Depth cues, rather than perceived depth, govern vergence

We studied the influence of perceived surface orientation on vergence accompanying a saccade while viewing an ambiguous stimulus. We used the slant rivalry stimulus, in which perspective foreshortening and disparity specified opposite surface orientations. This rivalrous configuration induces alternations of perceived surface orientation, while the slant cues remain constant. Subjects were able to voluntarily control their perceptual state while viewing the ambiguous stimulus. They were asked to make a saccade across the perceived slanted surface. Our data show that vergence responses closely approximated the vergence response predicted by the disparity cue, irrespective of voluntarily controlled perceived orientation. However, comparing the data obtained while viewing the ambiguous stimulus with data from an unambiguous stimulus condition (when disparity and perspective specified similar surface orientations) revealed an effect of perspective cues on vergence. Collectively our results show that depth cues rather than perceived depth govern vergence.
Introduction
In vision, binocular fusion is facilitated by vergence. Various different types of horizontal vergence have been distinguished based on inputs used by the vergence system, e.g. binocular disparity and accommodation. One of those, fusional vergence, uses binocular disparity as input. And another, proximal vergence, is associated with “knowledge of nearness” (Howard, 2002). Recently, the influence of depth perception on vergence has regained interest, because of the possibility to probe perception through vergence. In several studies, various depth cues were used to induce depth and the influence of perception on vergence was studied both without and with binocular disparity present. In this study, we investigate whether depth perception per se contributes to vergence.
Enright (1987b) measured vergence movements corresponding to the depth relations implied by perspective foreshortening, under monocular viewing conditions. He concluded that the perceived depth suggested by perspective foreshortening could elicit vergence. Ringach et al. (1996) used the kinetic depth effect to induce perception of depth. They measured vergence, also under monocular viewing conditions, and found that vergence corresponded to depth perceived by the subject. This led them to conclude that perceived depth can elicit vergence.
More recently, several studies investigated the influence of perceived depth on vergence in the presence of disparity, i.e. under binocular viewing conditions. Sheliga and Miles (2003) used Ogle’s induced effect in which vertical disparities give rise to depth. The authors reported that depending on the condition, a maximum of 41% of the vergence could be attributed to perceived depth. Both et al. (2003) used Werner’s illusion to induce depth. They reported slight vergence responses corresponding to perceived depth. These results led to the conclusion that perceived depth contributed to the vergence responses.1
However, in all aforementioned studies, depth perception was correlated with stimulus properties, i.e. the depth cues giving rise to the perceived depth. Therefore, the distinction between an effect on vergence caused by perception and an effect due to depth cues could not be made. Thus, it may be possible that signals related to the depth cues contributed to vergence rather than perceived depth itself.
To investigate whether perceived depth itself is sufficient to influence vergence, a condition is required in which depth perception is not correlated with both monocular and binocular cues that give rise to the perceived depth (Allison et al. 1998; Gillam 1968; Gillam and Ryan 1992; Gillam and Cook 2001). The slant rivalry stimulus offers the possibility to dissociate vergence effects due to depth cues from those caused by perceived depth (van Ee et al. 2002). To create a slant rivalry stimulus, perspective foreshortening and binocular disparity are used to define slanted surfaces. If the cues specify opposite slant orientations, subjects report alternations between perceiving a slanted rectangle and perceiving an oppositely slanted trapezoid (van Ee et al. 2002). Thus, perceived slant alternates, whereas the depth cues remain constant.
Using these slant stimuli, the contribution to vergence predicted by perception is different from that predicted by the depth cues. If perceived depth is sufficient to influence vergence, a difference in vergence should be observed while alternations occur between the two possibly perceived surface slants. However, if perceived slant does not influence vergence, vergence should remain stable regardless of the perceived surface slant orientation.
To study the effect of perceived surface slant on vergence, we analyzed saccades made under various stimulus and perceptual conditions. Binocular saccades usually contain a conjugate component (version) and a disjunctive component (vergence) (Erkelens et al. 1989). Both components are preprogrammed, thus if perceived depth influences vergence, the disjunctive component should be related to the depth direction specified by the perceived slant. Hence, measured vergence at saccade offset can give insight into the input that drives the vergence system.
Methods
Experimental setup
Stimuli were displayed using a conventional Wheatstone stereoscope consisting of two TFT displays (20″ LaCie Photon20Vision II, 1,600 × 1,200, 75 Hz) and two small mirrors, see Fig. 1. The mirrors were slanted about the vertical axis at an angle of 45° with respect to the display. The virtual intersection point of the orthogonal mirrors was aligned with the center of the displays. Subjects were seated close to the mirrors so that the left eye could not see the right mirror and vice versa. The straight-ahead viewing distance (eye-mirror-display) was 57 cm. Subjects’ heads were fixated using a bite-board.
Fig. 1Wheatstone stereoscope. Subjects viewed one TFT display with the corresponding eye via one of the mirrors. The viewing distance (eye-mirror-display) was 57 cm. Note that the subjects were in reality much closer to the mirrors than depicted here and that there was no crossover, i.e. each eye could only see via one mirror
Eye movements were measured using the head-mounted Eyelink I system at 250 Hz. The cameras were positioned beneath the mirrors. The whole setup and experimental room were painted black matte and the room was darkened.
Stimuli
A stimulus consisted of two images displayed, one on each of the displays. The displayed images were generated using custom Open GL based software. The images used were trapezoidal or rectangular shapes composed of lines and a sparse random dot texture, as shown in Fig. 2. The images were surrounded by a fronto-parallel sparse random dot background (not shown).
Fig. 2An image as shown on one display. Perspective foreshortening indicates a slant of 70°. The disparity gradient was produced by horizontally scaling the two eyes’ half images. The red fixation cross is positioned in the center of the stimulus
Perspective foreshortening of both images corresponded with a 2D projection (cyclopean viewpoint) of a rectangular surface slanted (±) 70° about the vertical axis. A horizontal (binocular) disparity gradient defined a (∓) 50° slant about the vertical axis, which was produced by scaling the two eyes’ half images horizontally. After scaling, the horizontal angular width of the stimulus was 20° for all stimuli independent of the imposed slant angles, see Fig. 3a. The same figure also shows that a counter clockwise rotation (CCW) about the vertical axis was defined as a positive slant angle and a clockwise rotation (CW) as a negative slant angle.
Fig. 3a The subtended angle from center to left or right side of the stimulus was 10° (γ) irrespective of the imposed surface slants defined by disparity (D) and perspective (P). A counter clockwise rotation of the surface about the vertical axis is defined as a positive surface slant angle (+). b Examples of different stimulus conditions. The surface slant angle as defined by disparity was ±50° (α) and the perspective defined surface slant angle (β) was ± 70°. In the unambiguous stimulus, disparity and perspective defined the same surface orientation yielding a stable perception of surface slant (1). Whereas in the ambiguous stimulus condition (2), the orientations of the defined surfaces were opposite, giving a bistable perception of surface slant
Stimulus parameters could define any of the following possibilities: (1) a pair of trapezoids in which the signs of slant defined by perspective foreshortening (P) and disparity (D) were equal [unambiguous stimulus, e.g. P = 70° and D = 50°, see Fig. 3b(1)] or (2) a pair of trapezoids in which the signs of slant defined by perspective foreshortening and disparity were opposite [ambiguous stimulus, e.g. P = 70° and D = −50°, see Fig. 3b(2)]. These physical parameters led to the following perceived slanted surfaces: ad (1) a slanted rectangle (stable slant) or ad (2) alternations of a slanted rectangle and an oppositely slanted trapezoid (bistable slant). Note that the slant angle defined by the monocular cues is larger than the angle defined by the binocular cues, to produce a sufficient degree of bistability for all subjects. The disparity gradient could not be diminished, as this would deteriorate the signal-to-noise ratio of vergence (see Sect. 2.5 as well).
Furthermore, a standing disparity was added to all stimuli, which made all surfaces appear to be positioned in front of the display. A fixation cross was presented in the center of the stimulus.
Procedure and tasks
Experimental trials consisted of a sequence of five different displayed items. Subjects were first presented with a fixation dot (used for offline drift correction) in the center of the display at display depth for 1.5 s (1). This dot was replaced by a fixation cross at the location and depth corresponding to the center of the stimulus (2). After 1.5 s, the stimulus images were added (3). A beep was sounded 2.0 s after stimulus onset. A monocular arrow appeared 2 s thereafter in the left eye’s image (4). The screen was blanked after 4 s (5).
During the trial, subjects were instructed to fixate the cross. When the monocular arrow had appeared, subjects were instructed to make one single saccade towards the side of the surface indicated by the arrow. The arrow always pointed towards the near side of the slanted surface defined by disparity. By doing this the vergence response (disjunctive part of saccade) was maximized, because some subjects showed considerably less vergence change when saccades were made towards the uncrossed disparity side of the stimulus. This is in agreement with the effect of version–vergence ratio on the disjunctive part of the saccade as described by Enright (1984).
Subjects had to report their percept prior to saccade onset. They were asked to report the perceived nearest side of the stimulus after the beep using a numerical keypad. By reporting which stimulus side they perceived as nearest, subjects implicitly indicated whether they perceived a slanted rectangle or a slanted trapezoid.
When viewing a slant rivalry stimulus, subjects report perceiving spontaneous alternations of their percept. In addition, they are able to voluntarily control their perception as well (van Ee et al. 2002, 2005b). To obtain equal sized data sets for each of both perceptual conditions, we used this ability to voluntarily control perception of the ambiguous stimulus. Thus, subjects were instructed to hold one of both possible perceived surfaces when viewing the bistable stimulus before the experiment started.
One experimental block consisted of a total of 16 trials, of which 8 were ambiguous stimuli conditions and the other 8 were unambiguous stimuli conditions. Both conditions were counter balanced for saccade direction and trials were randomized for conditions and saccade directions. Each experimental session contained three to four experimental blocks run consecutively. A calibration was performed, at the start of each block, i.e. every 5 min.
Data analysis
Raw data provided by the Eyelink system was analyzed off line. We used a custom calibration procedure specifically designed to calibrate the raw data. This procedure consisted of a weighted least square error fit with a third order polynomial [35 measurement points, BFGS minimization technique (Press 1993)] (van Beers 2007). Offset correction was performed offline for each trial using the data obtained with the first fixation dot. On the calibrated data, saccade detection was performed based on version (mean gaze angle of left and right eye) velocity. A saccade was detected when the version velocity was higher than 120°/s. The end of the saccade was defined when version velocity dropped below 20°/s. Only saccades with an amplitude of at least 7° in the indicated direction were analyzed. After the saccade, a slower vergence movement was usually present, vergence calculated as right eye gaze data–left eye gaze data. This was assumed to be part of the initial preprogrammed vergence. We defined the preprogrammed vergence offset when vergence velocity was minimum within a timeframe of 100 ms after saccade offset and 5°/s at the most. We used the data at the point of minimum vergence velocity for further analysis. As we were interested in the vergence difference between the onset and offset of the saccade, we present the data relative to the gaze direction before onset of the saccade. Vergence data were averaged [three nearest neighbor average (24 ms interval)] to remove high-frequency noise.
Subjects
Subjects (6 females and 9 males aged between 18 and 30) had normal or corrected to normal vision. Before taking part in the experiment, it was checked whether a subject was able to comply with the instructions of the experiment. Not all potential subjects could perceive the trapezoidal interpretation of the stimulus within the timeframe of the experiment (2 s), when disparity and perspective foreshortening were in conflict. These subjects either had poor stereovision or needed more than 2 s to elicit a voluntary controlled flip. In total eight subjects were excluded from further participation, because it would have been impossible to measure the effect of depth perception on vergence.
Results
Saccades had normal main sequence characteristics (amplitudes of about 9°, durations of about 56 ms, maximum velocities of about 310°/s) (Collewijn et al. 1988). Most saccades reached an amplitude of 80–100% of the amplitude defined by the width of the stimulus. Sometimes a correction saccade was made after about 150 ms. Only a small fraction of saccades (less than 2%) was in the opposite direction as indicated by the monocular arrow presented.
Experiment 1: Contribution of perception of depth to vergence
To investigate whether perceived surface orientation contributes to vergence, we analyzed the data obtained during the presentation of the ambiguous stimulus. As mentioned in Sect. 2.3, we asked subjects to hold one of both possible orientations. In 52% of the 659 trials subjects reported perceiving a slanted rectangle and the slanted trapezoid was perceived in 48% of the trials. In 88% of all trials the percept was in agreement with the instruction given before the experiment started. The data of trials in which they failed (due to spontaneous or no (controlled) flips) are not included in the data presented here, but were not significantly different from the trials in which subjects were successful in obliging to the instruction.
Predictions
While the cues remained constant, perceived orientations alternated. Thus, we predict that if perception contributes to vergence, vergence should alternate accordingly. But, if not, the measured vergence changes should be independent of perceived slant orientation. Because we asked subjects to report only the sign of the orientation (and not the magnitude) of perceived slant, we can only predict the direction of the vergence changes, as illustrated in Fig. 4a. When subjects perceived the trapezoid (disparity-based percept), the saccades were directed towards the near side of the perceived surface, whereas saccades were directed towards the far side when subjects perceived the rectangle (perspective-based percept). Consequently, the vergence changes predicted by perception are in the converging direction (blue area) when subjects perceived a trapezoid and in the diverging direction (red area) when they perceived the rectangle. The upper and lower limits of the predicted vergence regions in Fig. 4a are defined by the vergence based on a zero depth change (grey line) and a vergence change based on the maximum slant defined by either depth cue: disparity (perception of trapezoid, blue line) or perspective (perception of rectangle, red line).
Fig. 4a Predicted vergence changes based on perceived surface slant orientation. The transitions between vergence at saccade onset (t = 0 ms) and at saccade offset are described using a sigmoid function. The upper and lower limits of the predicted vergence regions are based on the vergence step corresponding with the depth defined by a single cue. The box indicates the range of the detailed graphs in b. b Vergence changes based on perceived surface slant orientation of subject S6 (top) and subject S1 (bottom). Mean traces for each condition with SE of every fourth data point are displayed. Saccade offset occurred at 55 ms for S6 and at 46 ms for S1 conform main sequence characteristics (saccade onset at t = 0 ms). These data show that (1) saccade offset does not coincide with offset of preprogrammed vergence per se, (2) that the “perception of rectangle” condition is more converging than the “perception of trapezoid” condition from S6, which is opposite to the traces of S1
Results
Figure 4b shows representative vergence traces from the “perception of trapezoid” and “perception of rectangle” conditions. These vergence traces are means of 25 trials at the least, with standard error (SE) displayed at every fourth data point. Saccade onset (t = 0 ms) has been used to align the individual vergence traces for calculating the mean. Saccade offset occurred at t = 55 ms for S1 and at t = 46 ms for S6, which is in accordance with main sequence characteristics. Figure 4b shows that at this point in time, the preprogrammed vergence movement has not ceased. Preprogrammed vergence offset was determined for individual traces (see Sect. 2.4) and cannot be pinpointed exactly in these graphs. Preprogrammed vergence offset typically occurred at t = 120 ms (±30 ms), at which point the vergence velocity (mean of all subjects) was 0.2°/s (SD = 0.15°/s). The diverging movement present in the onset of the saccades is commonly observed in most conjugate saccades and caused by temporal/nasal eye movement differences (Collewijn et al. 1988).
As Fig. 4b shows, the vergence changed in the converging direction for both perceptual conditions. Moreover, all subjects showed converging responses. Thus, if subjects perceived a slanted trapezoid, vergence was in the same direction as the perceived orientation of the surface. And, if they perceived a slanted rectangle, the direction of vergence was opposite to the perceived orientation of the surface. This indicates that the contribution of perceived orientation to vergence, if any, is small. When comparing the data traces with the predictions in Fig. 4b, it is clear that the measured vergence changes are close to the value predicted by the disparity cue (blue dotted line). This result suggests that the largest contribution to vergence is related to the disparity cue.
To investigate the magnitude of the effect of perceived orientation on vergence, we further analyzed the data of all subjects, starting with selecting the end of the preprogrammed vergence movement as described in Sect. 2.4. We then normalized the data, using the calculated prediction based on the disparity cue, thereby effectively removing the variability caused by the interocular distance.2 These normalized data are presented in Fig. 5a. Because these data show individual biases (e.g. S4), we used the difference between the two conditions tested for calculations on group data (see Fig. 5b). The data of leftward (bottom) and rightward (top) saccades are shown separately, because individual biases varied across these conditions (e.g. for S4). ANOVA statistics on individual and group data showed that there was no significant difference between the two investigated conditions (except for S1 leftward saccade, see Fig. 5 caption), thereby rejecting the hypothesis that perceived orientation influences vergence. Thus, vergence is independent of perceived orientation.
Fig. 5a Vergence (mean and SE selected as described in Sect. 2.4) relative to predicted vergence based on the disparity cue (0 on vertical axis) from leftward saccade trials (bottom) and rightward saccade trials (top). These data show that (1) there are individual biases (e.g. S4), (2) the “perception of rectangle” (red circle) is not less convergent than the “perception of trapezoid” (blue pentagon) condition for all subjects (e.g. S2, S5) and both directions (e.g. S4, S6), rebutting the predictions based on perception. b Vergence (mean and SE selected as described in Sect. 2.4) relative to “perception of a trapezoid” condition (0 on vertical axis). On the right side of these graphs the mean (and SE) of all subjects is shown. There is no systematic trend visible across subjects. All differences are nonsignificant except for S1, leftward saccade (F = 6.36, P < 0.05)
Experiment 2: Contribution of depth cues other than disparity to vergence
The results from experiment 1 show that perceived orientation does not influence vergence. Still, as stated in the introduction, cues other than disparity may contribute to vergence. We investigated this hypothesis by analyzing the data from the ambiguous stimulus and the unambiguous stimulus presentations.
Predictions
For both conditions the disparity defined slant was identical. In the unambiguous stimulus, the orientation of slant angle defined by perspective foreshortening was identical to the one defined by disparity, whereas it was opposite in the ambiguous stimulus. Thus, if perspective contributes to vergence, we predict that the vergence change of the ambiguous condition should be less convergent than that of the unambiguous condition. But, if not, vergence should be independent of the stimulus presented. Predictions of vergence changes based on slant cues are illustrated in Fig. 6a. For both conditions, the disparity cue signaled a slant of 50° in magnitude and as saccades were directed towards the crossed disparity side of the stimulus, the corresponding predicted vergence change converges (blue dotted line). The perspective cue signaled a slant of 70° in magnitude in both conditions. In the unambiguous condition, the vergence change predicted by perspective converges (green dashed line) and consequently, the predicted vergence change in the ambiguous condition diverges (red line).
Fig. 6a Predicted vergence changes based on the slant cues of the stimulus. The predicted values are based on the depth defined by a single cue. Details similar as in Fig. 4. b Vergence changes from ambiguous and unambiguous stimulus trials of subject S6 (top) and subject S1 (bottom). Mean traces of each condition with SE of every fourth data point are displayed. Saccade offset occurred at 56 ms for S6 and 46 ms for S1 (saccade onset at t = 0 ms). These data show that the vergence step of the ambiguous stimulus condition is less convergent than the vergence step of the unambiguous stimulus condition for both subjects
Results
Figure 6b shows representative traces of the unambiguous and ambiguous stimulus conditions. The vergence traces are means of 25 trials at the least with the SE displayed at every fourth data point. The characteristics of these vergence traces are similar to the traces in Fig. 4b. The vergence traces in Fig. 6b both converge towards the predicted value based on the disparity cue, see Fig. 6a (blue dotted line). Thus, the contribution to vergence that can be related to disparity is relatively large, consistent with the results described before.
We further analyzed the data to investigate whether there was a significant effect of the perspective cue on vergence, using the same method as described in the previous subsection. Figure 7a shows the normalized data, whereas Fig. 7b displays the data from the ambiguous condition relative to the data from the unambiguous condition including the group means. Leftward (bottom) and rightward (top) saccades are shown separately. ANOVA statistics on group and individual data show that there is a significant difference between the ambiguous and unambiguous condition. The statistics for group data are: leftward saccades (lw) (difference = 0.14°, F = 13.04, P < 0.05) and rightward saccades (rw) (difference = 0.16°, F = 62.89, P < 0.001). And for individual subjects: S0 lw (F = 11.29, P < 0.01), S1 lw (F = 14.50, P < 0.001) and rw (F = 4.04, P < 0.05), S2 lw (F = 25.22, P < 0.0001) and rw (F = 24.14, P < 0.0001), S4 lw (F = 24.84, P < 0.0001), S6 lw (F = 5.22, P < 0.05) and rw (F = 11.02, P < 0.01).
Fig. 7a Vergence (mean and SE selected as described in Sect. 2.4) relative to predicted vergence based on the disparity cue (0 on vertical axis) from leftward saccade trials (bottom) and rightward saccade trials (top). Note that (1) there are some large individual biases, (2) that the ambiguous condition (purple triangle) is consistently less convergent than the unambiguous condition (cyan square), supporting the predictions based on slant cues. b Vergence (mean and SE) relative to unambiguous condition. On the right side of these graphs the mean of all subjects is shown. The systematic trend visible between and across subjects is significant for the whole group (leftward saccades: F = 13.04, P < 0.05 and rightward: F = 62.89, P < 0.001). All significant differences are denoted by a star (values are stated in text)
Thus, perspective, or signals related to it, significantly contributed to vergence.
Discussion
Our findings show that depth cues rather than depth perception itself contribute to vergence accompanying saccadic movements. Perspective being congruent or incongruent with disparity caused a difference of about 14% in vergence changes predicted by disparity alone. Although our results show that vergence remained constant in the ambiguous stimulus condition, subjects perceived alternations of surface orientation. Collectively, these results show that perspective and disparity are each weighted differently for perception and vergence.
Binocular studies
Our result on the influence of perception on vergence refines the conclusions of previous studies relating perception and vergence (Sheliga and Miles 2003; Both et al. 2003). These studies investigated the influence of perception on vergence under binocular viewing conditions, similar as in our study. However, contributions of perception of depth to vergence could not be dissociated from those of depth-inducing cues. This dissociation was possible in our study. We found that, perspective contributes to vergence, independent of its perceptual effect. This conclusion suggests that vertical disparities (Sheliga and Miles 2003), global horizontal disparities (Both et al. 2003) and perspective (present study) contribute to vergence similarly as local horizontal disparities do, albeit to a lesser extent.
Monocular studies
The perspective studies of Enright (1987a, b) show that vergence occurs corresponding to the depth implied by (linear) perspective under monocular viewing conditions. Enright concluded that the perceived depth contributed to vergence. However, in light of the present study, his results show that depth cues contribute to vergence under monocular viewing conditions as well. Moreover, without the dominantly contributing factor (disparity) being present, other depth cues become the main contributors to vergence.
The Necker cube study of Enright (1987a, b) and the ambiguous rotating sphere study of Ringach et al. (1996) seem to provide similar stimulus conditions as our experiment. The single depth cue used induced alternations in depth perception. In both studies corresponding alternations in vergence were measured and both authors conclude that depth perception induced vergence.
However, each depth cue used in these studies was inherently ambiguous. Thus, in these studies, the contribution of perception of depth to vergence could again not be dissociated from the contribution of the depth cues themselves. Taking the present study into consideration, there is evidence that their results reflect the alternations of the depth cue related signals and not those related to perception. In view of this conclusion, the fact that the vergence changes measured by Enright in the Necker cube experiment were much smaller than those in the linear perspective experiment may be explained by a degradation of the signals related to the depth cues due to the ambiguity herein.
Influence of perception on other visual phenomena
Our results indicate that vergence is influenced by low-level (sensory) processes and not by high-level (voluntary control) processes. Studies on bistability (van Ee et al. 2005b, a; Toppino 2003) show that high-level mechanisms can influence perception, whereas, as shown in the present study, they do not influence vergence. A recent study by Knapen and van Ee (2006) shows that slant adaptation is not influenced by perception or by voluntary control of perception, but is solely based on depth cues in the (slant rivalry) stimulus. Thus, both adaptation and vergence are influenced by low-level mechanisms, but not by high-level mechanisms.
Different weighting of cues for vergence and perception
To explain the current results and those of Enright (1987a, b) and Ringach et al. (1996), a single 3D surface representation used for perception and binocular eye movements as suggested by Ringach et al. (1996) is inappropriate. The perceptual states and the alternations thereof are not reflected in the vergence responses, suggesting that two separate representations are used for perception and eye movements. Moreover, the present results suggest that the weights assigned to signals related to monocular and binocular depth cues are different for perception and vergence, resulting in either alternating or stable outcomes respectively, see Fig. 8. These outcomes may construct two separate 3D space representations one used for perception and the other for vergence or, as an alternative, one 3D representation for perception whereas the signals for vergence are directly wired to the oculomotor system. However, in the latter situation the monocular signals must still be disambiguated before the motor command can be given to reorient the eyes.
Fig. 8Perception and vergence are based on separate processing streams. Both monocular cues (such as relative motion, linear perspective, blur and looming) and binocular cues [such as horizontal disparity (global and local) and global vertical disparity] are used for perception as well as for vergence. The weights assigned to the individual cues might be different when used for perception than when used for vergence, resulting in different outcomes. In the present study, for example, the ambiguous stimulus yields a bistable perception of surface slant, whereas it yields a stable vergence angle. Cognitive factors, such as voluntary control, do not seem to exert influence on vergence, but they do influence perception
Perception and action
Our results constitute another example of a dissociation between perception and action systems as suggested by Goodale and Milner (1992). Since then evidence supporting a dissociation has been accumulating, for a review see Goodale and Westwood (2004), Carey (2001) and Bruno (2001). Haffenden and Goodale (2000) and Haffenden et al. (2001) have shown that there are conditions in which perception has no influence on action, whereas other studies (Erkelens and Collewijn 1985; Masson et al. 1997; Schreiber et al. 2001; Banks et al. 2001; van Ee and van Dam 2003) have shown that conditions exist in which eye movements have no influence on visual perception. On the other hand, some studies conclude that action is influenced by perception (Franz et al. 2000, 2001; Franz 2001; Smeets et al. 2002; Lopez-Moliner et al. 2003; de Grave et al. 2006a, b; Bernardis et al. 2005; Knox and Bruno 2007). As we have shown all depth cues are used by the vergence system. The use of pictorial depth cues by other motor systems could perhaps explain this incongruence.
Conclusion
Our findings show that depth cues rather than perceived depth govern vergence that accompanies saccades. Perspective being congruent or incongruent with disparity caused a 14% difference in vergence change. These results combined with other studies show that in addition to local horizontal disparity, also global disparity, relative motion and perspective are used as input by the vergence system. Furthermore, our findings suggest that monocular and binocular cues are weighted differently for perception and vergence.

vergence
ambiguous
disparity
depth perception


Linkage study of 14 candidate genes and loci in four large Dutch families with vesico-ureteral reflux

Vesico-ureteral reflux (VUR) is a major contributing factor to end-stage renal disease in paediatric patients. Primary VUR is a familial disorder, but little is known about its genetic causes. To investigate the involvement of 12 functional candidate genes and two reported loci in VUR, we performed a linkage study in four large, Dutch, multi-generational families with multiple affected individuals. We were unable to detect linkage to any of the genes and loci and could exclude the GDNF, RET, SLIT2, SPRY1, PAX2, AGTR2, UPK1A and UPK3A genes and the 1p13 and 20p13 loci from linkage to VUR. Our results provide further evidence that there appears to be genetic heterogeneity in VUR.
Introduction
Vesico-ureteral reflux [VUR (MIM 193000)], the retrograde passage of urine from the bladder, is one of the most commonly detected congenital anomalies. With a prevalence of approximately 1% [1], VUR can be primary, due to an incompetent valve mechanism at the uretero-vesical junction, or secondary, due to a functional or anatomical urethral obstruction. VUR is often accompanied by non-neuropathic bladder/sphincter dysfunction (NNBSD). This complex is a major cause of urinary tract infections in children [2] and the sometimes resulting reflux nephropathy is the cause of approximately 7% of end-stage renal disease in paediatric patients in the Netherlands [3]. Severe primary VUR can concur with congenital renal insufficiency based on hypoplasia/dysplasia of one or both kidneys. Genetic factors play an important role in the aetiology of primary VUR, since siblings of affected children have a 32% risk of VUR [4], and since there is 80% concordance between monozygotic twins [5]. VUR may occur in isolation or as part of a syndrome, such as renal-coloboma syndrome. Apart from the recently published involvement of ROBO2 [6] little is known about the genetic causes of isolated primary VUR in humans. The aim of the present study was to confirm linkage to published candidate loci and genes. So far, only one genome-wide linkage study has been reported, which showed significant linkage to a 17 cM locus on chromosome 1p13 in five Caucasian families and suggestive linkage to chromosome 20p13 [7]. To date, these results have not been replicated [8]. Embryonal ectopic ureteral budding has been proposed to be a mechanism for the development of VUR [9, 10]. Defects of the RET and GDNF genes have been shown to cause ectopic ureteral budding [11, 12]. Hence, these and other genes involved in the RET/GDNF pathway are obvious functional candidate genes for VUR. Genes involved in syndromal VUR and genes derived from mouse models with urinary tract abnormalities (such as AGTR2) are also attractive functional candidate genes for VUR. The aim of this study was to assess the 1p13 and 20p13 loci and appropriate candidate genes (Table 1) for their role in the Dutch VUR population by performing linkage analysis in four large families.
Table 1Genes tested in linkage study of four large multi-generational VUR families (LOD logarithm of the odds, NPL non-parametric linkage, HLOD heterogeneity LOD, A ureteral budding, BRET/GDNF pathway, C mouse and human phenotype, D linkage study, E in urothelial plaque with UPK3A (mouse model), F mouse model.)GeneRelevanceChromosomeLocation (cM)Multi-point LOD score at the gene locationNPLNPL p-valueAlphabHLODbReferenceGDNFA/B554−2.030.700.220.100.01[13]RETA/B1066−2.55−0.850.800.000.00[13]SLIT2A/B434−2.150.350.320.150.80[14]SPRY1A/B4126−3.25−0.980.860.000.00[14]PAX2A/B10124−3.43−0.490.630.000.00[13]AGTR2A/CX71−3.81−1.180.880.000.00[15]HLADRB1D646−1.840.250.350.200.16[16]UPK1AE1961−2.90−0.620.690.000.00[17, 18]UPK1BE31380.151.240.120.650.43[17, 18]UPK2E11115−1.50−0.220.520.000.00[17, 18]UPK3AA/F2253−3.40a−1.200.800.000.00[17, 18]UPK3BE789−1.080.590.250.000.00[17, 18]aTwo-point analysis of marker D22S928; 0.5 cM away from UPK3A.bAlpha: estimated proportion of families linked to result in corresponding heterogeneity LOD (HLOD). HLOD analyses were performed, but did not contribute and are not discussed.
Methods
DNA of four unrelated Dutch VUR families was collected (Fig. 1), which had been ascertained as part of a previous study [19]. Of a total of 51 samples there were 21 affected individuals. The families provided moderate power to detect linkage as calculated with SLINK (probability of obtaining LOD scores of at least 1.0, 2.0, or 3.0 was 74%, 49% and 18%, respectively). An affected phenotype for index patients was based on their having been treated for primary VUR, while for family members it was based on having a positive case history (of actual VUR, or multiple urinary tract infections with high fever as a child, or evidence of reflux nephropathy, such as requiring renal replacement therapy without obvious other causes) (see also Fig. 1). All other family members were classified as “unknown”, despite negative imaging results at a young age in some of them. Dutch paediatric urologists consider the use of voiding cysto-urethrography (VCUG) in asymptomatic children just for research purposes inappropriate. Therefore, we could not classify family members as “not affected”.
Fig. 1VUR family pedigrees (21 affected individuals in 51 samples). Family 1: 2 left kidney had to be removed at 5 years; 3, 4 end-stage renal disease (ESRD); 9 VUR and ESRD; 15 VUR and duplex collecting system; 19 VUR; 18 VUR and nephropathy. Family 2: 68, 69, 73 not included in analyses; 25, 26, 30 and 31 VUR; 33 VUR and dysfunctional voiding. Family 3: 39, 42, 43 VUR and dysfunctional voiding symptomatology; 47 VUR, dysfunctional voiding, meatal stenosis. Family 4: 50 and 52 recurrent urinary tract infections (UTIs) as a child, duplex collecting system; 55 UTIs and urinary tract operation; 49 VUR; 51 VUR and dysfunctional voiding symptomatology
Some of the candidate genes play roles in congenital anomalies of the kidney and urinary tract (CAKUT) phenotypes (such as VUR, duplex collecting system and renal hypoplasia in mice) [9, 15]. Therefore, families in which one or more patients had these kinds of phenotypes (and VUR) were not excluded. All participants gave their informed consent, and the Medical Ethics Committee of the University Medical Centre Utrecht approved the study.
For the 1p13 locus, we started out with the markers flanking the reported 1p13 linkage peak (D1S1653 and GATA176C01) [7]. Both markers now have different map locations if one is reviewing the most recent updates of the Ensembl (v38) and Marshfield databases. In fact, the telomeric marker GATA176C01 (D2S2972) even maps to a different chromosome (2q11). The centromeric marker D1S1653, which in our query result has roughly the same genetic position (164.09-166 cM) as previously published [7, 8], localizes on chromosome 1q23. Therefore, we tested both the entire 1p13–1q23 and 2q11 loci for linkage to VUR. A total of 11 short tandem repeat polymorphism (STRP) markers for 1p13–1q23 and seven STRP markers for 2q11 (with an average intermarker distance of 5 cM) were chosen to saturate the regions spanning 55.3 Mb on chromosome 1 and 46.3 Mb on chromosome 2. For 20p13, five STRP markers were selected, spanning 12.0 Mb (Supplementary Table 1 Online).
For the candidate genes, we aimed to cover the specific location with an average intermarker distance of 2 cM (Supplementary Table 1 Online).
Markers were genotyped as described elsewhere [20] in the 51 family members, together with three Centre d’Etude Polymorphism Humaine (CEPH) reference samples and three negative controls. The polymerase chain reactions (PCRs) were carried out on a GeneAmp PCR system 9700 machine (Applied Biosystems). The PCR products were separated on an ABI 3730 DNA sequencer (Applied Biosystems). The output was analysed with Genemapper 3.5 software (Applied Biosystems). Two investigators checked all the genotypes, and we verified the identity of the markers by comparing genotypes of the CEPH reference samples with the CEPH genotype database. A Mendelian inheritance check was performed with PedCheck 1.1 software [21], and samples with Mendelian errors were excluded from the linkage analysis.
Multi-point (both parametric and non-parametric) analyses were performed for all markers with GENEHUNTER (version 2.1_r2 beta), or GENEHUNTER PLUS (for X-linked dominant calculations in AGTR2) [22]. We assumed an autosomal dominant model with reduced penetrance (0.8) for the parametric analyses, similar to the parameters previously described [7, 23]. This mode of inheritance agreed most with our pedigrees (Fig. 1). The phenocopy rate was equal to the population frequency of VUR (0.01). Disease allele frequency was assumed to be 0.01. Regions with a parametric LOD score ≤ −2 were defined as exclusion regions [24]. All significance levels applied in this study were based on previously proposed thresholds [24, 25].
Results
Twelve functional candidate genes were screened for linkage to VUR. The multi-point LOD score obtained for each of the 12 genes (at the genetic location of the gene) is shown in Table 1, together with the non-parametric linkage (NPL) score and corresponding P value. Multi-point LOD scores with NPL score and corresponding P value for all markers are shown in Supplementary Table 1 Online. Eight of the functional candidate genes (GDNF, RET, SLIT2, SPRY1, PAX2, AGTR2, UPK1A and UPK3A) were completely excluded. For the other four, the results were inconclusive, although linkage is highly unlikely.
For the reported linkage regions, no significant linkage was detected either. One of the markers reported to be on chromosome 1 [7] appeared to reside on chromosome 2 (see Methods section). Therefore, both the original locus on chromosome 1p13 and the “new” locus on chromosome 2q11 were tested. The chromosome 1 locus was completely excluded, as the multi-point LOD score was below −2 for the entire region. Sixty-one percent of the locus on chromosome 2 could be totally excluded. The locus on chromosome 20p13 was completely excluded (Fig. 2).
Fig. 2Multi-point LOD plots for the reported loci tested in the linkage study of four large multi-generational VUR families. Because one of the flanking markers of the 1p13 locus proved to actually map to chromosome 2, we also tested the chromosome 2q11 region. HLOD (heterogeneity LOD) analyses did not contribute. Positions in cM
Discussion
We performed a comprehensive screen of 12 functional candidate genes and two reported loci (which later proved to be three separate regions). All the genes, except HLADRB1, had, in some way, been proven to play a role (indirectly) in ectopic ureteral budding and were thought likely to play a role in human primary VUR. However, we did not detect linkage to any of them. We were able to exclude eight genes (GDNF, RET, SLIT2, SPRY1, PAX2, AGTR2, UPK1A and UPK3A) as major players in these Dutch VUR families. ROBO2, the receptor of SLIT2 [26], had already been ruled out, since it had been sequenced in the four probands in a parallel study; no mutations were detected [6], therefore it was not included in the present study. Nevertheless, these genes may still be involved in the mechanism causing VUR. The moderate power these pedigrees provided to reveal linkage means that it is possible that the genes that showed inconclusive results might have yielded positive results in a more highly powered study. Since the RET/GDNF pathway plays such a central role in ureteral budding, it is plausible that these genes are, indeed, causative factors for VUR but that the pathogenetic variants lie in upstream or downstream regulatory elements. Other genes that are more or less directly involved in the RET/GDNF pathway or in ureteral budding in general, such as EYA1, GATA3, WT1 or BMP4 [27], may also contribute to VUR.
We could not confirm linkage for any of the reported loci, and we were even able to exclude completely the 1p13 and 20p13 loci. This is the second non-replication of the 1p13 region [8]. Our data show that the 1p13 locus resides either on chromosome 1p13 to 1q23 or on 2q11. We also excluded linkage to most of this 2q11 locus.
We realize that these families show some intra-familial and inter-familial heterogeneity. This heterogeneity, however, is similar to that described in VUR families by others [7]. In older generations (Fig. 1) it is impossible to have more data than self-reported history, but the described phenotypes are very likely to be caused by VUR. Therefore, we did assign the affected status to these patients, but we are aware of the fact that this is one of the limitations of the study.
One of the major obstacles for linkage studies in VUR is the relative rarity of large pedigrees, which is due to many children growing out of the disorder, the reduced penetrance of the trait, and the locus heterogeneity [8]. Furthermore, when doing linkage studies in relatively few families, one assumes a large effect of one or few genes. Maybe the genes of interest do play a role, but their effect is too small to be picked up. Therefore, studies like ours and those published [7, 8] are useful to search for one or more major genes. For this reason, association studies with large sample sizes may offer a better approach for unravelling the genetics of VUR. Both a hypothesis-free (genome-wide) approach and a more elaborate candidate gene study would be interesting follow-up studies. Alternatively, it might be interesting to study the role of copy number variants (CNVs) in VUR, since, recently, such CNVs were proposed to be involved in the mechanism underlying a number of complex disorders [28, 29]. Apart from those in a recent study by Lu et al. [6], no genes have been published that appear to be directly involved in primary VUR in humans, and no replication of the linkage peak on 1p13 [7] has been reported. Our results provide further evidence for genetic heterogeneity in VUR. We hypothesize that several genes, which still have to be identified but which are likely to affect ureteral budding, will each play a role in the pathogenesis of VUR.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Supplementary Table 1 Online
Markers used in the linkage study of four large, Dutch, multi-generational VUR families. MPL multi-point LOD score (DOC 148 kb)

vesico-ureteral reflux/genetics
kidney diseases/genetics
kidney diseases/pathology
linkage (genetics)


A comparison of echocardiographic and electron beam computed tomographic assessment of aortic valve area in patients with valvular aortic stenosis

The purpose of this study was to compare electron beam computed tomography (EBT) with transthoracic echocardiography (TTE) in determining aortic valve area (AVA). Thirty patients (9 females, 21 males) underwent a contrast-enhanced EBT scan (e-Speed, GE, San Francisco, CA, USA) and TTE within 17 ± 12 days. In end-inspiratory breath hold, a prospectively ecg-triggered scan was acquired with a beam speed of 50–100 ms, a collimation of 2 × 1.5 mm and an increment of 3.0 mm. The AVA was measured with planimetry. A complete TTE study was performed in all patients, and the AVA was computed using the continuity equation. There was close correlation between AVA measured with EBT and AVA assessed with TTE (r = 0.60, P < 0.01). The AVA measured with EBT was 0.51 ± 0.46 cm2 larger than the AVA calculated with TTE measurements. EBT appeared to be a valuable non-invasive method to measure the AVA. EBT measures the anatomical AVA, while with TTE the functional AVA is calculated, which explains the difference in results between the methods.
Introduction
Aortic valve sclerosis is common in the elderly population, affecting about 25% of adults over 65 years of age [1, 2]. The presence of aortic sclerosis, with or without demonstrable haemodynamic obstruction, is associated with an increase of approximately 50% in the risk of cardiovascular death [3]. The timing of aortic valve replacement surgery has also an effect on mortality risk [4]. The indication for aortic valve replacement generally is based on haemodynamic variables, such as transvalvular pressure drop and the presence of symptoms. Significant reduction in the aortic valve area (AVA) is also an important indicator [4]. Hence, exact evaluation of the aortic valve and the AVA is necessary to make the best treatment decision.
Evaluation of aortic valve stenosis by means of transthoracic echocardiography (TTE) has gained widespread acceptance in clinical routine and pressure gradients across the aortic valve have shown to correlate well with invasive measurements [5, 6]. Moreover, TTE is used to determine the functional AVA, using the continuity equation [5, 6]. However, the reliability of TTE measurements depends heavily on image quality, which is influenced by aortic valve calcification and adequacy of the ultrasound window [7–9]. The results also depend on the patient’s haemoglobin level, heart rate and left ventricular function [7]. Thus, invasive confirmation is often still necessary preceding valve replacement surgery. For this purpose, cardiac catheterization evaluating AVA by applying the Gorlin formula is available [10]. However, this method may not give reliable results, depending on cardiac function and aortic regurgitation [11]. Aforementioned techniques rely on and are affected by physiological parameters. Moreover, catheterization is an invasive procedure that may be associated with serious complications [12]. Therefore, new techniques are necessary to evaluate the severity and progression of aortic sclerosis. These new techniques could measure the anatomical AVA, which measurement is independent of physiological parameters.
Contrast-enhanced computed tomography is a useful technique to evaluate cardiac structures, like coronary arteries and aortic valves [13]. EBT, a non-mechanical computed tomography scan with a high temporal resolution (50–100 ms), is widely used to assess cardiac calcification, which is also an early marker of the aortic sclerotic process [1, 2]. To assess cardiac calcification, administration of contrast agent to the patient is not necessary. Several studies have shown the usefulness of assessing the degree of aortic valve calcification (AVC) with EBT as a measure of the severity of aortic valve sclerosis [14–17]. However, these studies did not use EBT to measure the AVA. MacMillan et al. were the first to use contrast-enhanced EBT to measure the AVA [18]. They were able to determine AVA in six patients with known aortic sclerosis. We wished to compare EBT and TTE in order to compare the assessment of aortic stenosis with both methods. We hypothesize that EBT is an useful method to evaluate aortic stenosis; both in terms of AVA as well as AVC.
Methods
Between June 2004 and February 2006, patients with a known peak gradient (≥ 30 mmHg) across their native aortic valve were invited to participate in the study during their control visit to the outpatient clinic of cardiology. All patients were under control for valvular aortic stenosis. The study was in accordance with principles that have their origin in the Declaration of Helsinki, and was approved by the local review board and all patients gave their informed consent. All patients were over 18 years of age, and had to be able to lie flat and hold their breath for 20 s. Patients with congenital heart defects, renal insufficiency (serum creatinin >120 μmol/l), known contrast allergy or history of hyperthyroidism, thyroid cancer, Kahlers disease, myasthenia gravis, pheochromocytoma or mastocytosis were excluded. Patients with a body weight over 100 kg were also excluded because image quality of EBT is impaired in obese patients.
Electron beam computed tomography
Scans were performed with an EBT scanner (e-Speed, GE, San Francisco, CA, USA), in the dual 1.5 mm slice mode with an image acquisition time of 50 or 100 ms depending on patient size. First, non-enhanced EBT data were collected during end-inspiratory breath hold. The non-enhanced scan was ecg-triggered at 42% of the R–R interval. A set of 40 continuous axial coupes were obtained from under the trachea bifurcation to the heart. An Agatston calcification score of the aortic valve was obtained [19]. Area and volume of calcifications were calculated and summated. The localization and severity of AVC was graded using a scale of 1 to 6 according to Bahler et al. [20].
Second, contrast-enhanced EBT data were collected during end-inspiratory breath hold. To achieve optimal contrast enhancement, bolus time was determined for each patient individually before EBT scanning. Bolus timing, using 20 ml contrast agent, was done with the region of interest placed in the ascending aorta. The ecg-triggered multiphase scan was carried out with 120 ml contrast agent (Iomeron 400®, Bracco, Italy) followed by a saline bolus chaser. Contrast and saline bolus chaser were injected through a 18G venflon, placed in a cubital vein, with a flow rate of 4 ml/s. Ecg-triggered multiphase scanning started at 5% of the R–R interval, the first phase, and continued during systole till 50% of the R–R interval, the last phase. Depending on the patient’s heart rate and beam speed, measurements were performed during 7 to 10 phases.
The data were transferred to a workstation (GE Advanced Workstation, GE Medical, Waukesha, WI, USA) and reviewed by 1 observer (R D), blinded from echocardiographic results. An axial view of the aorta valve was created with a double oblique view. One oblique axis was put trough the aortic valve in the coronal view of the heart, see Fig. 1(A). The second oblique axis was set in the created oblique view, see Fig. 1(B). The AVA was than measured in six planes at several levels in the created axial view of the aortic valve, see Fig. 1(B). The plane with the smallest AVA was selected and measured three times using planimetry, see Fig. 1(C). The mean AVA was assessed in this way for each phase. The phase with the maximal mean AVA was selected and was considered to be the AVA of concern. This AVA was compared to TTE findings.
Fig. 1All patients underwent electron beam computed tomography. (A) One oblique axis was put trough the aortic valve in the coronal view of the heart. Based on this orientation an oblique view was created. (B) The second oblique axis was set in the created oblique view. Based on this orientation an axial view of the aortic valve was made. The aortic valve area (AVA) was than measured in six planes at several levels in the created axial view of the aortic valve. (C) The AVA was measured three times at each level in each phase using planimetry. The mean AVA was assessed for each phase. The phase with the maximal mean AVA was selected and was considered to be the AVA of concern
Transthoracic echocardiography
A complete TTE study was performed in all patients by an experienced sonographer, blinded for EBT, following a standard procedure (GE Vingmed Ultrasound Vivid Five, GE Medical Systems, Waukesha, WI). The diameter of the left ventricular outflow tract was measured from the parasternal long-axis view. Flow velocity in the left ventricular outflow tract was assessed by pulse-wave Doppler from the apical 4 chamber approach. Flow velocity across the aortic valve was measured in the apical 5 chamber view. The AVA was computed using the continuity equation [5]. The presence of aortic regurgitation was determined from standard images, and quantified as the short-axis area of the regurgitant jet as a percentage to the short-axis area of the left ventricular outflow tract, moreover standard images of the left ventricle were obtained to evaluate left ventricular function and hypertrophy. Left ventricular function was based on estimated ejection fraction and wall motion, and was graded as being normal if the ejection fraction ≥60%. Left ventricular dysfunction was mild if the ejection fraction was 45–60%, moderate if the ejection fraction 30–45%, or poor if the ejection fraction was <30%. To evaluate left ventricular hypertrophy, posterior wall and septal thickness were measured from the parasternal length axis.
Statistical analysis
For the measurement of the AVA by EBT, 3 manual tracings made by 1 observer (R D) were averaged. Results of continuous normally distributed variables are expressed as mean ± SD, results of continuous not normally distributed variables are expressed as median (range). The data were analyzed with the use of standard software (SPSS version 12.0.1, SPSS Institute, Chicago, IL, USA) on a PC. The Spearman’s and Pearson’s correlation coefficients (r) were used to establish the presence of linear relationships. To compare not normally distributed data, the Mann–Whitney U test was used. A Bland–Altman analysis was used to evaluate the agreement between the AVA assessed with EBT and TTE. A level of significance below 0,05 was defined as clinically significant.
Results
A total of 61 patients were screened for participation in the study. Nine females and 21 males (age 70.9 ± 10.0 years) were enrolled in the study. Eight patients were excluded because of renal insufficiency, 1 because of known contrast allergy, 1 because of body weight over 100 kg, and 4 patients declined to undergo an EBT. Another 17 patients were excluded because the AVA could not be assessed by TTE.
In the study group, the mean peak pressure gradient across the valve was 64 ± 21 mmHg. Ten patients had symptoms according to NYHA class I, 12 patients class II and 8 patients class III. Left ventricular end-diastolic diameter was 4.9 ± 0.6 cm, interventricular septal thickness was 1.2 ± 0.3 cm and posterior wall thickness was 1.1 ± 0.1 cm. Ninety percent of the patients had signs of left ventricular hypertrophy on TTE. Twenty two patients (73%) had normal left ventricular function, 6 patients (20%) had mild and 2 patients (7%) moderate left ventricular dysfunction. Mild aortic regurgitation was present in 8 patients (27%) and moderate aortic regurgitation in 2 patients (7%). The average time between TTE and EBT was 17 ± 12 days.
Aortic valve area
Mean AVA assessed with TTE was 0.99 ± 0.31 cm2. The median AVA measured with EBT was 1.34 (0.69–2.84) cm2. There was a significant correlation between AVA measured with EBT and AVA assessed with TTE (r = 0.60, P < 0.01), as shown in Fig. 2. The AVA measured with EBT was on average 0.51 ± 0.46 cm2 larger than the AVA assessed with TTE, which was confirmed with Bland–Altman analysis, see Fig. 2. The difference in AVA between EBT and TTE showed a not statistically significant trend to be larger in patients with mild aortic regurgitation than without aortic regurgitation (0.62 (0.23–1.22) cm2 and 0.33 (−0.14–1.52) cm2, respectively, P = 0.08). The difference in AVA did not differ between patients with normal left ventricular function and left ventricular dysfunction (0.45 (−0.14–1.52) cm2 and 0.43 (−0.03–1.26) cm2, respectively, P = 0.78)
Fig. 2By using electron beam computed tomography (EBT) planimetry the aortic valve area (AVA) can be measured. ( A) AVA measured with EBT correlate well with those assessed with transthoracic echocardiography (TTE) (r = 0.60, P < 0.01). (B) AVA measured with EBT is larger on average than AVA assessed with TTE, as is confirmed with Bland–Altman analysis, exhibiting a mean difference of 0.51 ± 0.46 cm2. – – – = 95% confidence interval
Aortic valve calcification
The median Agatston score of the aortic valve was 3363 (425–10230). There was a moderate, but significant, inverse correlation between the Agatston score of the aortic valve measured with EBT and AVA assessed with TTE (r = −0.38, P = 0.04), as shown in Fig. 3. In 17 patients with an AVA <1.0 cm2 the aortic valve was more calcified than in 13 patients with an AVA ≥ 1.0 cm2 (Agatston score 3879 (1192–10230) and 2447 (425–6378), respectively, P = 0.04).
Fig. 3Aortic valve calcification can be quantified with electron beam computed tomography using the Agatston score. The Agatston score correlates well with the aortic valve area (AVA) measured with transthoracic echocardiography (TTE)(r = −0.38, P = 0.04). – – – = 95% confidence interval
Discussion
EBT is an useful non-invasive method to evaluate aortic stenosis, both in terms of AVA and AVC. However, on average a larger AVA was measured with EBT than was assessed AVA with TTE. Indeed, this is not surprising since EBT determines the anatomical AVA, which is expected to be greater than the functional AVA assessed with TTE. Because of calcifications the valvular leaflets are less flexible, as a result the AVA will be irregularly shaped, hence the AVA becomes haemodynamically less efficient. This results in a discrepancy between the functional and anatomical AVA.
To our knowledge, the publication of MacMillan et al. is the first study investigating the usefulness of contrast-enhanced EBT to assess AVA [18]. In this study, 8 patients with known calcific aortic stenosis underwent EBT one day after cardiac catheterization. In six out of eight patients close agreement was found, within a 0.25 cm2 margin, between catheterization-derived AVA and AVA measured with EBT. In 1 patient the AVA could not be determined EBT and in 1 patient the AVA was overestimated by EBT with 0.60 cm2.
The results of our study are in line with those of MacMillan et al. Our study also showed that EBT measures larger AVA compared to TTE. The AVA is measured planimetrically with EBT, so it can be considered as the anatomical AVA. Our study as well as the study of MacMillan et al. compared this anatomical AVA to the functional AVA. This functional AVA is derived from haemodynamic characteristics of the aortic valve, (pressure changes and flow velocity changes) across the aortic valve. The functional AVA is supposed to yield the haemodynamic relevant AVA. However, using the continuity equation, the AVA is assumed to be circular, which does not reflect the actual morphology of the orifice of the stenotic valve, especially not in case of heavy calcification.
TTE is the principal diagnostic tool in routine clinical practice to assess the AVA, next to cardiac catheterization. To determine the AVA with TTE, the continuity equation is used [5, 6], which yields the functional AVA. However, this technique has its limitations. First, TTE is not suitable in every patient with aortic valve sclerosis. Poor sonographic windows and calcified deposits in the aortic valve and cardiac wall hamper the determination of the left ventricular outflow diameter. A small fault in the measurement of this diameter can seriously influence the calculated AVA, because this diameter is squared in the continuity equation. Moreover, if the ultrasound beam is not directed parallel to the velocity jet, peak transvalvular velocity may be underestimated. Hence the peak pressure gradient and AVA may be underestimated. Second, additional cardiac dysfunction can influence the outcome of TTE measurements. The flow velocity can be decreased in case of poor left ventricular function, even though there is an anatomical small AVA. This leads to an underestimation of the severity of the aortic stenosis. Furthermore, the severity of aortic stenosis can be overestimated in cases with concomitant aortic regurgitation. Consequently, the aortic valve stenosis is overestimated [7–9]. Our study also showed a larger difference in AVA between EBT and TTE in patients with additional mild aortic regurgitation EBT is not influenced by aortic regurgitation, therefore EBT is a more reliable technique, in those patients, to conclude about the severity of aortic stenosis.
The larger AVA measured by EBT compared to TTE and cardiac catheterization may be related to slice orientation. In order to get an axial view of the aortic valve, the image has to be rotated in two planes by the observer. If the selected axial view is not situated perpendicular to the aortic valve, the AVA is overestimated. Moreover, from this axial view the plane with the smallest AVA has to be selected. Although the temporal resolution of EBT is high, it is possible that the smallest AVA is situated between 2 planes. Hence, the AVA can be overestimated. Nevertheless, we addressed this problem by acquiring several slices at different levels of the aortic valve. This is necessary to minimize the potential of AVA overestimation because of imprecise localization.
In addition to the measurement of AVA, AVC can be quantified with EBT. Many studies have made clear that EBT is also effective in the evaluation of AVC [14–17, 21, 22]. The findings of these studies suggest that elevated AVC scores are a marker for the presence of aortic stenosis; our study underlined these findings. A diagnostic threshold value of 1000 Agatson units has a sensitivity of 93% to detect severe aortic stenosis (AVA < 1.0 cm2) according to Messika-Zeitoun et al. [16]. Rosenhek et al. concluded that the extent of AVC was a strong independent predictor of outcome [23]. In a study population of 126 patients with asymptomatic severe aortic stenosis, four-year event-free survival was 75% in patients with mild AVC, compared to 20% in patients with moderate or severe AVC.
As mentioned above, the grade of AVC is a predictor of outcome in patients with aortic stenosis. Although the echocardiographic severity of aortic stenosis is correlated to AVC score, the AVA can not be predicted from the AVC score. So, to evaluate the severity of aortic valve stenosis and to indicate whether aortic valve replacement surgery is necessary it is inevitable to measure AVA. In our study, we showed a significant correlation between the AVA measured with EBT and the severity of aortic stenosis assessed with TTE. However, further research is needed to validate EBT as a technique to evaluate the AVA.
Next to EBT, there are other non-invasive imaging techniques being evaluated for their value in AVA assessment. Alkadhi et al. compared 16 detector row computed tomography to tranesophageal echocardiography in the evaluation of AVA in 20 patients with and 20 patients without aortic stenosis [24]. They reported a mean difference of 0.06 cm2. Reant et al. performed a similar study using magnetic resonance imaging (MRI) [25]. They enrolled 39 patients with aortic stenosis and compared the AVA measured with MRI to tranesophageal echocardiography and cardiac angiography. Their results suggest good correlation between techniques; MRI and transesophageal echocardiography differed 0.01 cm2, MRI and cardiac angiography differed 0.06 cm2. Both studies show that non-invasive imaging techniques are a good possible alternative for the evaluation of aortic valve stenosis.
Conclusion
EBT appears to be valuable as a non-invasive method to assess the severity of aortic valve sclerosis. EBT holds the qualifications for accurate assessment of the anatomy, morphology and physiology of the aortic valve. However, further investigations will have to be done to verify whether EBT is a suitable non-invasive imaging technique for evaluation of aortic valve disease.

electron beam computed tomography
echocardiography
aortic valve stenosis