February 2001
Volume 42, Issue 2
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Glaucoma  |   February 2001
Photodetector Sensitivity Level and Heidelberg Retina Flowmeter Measurements in Humans
Author Affiliations
  • Larry Kagemann
    From the Departments of Ophthalmology and
  • Alon Harris
    From the Departments of Ophthalmology and
    Physiology and Biophysics, Indiana University School of Medicine, Indianapolis;
  • Hak Sung Chung
    From the Departments of Ophthalmology and
  • Christian P. Jonescu-Cuypers
    From the Departments of Ophthalmology and
  • Drora Zarfati
    Haemek Medical Center, Afula, Israel; and
  • Bruce Martin
    From the Departments of Ophthalmology and
    Medical Sciences Program, Indiana University, Bloomington.
Investigative Ophthalmology & Visual Science February 2001, Vol.42, 354-357. doi:https://doi.org/
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      Larry Kagemann, Alon Harris, Hak Sung Chung, Christian P. Jonescu-Cuypers, Drora Zarfati, Bruce Martin; Photodetector Sensitivity Level and Heidelberg Retina Flowmeter Measurements in Humans. Invest. Ophthalmol. Vis. Sci. 2001;42(2):354-357. doi: https://doi.org/.

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

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Abstract

purpose. In vitro models suggest that Heidelberg retina flowmeter (HRF) measurements are affected by changes in photodetector sensitivity. We measured blood flow in a single volume of human retinal tissue in vivo at various sensitivity (DC) levels.

methods. The peripapillary retinal regions of 12 normal subjects were examined by HRF under five different sensitivity settings: (1) average DC range below 100; (2) average DC range below 125; (3) average DC range near 150 (normal sensitivity); (4) average DC range above 175; and (5) average DC range above 200 or extremely overexposed. The distributions of flow values were examined by pointwise analysis. All pixels from a common tissue location were analyzed, and the effect of their brightness on the flow measurement was evaluated by ANOVA with Fisher’s protected least significant difference model.

results. ANOVA analysis of image DC level showed that significantly different DC levels were achieved for each of the five sensitivity settings (P < 0.0001). Flow values decreased with increasing DC for each of the 25th percentile, 50th percentile (P < 0.0001 for each), 75th percentile (P = 0.0026), 90th percentile (P = 0.0216), and mean (P = 0.0004) flow values. The percentage of pixels with values of zero (avascular tissue) increased with increasing photodetector sensitivity (P < 0.0001).

conclusions. Improper sensitivity settings alter the detected percentage of avascular tissue and the blood flow measurements in tissue containing capillaries. Consistent assessment of retinal blood flow requires consistent photodetector sensitivity settings between longitudinal images.

Advances in the basic and clinical science of the eye depend on accurate, reproducible methods for the measurement of tissue perfusion. Defects in ocular blood flow have been implicated in the pathophysiology of a number of diseases, including diabetic retinopathy, age-related macular degeneration, glaucoma, and anterior ischemic optic neuropathy. Recently, the development of the Heidelberg retina flowmeter (HRF), a scanning laser Doppler flowmeter, has provided an index of retinal capillary blood flow in regions devoid of large vessels. 
Early animal models found that HRF measurements contain a considerable zero-offset error. 1 Validation studies using an in vitro model (milk or blood flowing through a rigid glass tube) show that the HRF measurements reliably and linearly respond to changes in velocity, within a given operating range. 1 2 3 The Tsang study, however, found that HRF measurements are altered by changing the scattering qualities of the background. 2 This unexpected finding raises the concern that, in vivo, photodetector sensitivity settings for the retinal image may affect HRF determinations of retinal perfusion. To investigate this question, we compared HRF measures of retinal blood flow across a wide range of controlled photodetector sensitivity conditions. 
Methods
Before examination, a full explanation of the procedure was given and informed consent was obtained from each subject. This study was performed in accordance with the tenets of the Declaration of Helsinki and the institutional review board approved the procedure. Eleven female and two male subjects with no history of eye disease were recruited (32 ± 9 years). From each subject, a random eye was examined five times by HRF (Heidelberg Engineering GmbH, Heidelberg, Germany). 
The HRF is a confocal scanning laser Doppler flowmeter that maps blood flow within the fundus. The HRF has previously been described in detail. 4 5 6 Moving blood cells Doppler-shift 785-nm laser light. Doppler-shifted light and nonshifted reflected light interfere to form pulsations in the detected light. The HRF measures the intensity of scattered light (brightness) from every individual pixel 128 times at a frequency of 4000 samples per second. Each pixel contains a 10 × 10 × 400 μm3 volume of tissue, and the 256 × 64 pixels in the image result in a retinal measurement area of 2560 × 640 μm2. 6 A fast Fourier transform is performed on the intensities to determine the frequency content of the interference pattern and hence the various velocities and volumes of moving reflective sources. 
The subjects were asked to close their eyes between images but to leave their head in the headrest. The laser level entering the eye was not altered, but the photodetector sensitivity setting of the HRF camera was used to produce images varying in DC level. The DC value is the HRF parameter that quantifies the intensity of reflected light at each pixel location. Photodetector sensitivity was quantified by averaging the DC value in the measurement area. Clinical photodetector sensitivity levels produce images containing pixels with DC values averaging approximately 150. Using this definition as “normal sensitivity,” we defined four other levels of photodetector settings, two each within the undersensitive and oversensitive domains. The highest and lowest DC level images were defined as those images in which most pixels in the region of interest had average DC values less than 100 and greater than 200, respectively. Undersensitive and oversensitive photodetector images were defined as images whose average region-of-interest DC value was greater than 175 or less than 125. 
Each image was centered on the superior temporal peripapillary retina. These images were analyzed by pointwise analysis, which has been described in detail. 5 7 A single plastic sheet is overlaid on the computer screen, and the location of the image boundaries, eye movements, large vessels, and disc margin are marked for each of the five images. The tissue common to each image is analyzed. A single pixel is swept across the image, and the flow value (a unitless index of retinal capillary flow) of each pixel is entered into a log file. All pixels from a common tissue location were analyzed. These pooled individual flow values are analyzed by a software package developed in our laboratory, which sorts the log file by flow and calculates the percentage of zeros, 10 percentile flow value, quartile flow values, and 90 percentile flow value as well as mean flow value. The effect of photodetector sensitivity level on flow values was performed by ANOVA using a Fisher’s protected least significant difference comparison. A P value less than 0.05 was considered significant. 
Results
ANOVA analysis showed that significantly different DC levels were achieved for each of the five photodetector sensitivity settings (P < 0.0001; see Figs. 1 and 2 ). Photodetector sensitivity had a significant effect on the percentage of zeros, the 25 percentile, 50 percentile (P < 0.0001 for each), 75 percentile (P = 0.0026), 90 percentile (P = 0.0216), and mean (P = 0.0004) flow values. P values for comparisons of flow parameters in individual sensitivity settings to flow parameters of the“ normal sensitivity” image are provided in Table 1
Discussion
The HRF is a sensitive instrument producing a unitless index that represents volumetric blood flow within capillary beds in the retina. It is also sensitive to changes in the photodetector gain. Although the beam entering the eye is held at a constant level by the HRF controls, the user may change the sensitivity of the HRF photodetector manually. Indeed, determining the proper photodetector setting requires a skilled technician. Considering the results of the present study, previous studies that did not control for variations in DC levels should be interpreted with appropriate prudence. To accurately assess retinal blood flow with the HRF, the sensitivity setting between longitudinal images must remain unaltered. A target DC level of each image should be known before acquiring image data and recorded in the subject’s records. After imaging, the DC level should be checked. This may be accomplished quickly and easily with a 50 × 50 pixel box placed in the area of interest; with the sole purpose of measuring the area’s DC level. The large box is inappropriate for flow measurements because large vessels must be excluded for accuracy. 6 In longitudinal studies of disease, as retinal disease progresses the optical properties of the tissue may change, and alterations in the sensitivity setting may be necessary to maintain the target (baseline) DC level. 
A previous study of the acute test–retest reproducibility of the HRF found a coefficient of variation of repeated measures of 6.6% for blood flow. 5 In that study the subject sat back from the HRF, and the headrest and all camera settings were randomized between the two images. In the present study, only the sensitivity was changed. The subject remained in the headrest with eyes closed between each of the five images, and focus settings and camera alignment remained constant. Further, in the original test–retest study, the default 100-pixel sample box was used to measure flow. In the present study, pointwise analysis was used to analyze all well-focused pixels, resulting in the analysis of approximately 700 pixels common to each of the five images included in the flow measurement. The high control of the imaging technique as well as an increased analysis area suggests that the coefficient of variation in the present study is at the most 6.6%. It is likely that the differences in flow measurements observed between the various flow measurements resulted from the difference in illumination level and not physiological alterations or other HRF-based sources of noise. 
Flow is calculated from the moment of the corrected power spectrum weighted by intensity, which is synonymous with brightness in this context 2 :  
\[Flow_{x,y}{=}\ \frac{{{\int}}\ f{\cdot}Pc_{x,y}df}{I_{x,y}}\]
where  
\[f{=}\mathrm{frequency,}\]
 
\[Pc_{x,y}{=}\ corrected\ (for\ noise)\ power\ spectrum\]
 
\[I{=}\ intensity\ or\ pixel\ DC\ level.\]
 
An oversensitive photodetector may have two effects on the power calculation. The first is to drive the pulsations of the interference pattern into the saturation range of the HRF’s photodetector. The effect of saturation on the flow calculation would be to drive the power spectrum term Pc x,y toward 0. The second effect is to drive the pixel intensity term, I, toward a high value. These two effects work together in the power equation to reduce the calculated flow value. Similarly, small pulsations of the interference pattern will be exaggerated in conditions of very low sensitivity, because the flow calculation divides by the DC term, I. As I approaches 0, the flow value will be increased. 
Another potential source of sensitivity-derived errors in flow measurements is the HRF’s use of a noise correction algorithm based on image intensity. 2 Raw HRF measurements of Doppler shift are altered on the basis of the assumed level of noise within the measurement pixel. Pixels with high DC values are thought to contain a high level of noise, and pixels with low DC values are thought to contain less noise. Flow measurements in high DC images are therefore reduced by a large correction factor, whereas low DC images are corrected by only a small amount. This noise correction routine would tend to alter flow values in the same direction as the intensity term in the flow equation. It is possible that the combination of noise correction and inclusion of DC level in the flow equation contributed together to the different flow measurements observed in this study. This DC-linked error in HRF measurements has been observed previously in in vitro models but has not previously been demonstrated in the human fundus. Further, in the in vitro model study, the changes in intensity occurred in the background material and not in the area being measured. 2  
Further validation of the HRF or other instruments measuring retinal capillary blood flow requires good models with known flow conditions. Despite predicting photodetector sensitivity-based errors in HRF flow measurements, there are several differences between an in vitro glass capillary tube model and the human fundus. The HRF measures an interference pattern created by Doppler-shifted and non–Doppler-shifted light. It is important that the volume being analyzed contain both moving and stationary scattering sources. In an in vitro model, blood flowing through the glass tube features a parabolic velocity distribution across the tube. Blood in contact with the tube wall had a velocity of zero. Each pixel from the in vitro model included a stationary scattering source, red blood cells at the surface of the tube. Each pixel also contained a continuous spectrum of velocities. The penetrating beam passed into the parabolic distributions of velocities present in the tube. This is not the case in the fundus. The inner diameter of a capillary is approximately equal to the diameter of a red blood cell. Should a pixel be filled with a capillary, there will be no stationary scatter source within that pixel. Light will either strike a moving blood cell or plasma. When the beam encounters plasma in the vessel, it will be scattered by structures posterior to the capillary. Of course, and at 795 nm, the only prominent light-scattering structure is hemoglobin, the surrounding tissues being transparent. 8 Animal studies have found that laser Doppler techniques are limited to surface measurements within the fundus. 9 10 Therefore, an ideal in vitro model should have (1) blood flowing though vessels with diameters approaching those of capillaries so that there is no parabolic distribution of velocities across the vessel but that all blood cells travel with a velocity equal to the mean velocity; (2) vessels surrounded by a material approximately transparent at 795 nm8, similar to the optical-scattering characteristics of human tissue, and (3) this vessel/material combination should be mounted on top of a high flow layer that mimics the choroid. Given the difficulty of creating such a complex and small model, further HRF validation studies will depend on either highly controlled, yet nonphysiological capillary tube models, or by human and animal studies in which the actual flow conditions cannot be entirely known. 
 
Figure 1.
 
Significantly different brightness levels were achieved, based on the DC levels; the quantified brightness detected by the HRF (ANOVA; P < 0.0001). Individual comparisons to normal brightness are all significant and displayed.
Figure 1.
 
Significantly different brightness levels were achieved, based on the DC levels; the quantified brightness detected by the HRF (ANOVA; P < 0.0001). Individual comparisons to normal brightness are all significant and displayed.
Figure 2.
 
Variations in brightness were achieved by altering the sensitivity setting on the HRF, producing five levels of image brightness.
Figure 2.
 
Variations in brightness were achieved by altering the sensitivity setting on the HRF, producing five levels of image brightness.
Table 1.
 
Flow Values for Under- and Overilluminated Images
Table 1.
 
Flow Values for Under- and Overilluminated Images
Flow Values Darkest DC < 100 Underexposed DC < 125 Normal DC ∼ 150* Overexposed DC > 175 Brightest DC > 200
% Zeros 14 ± 6 12 ± 4 18 ± 9 43 ± 26, † 59 ± 13, †
25 percentile 111 ± 69, † 116 ± 41, † 69 ± 55 15 ± 29, † 8 ± 17, †
50 percentile 300 ± 89 277 ± 58 248 ± 111 133 ± 124, † 99 ± 146, †
75 percentile 538 ± 140 475 ± 95 457 ± 189 304 ± 218 251 ± 285, †
90 percentile 805 ± 209 693 ± 142 666 ± 286 523 ± 275 468 ± 368
Mean 370 ± 97 329 ± 65 300 ± 118 205 ± 124, † 172 ± 159, †
Kiel JW, Elliot WR III, Harrison JM. Preliminary evaluation of the Heidelberg retinal flowmeter. In: Proceedings of the 5th International Meeting on Scanning Laser Ophthalmoscopy, Tomography, and Microscopy. San Antonio, Texas, October 1995. Abstract.
Tsang AC, Harris A, Kagemann L, Chung HS, Snook BM, Garzozi HJ. Brightness alters Heidelberg retinal flowmeter measurements in an in vitro model. Invest Ophthalmol Vis Sci. 1999;40:795–799. [PubMed]
Chauhan BC, Smith FM. Confocal scanning laser Doppler flowmetry: experiments in a model flow system. J Glaucoma. 1997;6:237–245. [PubMed]
Michelson G, Schmauss B, Langhans MJ, Harazny J, Groh MJ. Principle, validity, and reliability of scanning laser Doppler flowmetry. J Glaucoma. 1996;5:99–105. [PubMed]
Kagemann L, Harris A, Chung HS, Evans D, Buck S, Martin B. Heidelberg retinal flowmetry: factors affecting blood flow measurement. Br J Ophthalmol. 1998;82:131–136. [CrossRef] [PubMed]
Zinser G. Scanning laser Doppler flowmetry: principle and technique. Pillunat LE Harris A Anderson DR Greve EL eds. Current Concepts on Ocular Blood Flow in Glaucoma. 1999;197–204. Kugler Publications The Hague, The Netherlands.
Chung HS, Harris A, Kagemann L, Martin B. Peripapillary retinal blood flow in normal-tension glaucoma. Br J Ophthalmol. 1999;83:466–469. [CrossRef] [PubMed]
Cui WH, Ostrander LE, Lee BY. In vivo reflectance of blood and tissue as a function of wavelength. IEEE Trans Biomed Eng. 1990;37:632–639. [CrossRef] [PubMed]
Wang L, Cioffi GA, Van Buskirk EM, Zhao DY, Bacon DR. Comparison of optic nerve blood flow measured with laser Doppler flowmetry and microspheres [ARVO Abstract]. Invest Ophthalmol Vis Sci. 1999;40(4)S276.Abstract nr 1459.
Petrig BL, Riva CE, Hayreh SS. Laser Doppler flowmetry and optic nerve head blood flow. Am J Ophthalmol. 1999;127:413–425. [CrossRef] [PubMed]
Figure 1.
 
Significantly different brightness levels were achieved, based on the DC levels; the quantified brightness detected by the HRF (ANOVA; P < 0.0001). Individual comparisons to normal brightness are all significant and displayed.
Figure 1.
 
Significantly different brightness levels were achieved, based on the DC levels; the quantified brightness detected by the HRF (ANOVA; P < 0.0001). Individual comparisons to normal brightness are all significant and displayed.
Figure 2.
 
Variations in brightness were achieved by altering the sensitivity setting on the HRF, producing five levels of image brightness.
Figure 2.
 
Variations in brightness were achieved by altering the sensitivity setting on the HRF, producing five levels of image brightness.
Table 1.
 
Flow Values for Under- and Overilluminated Images
Table 1.
 
Flow Values for Under- and Overilluminated Images
Flow Values Darkest DC < 100 Underexposed DC < 125 Normal DC ∼ 150* Overexposed DC > 175 Brightest DC > 200
% Zeros 14 ± 6 12 ± 4 18 ± 9 43 ± 26, † 59 ± 13, †
25 percentile 111 ± 69, † 116 ± 41, † 69 ± 55 15 ± 29, † 8 ± 17, †
50 percentile 300 ± 89 277 ± 58 248 ± 111 133 ± 124, † 99 ± 146, †
75 percentile 538 ± 140 475 ± 95 457 ± 189 304 ± 218 251 ± 285, †
90 percentile 805 ± 209 693 ± 142 666 ± 286 523 ± 275 468 ± 368
Mean 370 ± 97 329 ± 65 300 ± 118 205 ± 124, † 172 ± 159, †
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