, Baoding, Hebei, China
  • yufei lu
    Hebei University, Baoding, Hebei, China
  • jinying li
    Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
  • xuhui chen
    Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
  • chuanqing zhou
    Peking University Shenzhen Graduate School, Shenzhen, Guangdong, China
  • qiushi ren
    Peking University, Beijing, Beijing, China
  • Footnotes
    Commercial Relationships   zi jin None; chunxia jiang None; yueting yang None; yufei lu None; jinying li None; xuhui chen None; chuanqing zhou None; qiushi ren None
  • Footnotes
    Support  National Natural Science Foundation of China (61875123); Beijing Natural Science Foundation (Z210008); Shenzhen Science and Technology Program (1210318663).
Investigative Ophthalmology & Visual Science June 2022, Vol.63, 1578 – A0367. doi:
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      zi jin, chunxia jiang, yueting yang, yufei lu, jinying li, xuhui chen, chuanqing zhou, qiushi ren; Laser speckle contrast imaging derived retinal hemodynamics abnormalities in Alzheimer's disease. Invest. Ophthalmol. Vis. Sci. 2022;63(7):1578 – A0367.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : Laser speckle contrast imaging (LSCI) is a promising technique for measuring the retinal hemodynamics because LSCI is capable of imaging large field of retinal blood flow in just a few seconds. This study aims to evaluate retinal hemodynamics abnormalities in Alzheimer's disease (AD) through our custom-built LSCI.

Methods : 83 Patients with AD and 75 healthy age and sex-matched subjects were recruited in the study. All subjects underwent medical history, blood pressure measurement, the Montreal Cognitive Assessment (MOCA), best-corrected visual acuity, intraocular pressure (IOP), and custom-built LSCI. Based on the laser speckle phenomenon, the LSCI was used to measure the retinal blood flow. A laser diode at 850 nm wavelength was used for LSCI. An annular fiber bundle was adopted for uniform retinal illumination. The retinal speckle pattern images were captured by the CMOS camera (M3ST507M-H, DO3THINK, GuangDong, China) with 1224 pixels X 1024 pixels at a frame rate of 80 fps for a 5-second measured period. Then, LSCI algorithm was proposed to calculate the laser speckle contrast (LSC) value to represent the blood velocity. The relative blood flow of the optic nerve head area was chosen to obtain the retinal blood flow pulsatility curve (Fig. 1). According to the pulse-waveform analysis, the quantitative parameters can be calculated, such as flow acceleration index (FAI). The eye with better corrected visual acuity was selected for the LSCI.

Results : There was no significant difference between the groups for systolic blood pressure, diastolic blood pressure, mean blood pressure, IOP, and ocular perfusion pressure. No difference in patients with diabetes mellitus, and hypertension was observed between two groups. Compared to controls, the parameters corrected visual acuity (0.86±0.19 vs 0.93±0.19, p=0.05), MOCA scores (15.9±7.0 vs 28.1±1.5, p<0.01)), and FAI (1.17±0.39 versus 1.33±0.54, p=0.05) were significantly lower in AD (Fig. 2).

Conclusions : The present study demonstrates significant differences in retinal hemodynamics between AD patients and healthy subjects through LSCI. FAI may be a useful indicator for early detection of AD.

This abstract was presented at the 2022 ARVO Annual Meeting, held in Denver, CO, May 1-4, 2022, and virtually.

 

Figure 1. The typical retinal perfusion image (A) and blood flow pulsatility curve (B).

Figure 1. The typical retinal perfusion image (A) and blood flow pulsatility curve (B).

 

Figure 2. The output parameters of LSCI between AD and controls. BOS, blowout score; BOT, Blowout time; FR, falling rate; RI, resistivity index; RR, rising rate.

Figure 2. The output parameters of LSCI between AD and controls. BOS, blowout score; BOT, Blowout time; FR, falling rate; RI, resistivity index; RR, rising rate.

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