Ординатура / Офтальмология / Английские материалы / The Retina and its Disorders_Besharse, Bok_2011

Figure 3 High-pass resolution perimetry uses ring-shaped targets of varying size. The luminance inside each ring target is the same as the background while the core of the ring is brighter and its inner and outer edges darker. The overall intensity profile of the ring is such that when it cannot be resolved it cannot be detected. Patients press a response key when they see the stimulus (presentation time 165 ms) and a repetitive bracketing strategy is used to establish the minimum resolvable ring size.

Frequency-Doubling Technology Perimeter

The frequency-doubling technology (FDT) perimeter is based upon an illusion known as frequency doubling, in which an alternating sinusoidal grating of low spatial frequency (<4 cpd) appears, at certain temporal frequencies (>15 Hz), to have twice as many lines. This illusion is believed to be mediated by the magnocellular (M-cell) pathway and in particular by the large fiber diameter My ganglion cells which constitute 1.5–2.5% of all retinal ganglion cells.

Recent investigations of ganglion cell fiber loss in glaucoma have led to the development of a number of theories. One theory is that there is selective loss of M-cell fibers during the early stages of this condition. Another is that there is selective loss of large diameter axons and a third is that mechanisms with little redundancy (sparse representation) are likely to show losses earlier than those with greater redundancy. As FDT is believed to be based upon large-diameter M-cell fibers, which are sparsely represented, a screening test based upon this illusion should, according to all these theories, be particularly sensitive to early glaucomatous loss. However, while FDT perimeter uses the appropriate spatial and temporal frequencies for the illusion, the task presented to the patient is one of contrast sensitivity, that is, the patient is being asked when he/she can detect the appearance of a target in the peripheral field not when he/she sees the frequencydoubling illusion.

The FDT perimeter uses 0.25 cpd stimulus that subtend 10 at a temporal frequency of 25 Hz. A modified binary search strategy is used to test 17 locations. Threshold and suprathreshold test strategies have good sensitivities and specificities and the instrument has many attractive characteristics for glaucoma screening. It is a small, self-contained, portable instrument that is not sensitive to background illumination levels. It does not require a corrective lens for refractive errors (results are reported to be independent of refractive error up to 7.00 D) and it is relatively fast.

A second-generation FDT perimeter, Humphrey Matrix, was produced in 2005. This instrument uses a range of stimulus sizes (10 , 5 , and 2 ), two spatial frequencies (0.25 and 0.5 cpd) and three temporal frequencies (25, 18, and 12 Hz) to give a wider range of test programs including those that mimic those widely used in SAP (24-2, 10-2). These modifications overcome the major drawback of poor spatial resolution in the earlier instrument.

Reliability Estimates

Most modern perimeters incorporate methods for estimating the reliability of the patient’s responses. Widely used measures are fixation accuracy, false-positive response rate, and false-negative response rate. The relationship between these reliability measures and test–retest variability is, however, poor.

Fixation Accuracy

The simplest technique for monitoring fixation is observation by the perimetrist either with the aid of a telescope or camera. Such techniques are totally dependent upon the perimetrist’s judgment and continued vigilance.

Some perimeters incorporate a fixation monitor that indicates when fixation has been lost. Most fixation monitors cannot differentiate between rotations of the eye, which occur when the patient looks away from the fixation target, and translations of the eye. A translation of the eye, such as a slight sideways movement of the head, does not necessarily mean that fixation has been lost or that the angular subtence of the perimetric stimuli has been changed by a significant amount. Automatic fixation monitors are also generally insensitive to small, but significant, fixation errors (e.g., 1 ).

Most modern perimeters incorporate the Heijl–Krakau technique for sampling fixation. With this technique stimuli are occasionally presented in the region of the patient’s blind spot. If fixation is maintained during these presentations the stimulus will not be seen. If, on the other hand, fixation is lost then the stimulus is likely to fall outside of the blind spot and elicit a response from the patient. A frequently quoted cut off for reliable fixation errors is less than 33% of presentations. There is no evidence on which to base this cut-off value and the relatively small number of trials (on average there are only 10–12 fixation trials in a perimetric test) mean that the precision of the estimated rate is very low. As the position of the blind spot varies from one individual to another it is necessary, at the onset of the examination, to have a little routine which establishes the blind spot’s location. The accuracy of this routine is important as errors may later manifest themselves

as numerous fixation errors in a patient who has maintained good fixation.

The major advantages of the Heijl–Krakau technique are its simplicity and ease of implementation. Its disadvantages are:

1.It only samples fixation.

2.It increases the examination time.

3.It is unlikely to detect small fixation errors as the standard target subtends 0.5 and the blind spot an area of 5 7 .

At present, the best method is direct observation by the perimetrist. This technique is not only sensitive but also, via verbal feedback, can result in an improvement in the fixation accuracy for subsequent presentations.

False-Positive Responses

Single stimulus static test programs derive an estimate of the false-positive response rate with a number of catch trials (when the instrument goes through the motions of presenting a stimulus but does not actually present one). If the patient responds to one of these catch trials then this is classified as a false-positive response. In the SITA test algorithms this catch trial method has been replaced by one based upon response times (interval between the presentation of a stimulus and the patient pressing the response button). Olsson and colleagues were able to show a good relationship between the number of response times that fell outside a patient’s normal response window and the false-positive response rate. They also demonstrated that estimates based upon response times were more repeatable than those based upon catch trials. An added advantage of using response times is that it reduces, slightly, the overall test time. Many patients do not give a single false-positive response during a visual field test and it is important to remember the false-positive response rate is a poor predictor of test–retest variability. A less than 33% false-positive rate is often used to differentiate reliable from unreliable results, again there is no evidence on which to base this cut-off value and the relatively small number trials means that the precision of the estimate is poor. More stringent cut-offs are often used in research studies.

False-Negative Responses

An estimate of the false-negative response rate is obtained by retesting a location with a stimulus whose intensity is above the already established threshold. If the patient fails to respond positively to this presentation then this is classified as a false-negative. False-negative responses are often found to increase with the extent of visual field damage and this is believed to be due to the relationship between variability and sensitivity. Most patients have a low false-positive rate and those with a rate above 33% are

often classified as unreliable although more stringent cutoffs are often used in research trials. Again there is no evidence to support this cut-off value and the number of trials means that the precision of the estimate is poor.

Clearly, if the patient makes a high proportion of errors then his/her results must be viewed with a certain amount of suspicion. The judgment of an attending perimetrist is, however, often of greater value than these reliability estimates.

Analytical Techniques

Total Deviation and Pattern Deviation Plots

Total deviation and pattern deviation maps take the threshold data from a visual field examination and calculate, for each test location, whether or not the threshold values are significantly different from those of a normal eye of the same age. They rely upon the perimeter having a database of threshold values from normal eyes in order to perform these calculations. Pattern deviation values differ from total deviation values in that they have been adjusted for overall shifts in sensitivity. For example, we might get a patient whose overall sensitivity is below that of a normal patient of the same age. In this case, some locations may be highlighted as being abnormal on the total deviation map but not on the pattern deviation map. This adjustment is based upon the findings from some of the most sensitive test locations, that is, if these are above or below normal then all values are shifted down or up.

Total and pattern deviation probability maps use the distribution of total and pattern deviation values within a normal population, to calculate the probability of each threshold estimate coming from a normal eye. The results are expressed in the standard statistical way, that is, as being beyond the 5%, 2%, 1%, or 0.5% probability level.

Global Indices

Flammer and colleagues proposed that the visual field defects associated with glaucoma could be divided into three different categories: (1) those that cause an overall depression in the sensitivity of the eye, (2) those that cause local defects, and (3) those that cause an increase in the variability of results; and that each category could be represented by an index. They called these three indices mean defect, loss variation, and short-term fluctuation.

The concept of using three separate indices to represent a glaucomatous visual field has found very wide acceptance within the ophthalmological professions and most visual field instruments now incorporate a set of algorithms which compute these or very similar indices. The name given to these indices varies from one instrument to another, as does the form of calculation. In the

Humphrey instrument they are called mean deviation, pattern standard deviation, and short-term fluctuation and the values are weighted according to the normal variance seen at each location.

Mean defect (deviation) (MD) gives the average difference between the threshold values and those from an age-matched control. Loss variance is a measure of the spread of defect values which increases when there is a local defect. An alternative measure of spread is the standard deviation (SD). This statistic has the advantage of giving a measure of spread in decibels and is now used in preference to variance measures. In the Humphrey perimeter this is called pattern standard deviation (PSD) while in the Octopus it is called sLV, which stands for the square root of the loss variance, that is, the SD. Short-term fluctuation is a measure of variability in threshold estimates derived from repeat testing a subset of locations. However, a global estimate of variability is of limited value as it is now recognized that variability is dependent upon sensitivity and varies across the visual field.

MD and sLVor PSD can be plotted over time to aid the detection of progressive loss. They can also be plotted against each other to give a measure of the extent of loss, Brusini staging system. The sensitivity of MD to media opacities, such as cataract, has led to the development of a new global index for the Humphrey perimeter called the visual field index (VFI). This index gives a percentage measure of the residual field and uses pattern deviation values and a weighting system based upon cortical magnification to give a more representative measure of a patient’s visual function.

The Glaucoma Hemifield Test (GHT) (Humphrey) and cluster analysis (Octopus) are additional indices designed to help detect rather than quantify glaucomatous visual field loss. They are designed to detect vertical asymmetry between the superior and inferior hemifields. The GHT looks at the asymmetry in pattern deviation probability values between five areas in the superior field and their mirror images in the inferior field, while the Octopus cluster analysis uses the defect values within each of five slightly different clusters within the superior and inferior visual field. Both use a database of normal values to establish whether or not a response is outside normal limits.

Linear Regression

Linear regression can be applied to a longitudinal series of:

1.global indices (MD, PSD, VFI),

2.clusters of test locations (Octopus cluster analysis), or

3.individual test locations (point-wise analysis).

The significance of any change over time and the gradient of the regression line can be used for predicting long-term outcomes.

The Progressor software package presents the findings from a point-wise regression analysis in a particularly novel way that retains the information on both the depth of the defect and the significance of any change while the Peridata software package (Peridata) color codes each test location according to the significance of any change. From these charts the clinician can ascertain whether or not any progressive changes are close to fixation or at the edge of the visual field, where they may have been influenced by artifacts such as a droopy upper lid.

The accuracy of a regression analysis and any predictions is dependent upon the number of examinations. Several research groups have concluded that we really need about five visual field results before we can reliably calculate the gradient of the regression line.

Change Probability

Change probability compares each test location with that of a baseline measure and establishes whether or not there has been any significant change. Results are presented in the form of a visual field plot where each location is classified according to a series of cut-off probability levels, for example, p < 0.05. The baseline value is often the average of two visual field results (to give a better estimate) that need not be the first ones recorded, that is, we can establish whether there has been any significant change from intermediate results.

Change probability analysis takes into account the relationship between variability and sensitivity. One of the criticisms of change probability is that it does not use the information obtained in intermediate examinations, that is, it only compares the current finding with the baseline value. The recently introduced Progression Analysis Probability plot for the Humphrey Visual Field Analyzer uses different symbols to code whether or not the change has occurred in just the current, last two, or last three examinations.

See also: IOP and Damage of ON Axons; Retinal Ganglion Cell Apoptosis and Neuroprotection.

Further Reading

Anderson, A. J., Johnson, C. A., Fingeret, M., et al. (2005). Characteristics of the normal database for the Humphrey Matrix perimeter.

Investigative Ophthalmology and Visual Science 46: 1540–1548. Artes, P. H., Henson, D. B., Harper, R., and McLeod, D. (2003).

Detection and quantification of visual field loss: A comparison of perimetric strategies by computer simulation. Investigative Ophthalmology and Visual Science 44: 2582–2587.

Bengtsson, B. (2000). Reliability of computerised perimetric threshold tests as assessed by reliability indices and threshold reproducibility in patients with suspect and manifest glaucoma. Acta Ophthalmologica 78: 519–522.

Bengtsson, B. and Heijl, A. (2008). A visual field index for calculation of glaucoma rate of progression. American Journal of Ophthalmology

49: 66–76.

Bengtsson, B., Olsson, J., Heijl, A., and Rootzen, H. (1997). A new generation of algorithms for computerised perimetry, SITA. Acta Ophthalmologica 75: 368–375.

Brusini, P. and Filacorda, S. (2006). Enhanced glaucoma staging system (GSS2) for classifying functional damage in glaucoma.

Journal of Glaucoma 15: 40–46.

Cello, K. E., Nelson-Quigg, J. M., and Johnson, C. A. (2000). Frequency doubling technology perimetry for detection of glaucomatous visual field loss. American Journal of Ophthalmology 129: 314–322.

Chauhan, B. C., House, P. H., McCormick, T. A., and LeBlanc, R. P. (1999). Comparison of conventional and high-pass resolution perimetry in a prospective study of patients with glaucoma and healthy controls. Archives of Ophthalmology 117: 24–33.

Flammer, J., Drance, S. M., Augustiny, L., and Funkhouser, A. (1985). Quantification of glaucomatous visual field defects with automated perimetry. Investigative Ophthalmology and Visual Science

26: 176–181.

Frisen, L. (1987). A computer-graphics visual field screener using high-pass spatial frequency resolution and multiple feedback devices.

Documenta. Ophthalmologica Proceedings Series 49: 441–446. Henson, D. B. and Artes, P. H. (2002). New developments in

supra-threshold perimetry. Opthalmic and Physiological Optics

22: 463–468.

Henson, D. B., Chaudry, S., Artes, P. H., Faragher, E. B., and Ansons, A. (2000). Response variability in the visual field: Comparison of optic neuritis, glaucoma, ocular hypertension and normal eyes. Investigative Ophthalmology and Visual Science 41: 417–421.

Johnson, C. A., Adams, A. J., Casson, E. J., and Brandt, J. D. (1993). Blue-on-yellow perimetry can predict the progression of glaucomatous damage. Archives of Ophthalmology 111: 645–650.

King, A. J. W., Taguri, A., Wadood, A. C., and Azuara-Blanco, A. (2003). Comparison of two fast strategies, SITA fast and TOP, for the assessment of visual fields in glaucoma patients. Archives of Ophthalmology 240: 481–487.

Miranda, M. and Henson, D. B. (2008). Perimetric sensitivity and response variability in glaucoma with single stimulus automated perimetry and multiple stimulus perimetry with verbal feedback. Acta Ophthalmologica 86: 202–206.

Morales, J., Weitzman, M. L., and Gonzalez de la Rosa, M. (2000). Comparison between Tendency-Oriented Perimetry (TOP) and octopus threshold perimetry. Ophthalmology 107: 134–142.

Olsson, J., Bengtsson, B., Heijl, A., and Rootzen, H. (1997). An improved method to estimate frequency of false-positive answers in computerized perimetry. Acta Ophthalmologica 75: 18–183.

Viswanathan, A. C., Fitzke, F., and Hitchins, R. A. (1997). Early detection of visual field progression in glaucoma: A comparison of PROGRESSOR and STATPAC 2. British Journal of Ophthalmology

81: 1037–1042.

Relevant Websites

http://webeye.ophth.uiowa.edu – University of Iowa Health Care; IPS Standards: Imaging and Perimetric Society.

http://www.peridata.org – Peridata; Peridata Software GmbH.

Glossary

Contrast sensitivity function (CSF) – The reciprocal of contrast threshold measured as a function of spatial frequency in a spatial CSF or as a function of temporal frequency in a temporal CSF.

Higher order aberrations (HOAs) – These aberrations in the eye describe imperfections in the optics that cannot, in general, be corrected for with conventional refraction (i.e., sphere and cylinder components).

Mesopic – The range of intermediate light levels between cone threshold and rod saturation where both rod and cone signals contribute to a visual response.

Photopic – The range of high light levels above rod saturation where vision is mediated by signals from cone photoreceptors.

Scotopic – The range of low light levels below cone threshold where visual responses rely entirely on rod signals.

Spectral luminous efficiency (SLE) – The SLE function represents an appropriate measure of detector spectral responsivity, which in the case of the human eye is used to convert radiant flux to an equivalent photometric flux.

Spectral power distribution (SPD) – This describes the wavelength distribution of radiant flux (power) emitted by a source or surface.

Spectral radiance – A quantity that describes the radiant flux (power) within a narrow wavelength interval emitted by a source or surface in a given direction per unit solid angle, per unit area of the source.

Spectral responsivity (SR) – The SR of a detector of radiation is a measure of the spectral sensitivity of the detector and represents the signal generated per unit incident radiant flux as a function of wavelength.

Introduction

Arguably, the most important feat of human vision is its ability to operate effectively over the enormous 10 000-million-fold range of illumination levels to which

it can be exposed, from starlight to bright sunlight (see Figure 1). Moreover, it achieves this despite the limitations imposed by individual neurons in the visual system, many of which have dynamic ranges of no more than about 100-fold. Sensitivity regulation over such a massive range cannot be achieved without compromise. One compromise is to share the range between two different types of photoreceptors: the sensitive rods, functioning at lower scotopic levels of illumination, and the less-sensitive cones, functioning at higher photopic levels, the two working together at intermediate mesopic levels. Another important compromise is the trade-off between increased sensitivity at lower light levels, and improved temporal and spatial acuity at higher light levels. As the light level increases, high sensitivity is no longer needed and is traded for improvements in spatial and temporal acuity. These adjustments occur separately within the rod and the cone systems, but the rod system has a lower acuity and higher sensitivity compatible with its operation at lower light levels.

There are 100 million rods, but only 5 million cones in the human retina. The 5 million cones are most densely packed in a small region of the retina at the center of our vision, where photopic vision is best. Cones are less sensitive to light but respond more quickly than rods. The cones and their postreceptoral pathways operate well at higher light levels in the upper mesopic and photopic range. Over most of this range, processes of light adaptation within the retina ensure that the operation of the cone photoreceptors adjusts to changes in light level to provide a useful dynamic range of about 100, which remains relatively independent of light level. When the light level is lower, the cone system increases its sensitivity by increasing the spatial and temporal extents over which light is summed, as a result of which spatial and temporal acuity is reduced. Even at very high light levels, bleaching of the light-sensitive pigment in the cone protects its output from saturating and thus failing to signal changes in light. By contrast, rods and their pathways operate well at lower light levels in the scotopic and lower mesopic ranges. The rods and rod pathways are optimized for these levels. The rods are more sensitive and much slower than the cones, so that they integrate light over time, and their postreceptoral pathways integrate light over space. Capable of single-photon detection at the lowest levels, the rod system ceases to operate effectively at the top of the mesopic range.

Mesopic vision describes the range of light levels over which signals from both rods and cones contribute to the visual response. This range extends just over a 1000-fold from cone threshold to rod saturation, and encompasses light levels that are often found in occupational environments. At mesopic levels, the spectral composition of the illuminant plays an important role in determining the relative strengths of rod and cone signals (see Figure 2). Marked changes in visual performance that vary over the visual field are observed as the level of illumination is lowered and/or the spectral composition of the illuminant is modified. The gradual increase in rod signals as the light level decreases causes changes in the overall spectral sensitivity of the eye that has consequences for the visual effectiveness of the illuminant spectral power distribution (SPD) (i.e., the intensity of the light as a function of wavelength). Mesopic luminous efficiency is inherently complex, and therefore difficult to standardize or model, because it depends on the outputs of both the rod and the cone photoreceptors. Not only are there differences in the photoreceptor spectral sensitivities, but there are also differences in the properties of the postreceptoral pathways through which the rod and cone signals are transmitted.

The nature and balance of these pathways are constantly changing with changes in light level in the mesopic range.

Much effort has gone into establishing spectral responsivity functions that are appropriate for photopic, mesopic, and scotopic lighting conditions. This approach rests on the assumption that visual performance can be adequately predicted by weighting the intensity of the light reaching the eye (the radiant flux) with functions that reflect the spectral responsivity of the eye at high, intermediate, and low light levels. Some success has been achieved at low light levels in the scotopic range, but this success reflects the fact that the spectral responsivity is determined by a single photoreceptor type, the rods, which have a single type of photosensitive molecule. The approach has been less successful at photopic levels, where responsivity depends on up to three cone photoreceptors, and least successful at mesopic levels where it depends on different types of rod and cone photoreceptor systems with markedly different properties. In this article, we present data on how visual performance changes with light level. We argue that the changes in performance are much more profound than the changes in spectral sensitivity as captured by changes in luminous efficiency functions.

The Concept of Luminous Efficiency

Function

The traditional development of photometry has been strongly influenced by radiometry and the properties of an ideal detector of radiation, which is assumed to exhibit response linearity and additivity and has a well-defined spectral responsivity (in the case of the eye at photopic levels, V(l)). Let the radiant light fluxes Y1 and Y2, respectively, produce signals S1 and S2 when the detector is exposed to each flux separately. Response linearity means that when the detector is exposed to Y1 þ Y2 together, the signal generated must equal S1 þ S2, irrespective of the intensity and the spectral composition of Y1 and Y2.

When this is the case, the spectral responsivity curve of the detector becomes a particularly useful quantity since it provides the means of computing the detector signal in response to any broadband, spectral distribution of light flux. This is achieved by simply integrating the radiant flux (weighted at each wavelength by the spectral luminous efficiency function or V(l)) over the spectral range for which V(l) and Y(l) are both nonzero to give:

Z

YV V ðlÞYðlÞd l; ½1&

where YV is usually described as the luminous flux. The simplicity of this approach continues to be important and relevant in the more applied areas of vision science, for

example, in lighting engineering and photometry. Spectral luminous efficiency functions (i.e., curves that specify the spectral responsivity of the eye, and thus the effectiveness of lights) have been produced using a number of different experimental approaches for both photopic vision (when only cone photoreceptor signals are involved) and for scotopic vision (when vision relies entirely on rod signals). The response linearity of the eye is, however, very limited. Lights that are intense enough to adapt the visual response give rise to nonlinearities that break down the additivity required by eqn [1]. At photopic and mesopic levels, where up to four photoreceptors can contribute to the visual response, additivity is even more limited. As the wavelength or SPD of the illuminant changes, and the photoreceptors selectively adapt, so also do the relative contributions of the different photoreceptors to luminous efficiency. Mesopic and photopic luminous efficiency functions, in general, are not therefore fixed in spectral sensitivity.

Spectral luminous efficiency functions are currently used to provide a means of quantifying the total luminous flux associated with broad-band and narrow-band sources, and for computing the luminance contrast of an illuminated object with respect to that of the surrounding background. When the relative SPD of an object matches that of its surrounding background, the computed contrast is independent of the assumed spectral responsivity curve. However, for objects that differ in SPD from their surrounding background, the computed contrast is strongly dependent on the choice of spectral luminous efficiency function. Moreover, at photopic levels, perceived color differences can also contribute significantly to perceived object contrast, as a result of which any efforts to produce a single spectral luminous efficiency function to account for both the quantity of luminous flux and perceived contrast has severe limitations. In the mesopic range, when rod contrast signals also contribute to object conspicuity, prediction of effective contrast becomes

even more challenging. The relative rod and cone contributions to luminous efficiency vary continuously with the illumination level, the spectral composition of the adapting background, the location of the stimulus in the visual field, and the temporal and spatial characteristics of the stimulus. A number of studies have attempted, with limited success, to model these complex interactions. Yet, any model based only on changes in spectral sensitivity is unlikely to capture the effects of the large number of parameters that have been shown experimentally to affect object appearance.

In the remainder of this article, we will be less concerned with the need to produce fixed spectral luminous efficiency functions appropriate for either the photopic or the mesopic range. Instead, we focus on describing how key aspects of visual performance change with ambient light level.

Quality of Vision and Light Level

Spatial Acuity, Spatial Contrast Sensitivity,

and Light Level

An important aspect of visual performance is the ability to see fine spatial detail. A simple functional test is to measure the threshold luminance contrast (i.e., DL/Lb , where DL represents the increment in stimulus luminance with respect to the surrounding background (Lb)) needed to resolve letters or some other visual stimuli when the angular subtense of stimulus elements is larger than the high-contrast resolution limit of the eye. The latter is often taken to be 10 of arc and corresponds to a letter size of 50 of arc. In order to avoid eye strain and to minimize the effects of microfluctuations of accommodation, a stimulus size three times the spatial resolution limit of the eye or larger is often employed in various occupational environments. For these reasons, the data shown in Figure 3 were measured using a Landolt ring stimulus of

30 gap size. The orientation of the gap was restricted to four possible locations (i.e., top right, top left, bottom right, and bottom left) and the subject’s task was to press one of four buttons to indicate the position of the gap. Two different groups of normal subjects (with high-contrast acuity of 10 of arc or better) were involved in this study. The photopic thresholds follow a tight distribution, while the mesopic thresholds are larger and exhibit significantly increased variability. Interestingly, about 50% of the 219 subjects examined under mesopic conditions exhibit thresholds that fall within the normal 2s (s: standard deviation) limits of the photopic range.

The results show no significant correlation with either age or the quality of the observer’s optics (in terms of the mean-wavefront, higher order aberrations (HOAs)). Figure 4 shows similar data as a function of retinal illuminance for a single subject measured at the fovea and 2.5o away from fixation, along the horizontal meridian. In this experiment, the pupil size was measured every 20 ms and the luminance of the visual display was adjusted appropriately in order to maintain constant retinal illuminance. The results show a large 2.2-log unit increase in foveal contrast acuity thresholds over the 1000-fold change in retinal illuminance. Contrast acuity at low light levels in the mesopic range is extremely poor and below 0.6 photopic trolands (phot. td, a measure of photopic retinal illuminance) the periphery becomes more sensitive than the foveal region because of the distribution of rods and cones across the retina.

Retinal illuminance (log trolands)

Figure 4 Contrast acuity thresholds (DL/Lb) measured at the fovea and 2.5 in the periphery, as a function of retinal illuminance, for a 30-gap Landolt ring stimulus. Spectrally calibrated neutral density filters were employed to achieve the full range of retinal illuminance. Pupil size was monitored continuously and this measurement was used to adjust the luminance of the display to maintain constant retinal illuminance. The correlated color temperature of the background field was6500K (CIE (x, y) – chromaticity coordinates: 0.305, 0.323). The three stimulus locations were interleaved randomly and the subject’s task was to report the orientation of the gap (i.e., top right, top left, bottom right, or bottom left) by pressing one of four buttons located at the corners of a square, each button corresponding with one of the gap locations.

Contrast acuity measurements, although functionally important, only partly characterize how the spatial properties of the visual system change with retinal illuminance. A more complete characterization is provided by the spatial contrast sensitivity function or CSF, which defines the sensitivity (i.e., the reciprocal of the contrast threshold) for the detection of sinusoidal gratings (smoothly changing black-and-white stripes) as a function of their spatial frequency (i.e., as they change from coarse to increasingly fine stripes). Loosely speaking, the contrast describes local, spatial differences between the object of interest (the stripes, in this case) and its background (the mean background level), and often correlates with subjective measures such as object conspicuity. In a typical CSF experiment, the observer is presented with sinusoidal gratings of a given spatial frequency, and is asked to vary its contrast to find the contrast at which the grating is just visible. This threshold measurement is then repeated at a series of spatial frequencies to build up a complete CSF. Several spatial CSFs measured by van Nes and Bouman as a function of retinal illuminance are shown in Figure 5. As the illumination level increases, the CSFs change in shape from being low-pass (i.e., falling monotonically with increasing spatial frequency) to being much broader, slightly band-pass functions (i.e., peaking in sensitivity at some intermediate frequency and falling off in sensitivity at lower and higher frequencies).

There are two notable features of the data in Figure 5. First, at low spatial frequencies, the contrast sensitivity becomes roughly constant above about 0.09 phot. td. Thus, the contrast sensitivity becomes independent of the illumination level at higher photopic levels. Second, at higher spatial frequencies, the highest spatial frequency that can just be seen, which corresponds to the spatial acuity measurements just discussed, improves markedly, increasing from 5 cycles per degree at 0.0009 phot. td to 55 cycles per degree at 5900 phot. td. These two features reflect, in part, the decrease in the spatial extent of visual integration with increasing light level.

In general, the visual system is good at spatial-contrast detection to the extent that, under optimal conditions, the threshold contrast can be as low at 0.2% (see Figure 5). By contrast, the visual system is poor at estimating the amount of total luminous flux, a task which is presumably evolutionarily less important than the detection of faint edges and boundaries that reveal the presence of objects in the visual field.

Pupil Size, Higher order Aberrations, and Light Level

The size of the pupil affects retinal illuminance, depth of field, diffraction, and HOAs. In addition, it can also affect scattered light in the eye, when the light scattering is nonuniform over the pupil. The most important and

Log10 contrast sensitivity

3.0

van Nes and Bouman (1967)

2.5

best-studied afferent visual signal that drives the pupil is generated by changes in the ambient illumination, and this pathway is associated entirely with subcortical projections. Other pathways are also involved, but the corresponding pupil changes are more transient and smaller in amplitude. Aging affects steady-state pupil size, but in general the pupil varies from 2 mm in bright sunlight to over 8 mm in the dark (see Figure 1). The HOAs of the eye, mostly spherical aberration, increase rapidly with pupil diameter. Under natural conditions, this corresponds to either mesopic or scotopic vision. Figure 6(a) shows how the average wavefront aberration of the eye varies with pupil diameter. At low light levels, the pupil of the eye is large and consequently the quality of the retinal image is affected by increased aberrations and scattered light. When the light level is high, HOAs become very small and diffraction becomes more important and can often limit most the quality of the retinal image that can be achieved.

The potential of improving spatial vision by reducing HOAs using customized, wavefront-guided corneal refractive surgery has been of considerable interest. Since the size of the pupil significantly affects the aberrations in the eye (Figure 6(a)), any experimental assessment of the benefit of HOA correction must compare performance with and without correction using the natural pupil size. The visual benefit for contrast acuity that results from HOA correction is shown in Figure 6(b) for ambient light levels in the range 140–0.01 cd m 2. Although HOA correction undoubtedly improves retinal image quality for large pupil sizes in mesopic and scotopic vision, the visual

benefit for everyday visual performance as measured by contrast acuity is limited, largely as a result of the poor spatial resolution of mesopic and scotopic vision. The results of Figure 6(b) suggest that the spatial CSFs shown in Figure 5 are well matched to the increases in the HOAs with increasing pupil size. As the light level decreases and the pupil size and HOAs increase, the retina is less able to resolve higher spatial frequencies, and thus the most deleterious effects of the increasing aberrations are invisible.

Flicker Perception

Similar to the changes in the spatial properties of the visual system that accompany light adaptation, the changes in its temporal properties can also be characterized by contrast sensitivity measurements. A temporal CSF defines the sensitivity for the detection of flicker as a function of temporal frequency. In a typical experiment, the observer is presented with a uniform disk that flickers sinusoidally at some temporal frequency, and is then asked to adjust its flicker contrast (sometimes called ripple ratio or modulation) until the flicker is just visible. This threshold measurement is subsequently repeated at a series of temporal frequencies to build up a complete CSF. Figure 7 shows temporal CSFs measured by Kelly as a function of retinal illuminance. These functions have several characteristics in common with the spatial CSFs shown in Figure 5. As the illumination level is increased, the temporal CSFs, like the spatial CSFs, change from being lowpass to slightly band-pass.