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Keywords:

  • flow phantom;
  • machine settings;
  • power Doppler angiography;
  • three-dimensional ultrasound;
  • VOCAL

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. References

Objectives

Three-dimensional (3D) ultrasound is being used increasingly to acquire and subsequently quantify power Doppler data within the clinical setting. One proprietary software package calculates three 3D vascular indices: the vascularization index (VI), the flow index (FI), and the vascularization flow index (VFI). Our aim was to evaluate how different settings affect the Doppler signal in terms of its quantification by these three indices within a 3D dataset.

Methods

A computer-driven ‘flow phantom’ was used to continuously pump a nylon particle-based blood mimic (Orgasol) around a closed system through a C-flex tube embedded in an agar-based tissue mimic. The test tanks were insonated with a modified 3D transvaginal 4–8-MHz ultrasound transducer (V530D) and power Doppler data were acquired over a series of different settings. Each experiment involved the manipulation of just one Doppler setting in order to study it in isolation.

Results

As expected, all of the power Doppler settings, when altered, were found to effect significant changes (P < 0.05) in the VI, FI and VFI. The gain and signal power had the greatest effect, producing no Doppler signals at the lowest settings and the highest recordable indices at the maximum settings. The pulse repetition frequency (PRF) was the next most influential setting but a Doppler signal was seen and measurable at all of the different settings. The other Doppler settings had a much less profound effect on the vascular indices, with subtle but significantly different measures across the full range of settings. The speed of data acquisition was also found to affect the vascular indices, all of which were reduced when the fast mode was used although the only significant effect was on the VFI.

Conclusions

The VI, FI and VFI are all affected significantly by variations in power Doppler settings and by the speed of acquisition. The gain and signal power have the greatest effect on the power Doppler signal, followed closely by the PRF. The other settings and speed of acquisition also influence the signal, but to a much lesser degree. It is essential to maintain Doppler settings if any meaningful comparisons are to be made within and between subjects. Copyright © 2008 ISUOG. Published by John Wiley & Sons, Ltd.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. References

Quantitative three-dimensional (3D) power Doppler angiography represents the acquisition and measurement of power Doppler data within a 3D dataset1. Increasingly, this technique is being used to compare different patient populations and changes within the same population2. The majority of these studies use the ‘histogram’ facility (GE Medical Systems, Zipf, Austria), which displays the distribution of the power Doppler data and uses specific algorithms to derive three indices of blood flow within a volume of interest (VOI) defined by the user3. The vascularization index (VI) represents the relative proportion of color voxels to gray voxels, the flow index (FI) represents the mean power Doppler signal intensity value, and the vascularization flow index (VFI) is a combination of the two, calculated by multiplying the values together and dividing the result by 1004.

These 3D vascular indices depend on, and relate to, the total and relative amounts of power Doppler information within the VOI and the intensity of the signals5. The power Doppler signal is dependent on the presence of blood flow within the tissue or organ being studied and its intensity is dependent on the number of scatterers or blood cells within the blood vessels. The intensity of the power Doppler spectrum is determined by several settings, all of which are designed to induce an increase or decrease in the signal, as required6. It is likely that these settings affect the 3D vascular indices, but the precise effects and their extent have yet to be determined.

Whilst many authors are aware of the importance of maintaining Doppler settings between subjects in the research setting, there are no published studies to confirm this or to evaluate the magnitude of any effect produced by changing settings. The objective of this study, therefore, was to use an ‘ultrasound flow phantom’, a Doppler test device that can be used to mimic the in-vivo setting and provide a stable environment in which to examine the effects of machine settings7, to determine how the 3D vascular indices generated by the histogram facility are affected by different power Doppler settings.

Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. References

The study involved the development of a flow-phantom test device and the subsequent acquisition and measurement of 3D power Doppler data over a range of machine settings.

Flow-phantom test device components

The test device consisted of a flow phantom and gear pump, blood mimicking fluid (BMF), and an ultrasound test tank, as described by Raine-Fenning et al.8 In contrast to this previous study, in which the ultrasound settings were kept constant throughout the experiment to allow evaluation of the effect of external parameters on the 3D vascular indices, in the present study we made serial changes in one Doppler setting whilst the remainder were maintained constant. All experiments were conducted in a single test tank, designed specifically for the study, which consisted of a perspex box (dimensions: 25 × 30 × 12 cm) with a 0.8-mm thick C-flex tube 4 mm in diameter (Cole-Palmer, Vernon Hills, IL, USA) running horizontally through the center of the tank through an agar-based tissue-mimicking material (TMM). The gear pump was used to circulate the BMF, based upon a suspension of nylon particles, through this system at the constant flow rate of 9.8 mL/s. Details of the composition and methods used to make the TMM and BMF are given in our previous study8.

Experimental design

The baseline power Doppler settings were maintained for all experiments as follows: frequency, medium central; quality, 9; density, 8; reject, 82; signal rise, 0.4; signal persistence, 0.4; frame rate, 12.9; wall motion filter, 141 Hz; pulse repetition frequency (PRF), 2.4 kHz; power, 4 dB; color gain, 36.8 dB. These settings were chosen as they reflected those in general use in the clinical setting at the time of the experiment. The baseline settings were maintained while one parameter was altered, in steps that were as small as the equipment permitted, over the following ranges: PRF, 0.5–5.1 kHz; power, − 14 to 4 dB; wall motion filter, 176–341 Hz; color gain, 24.8–44 dB; signal rise, 0.1–1.2; persistence, 0.1–1.2. In a final experiment the machine settings were kept constant and datasets were acquired using each of the three acquisition speeds: slow, medium and fast.

Data acquisition and analysis

The techniques of data acquisition and measurement have been described elsewhere8. Briefly, they comprised the acquisition of 3D power Doppler datasets across the range of settings defined above, and the subsequent measurement of these datasets to quantify the power Doppler information in each. All data were acquired with a Voluson 530 (GE Medical Systems, Zipf, Austria) ultrasound machine equipped with a 7.5-MHz transvaginal probe held in position by a clamp attached to a retort stand. After a steady flow rate through the flow phantom had been achieved and maintained for a minimum of 30 min, a central two-dimensional (2D) view of the vessel was obtained by moving the clamp stand. 4D View (GE Medical Systems) was used for the subsequent data analysis to calculate the VI, FI and VFI within each 3D dataset. For this, we used a similar technique to that described previously8, in which an orthogonal VOI was defined by maximizing the x-axis (length) and minimizing both the y-axis (height) and the z-axis (depth). The algorithms used to define the three indices were as follows:

  • equation image

where: g is the gray-scale value and c is the color value in the ultrasound image, both normalized to 0–100 (low intensity being 0 and high intensity being 100), hg(x) is the frequency of gray value x in the ultrasound image and hc(x) is the frequency of color value x in the ultrasound image.

The relationship between each individual Doppler setting and the 3D indices of vascularity (VI, FI and VFI) was examined by regression analysis using Microsoft Excel (Microsoft Corp., Redmond, WA, USA) and the type of relationship was determined with the Statistical Package for the Social Sciences (SPSS Release 10.1.4; SPSS Inc., Chicago, IL, USA).

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. References

Repeated measurements were found to be reliable for all of the parameters (Table 1). Apart from the speed of acquisition, changes in all of the power Doppler settings were found to effect significant changes (P < 0.05) in the VI, FI and VFI. Changes in gain had the greatest effect, followed by signal power and PRF; the other Doppler settings had a much less profound effect, with subtle but significantly different measurements across the full range of settings.

Table 1. Reliability of measurements of the vascularization index (VI), flow index (FI) and vascularization flow index (VFI)
Variable/ settingMeanSDCVICC (95% CI)
  1. CV, coefficient of variation; ICC, intraclass correlation coefficient; PRF, pulse repetition frequency; WMF, wall motion filter.

Power
 VI29.218%0.052%0.1810.9919 (0.9801–0.9971)
 FI68.4000.1270.1860.9903 (0.9784–0.9967)
 VFI22.0200.0200.0910.9970 (0.9951–0.9985)
PRF
 VI31.634%0.039%0.0940.9984 (0.9968–0.9996)
 FI69.7830.1200.1720.9914 (0.9790–0.9975)
 VFI27.1390.0270.1020.9975 (0.9955–0.9992)
WMF
 VI32.831%0.028%0.0870.9978 (0.9964–0.9979)
 FI67.9670.1560.2290.9873 (0.9689–0.9945)
 VFI22.5550.0160.0740.9989 (0.9984–0.9999)
Gain
 VI29.830%0.031%0.1040.9967 (0.9948–0.9982)
 FI66.7360.1410.2120.9881 (0.9687–0.9948)
 VFI19.9080.0300.1540.9950 (0.9911–0.9981)
Signal rise
 VI33.897%0.035%0.1040.9976 (0.9959–0.9991)
 FI63.5910.1190.1880.9923 (0.9812–0.9979)
 VFI22.9310.0470.2060.9889 (0.9712–0.9956)
Persistence
 VI34.496%0.026%0.0760.9985 (0.9975–0.9993)
 FI66.38470.1110.1670.9921 (0.9721–0.9969)
 VFI23.7250.0310.1300.9959 (0.9917–0.9984)

Gain (Figure 1)

An increase in gain was associated with a significant increase (P < 0.05) in all three vascular indices. There were no Doppler signals at the lowest gain setting and the highest recorded indices were at the maximum setting (range of VI, 0–57%; range of FI, 0–70; range of VFI, 0–39). Both VI and VFI became apparent only at a gain setting of 28 dB and then increased in a linear manner to the highest gain setting of 44 dB, with no evidence of a plateau or signal saturation (Figures 1a and c). In contrast, the FI was quantifiable at an earlier stage, a gain setting of 26.4 dB, and increased more rapidly initially, before beginning to plateau at a gain setting of 40 dB, with a curvilinear relationship evident overall (Figure 1b).

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Figure 1. Effect of color gain on the vascularization index (a), flow index (b) and vascularization flow index (c). Mean and standard error are shown.

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Power (Figure 2)

The signal power was the next most influential parameter, again generating no recordable Doppler signals at the lowest setting and relatively high values at the highest setting (VI, 0–32%; FI, 0–69; VFI, 0–22). The maximum power achievable with the other Doppler settings used in these experiments was 4 dB. The power was then decreased in the smallest steps possible, to generate 12 different settings, the lowest power being − 14 dB. No power Doppler signals were evident subjectively or on application of the histogram, however, from a power of − 8 dB downwards.

There was a significant decrease (P < 0.05) in the VI and VFI with a reduction in power (Figures 2a and c). The initial drop, between 4 and 2 dB, was less marked than was the more linear drop with further reductions in power, with a 50% fall in both indices seen for every 4 dB decrease in power. The FI also decreased with serial reductions in power (P < 0.05), but much more gradually, in a way best described by a cubic relationship (Figure 2b). In contrast to the other two indices, the FI remained readily quantifiable at a power of − 6 dB.

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Figure 2. The effect of power on the vascularization index (a), flow index (b) and vascularization flow index (c). Mean and standard error are shown.

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Pulse repetition frequency (Figure 3)

The PRF was the next most influential setting (VI, 31–42%; FI, 66–71; VFI, 21–30), but, unlike gain and power, a Doppler signal was observed and measurable at all of the different settings. The lowest PRF possible was 0.5 kHz, and the smallest possible serial increases were applied to generate ten different PRF values, with a maximum value of 5.1 kHz. However, the PRF could not be adjusted in isolation, as every increment was also associated with an increase in the wall motion filter (Table 2). In addition, the power was automatically increased at a PRF value of 1.5 kHz and then again at a PRF of 4.6 kHz. Both power and wall motion filter were themselves found to effect significant changes in the power Doppler signal (Figures 2 and 4). These changes were associated with a variation in frame rate that was successfully maintained in the other experiments. The relationship between PRF and each vascular index was complex, therefore, but remained significant, with increases in the PRF effecting a reduction in all three overall (Figure 3). There was a similar pattern to the relationship for all three indices, characterized by increments at a PRF of 1.5 kHz and then again at 4.6 kH,z when the associated increase in power occurred. In the absence of these peaks the overall relationship was generally linear.

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Figure 3. The effect of pulse repetition frequency on vascularization index (a), flow index (b) and vascularization flow index (c). Mean and standard error are shown.

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Figure 4. The effect of wall motion filter on the vascularization index (a), flow index (b) and vascularization flow index (c). Mean and standard error are shown.

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Table 2. Effect of pulse repetition frequency (PRF) on vascularization index (VI), flow index (FI) and vascularization flow index (VFI)
PRF (kHz)VI (%)FI (0–100)VFI (0–100)Power (dB)Frame rate (per s)WMF (Hz)
  1. Automatic increments in the power and wall motion filter (WMF) are seen as the PRF is adjusted, with associated variation in the frame rate.

0.541.63569.81628.601221.460
1.038.64168.65426.978220.967
1.541.93971.26529.881317.987
2.140.25970.75728.486319.4128
2.638.89169.78427.139315.3155
3.136.74669.35425.486317.8188
3.531.17966.63320.775318.5207
4.230.90966.41420.473312.8250
4.635.22168.27223.911412.9275
5.133.93667.08922.768413.9303

Wall motion filter (Figure 4)

Only five wall motion filter settings were possible with the PRF setting used. An increase in the wall motion filter was associated with a significant (P < 0.05) linear reduction in all three indices. The first increment, from a wall motion filter of 176 to 224 Hz, was associated with a change in the VI and VFI, but not the FI, although subsequent increments were. Compared with the other power Doppler parameters, changes in the wall motion filter were associated with the smallest effect, or percentage change, in the power Doppler signal.

Signal rise and persistence (Figures 5 and 6)

Eight different settings for each of signal rise and persistence, ranging from 0.1 to 1.2, were available within the power Doppler sub-menu. The percentage change in all three indices over the complete range of signal rise and persistence values was relatively small and comparable to the effect of the wall motion filter.

Delaying the time at which the Doppler signal first appeared, by increasing the signal rise, was associated with a decrease in all three vascular indices (P < 0.05). The relationship was relatively linear with the FI and VFI, while the VI decreased more slowly until a rise of 0.6, when there was a more sudden drop in VI before it levelled out at a value of 1.0 (Figure 5).

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Figure 5. The effect of signal rise on the vascularization index (a), flow index (b) and vascularization flow index (c). Mean and standard error are shown.

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Increasing the persistence of the signal was associated with a small but significant increase in all three vascular indices (Figure 6). The relationship between the VI and VFI and signal persistence was less linear than the relationship with signal rise and was characterized by a fall in these indices with the first and last increments of signal persistence.

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Figure 6. The effect of signal persistence on the vascularization index (a), flow index (b) and vascularization flow index (c). Mean and standard error are shown.

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Speed of acquisition (Figure 7)

There were three different speeds for data acquisition: fast, medium and slow. The speed of acquisition did not affect the VI or FI but was associated with a significant reduction in the VFI (P < 0.05). This fall appeared to specifically relate to use of the medium and fast sweep modes (Figure 7c). Despite the overall lack of statistical significance, use of the fast mode appeared to reduce both the VI and FI.

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Figure 7. The effect of acquisition speed on the vascularization index (a), flow index (b) and vascularization flow index (c). Mean and standard error are shown.

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Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. References

This is the first in-vitro phantom study to quantify the effect of different Doppler settings on the 3D vascular indices generated through the application of the histogram technique to quantify power Doppler data. There was a quantifiable effect for the majority of changes in any of the settings, highlighting the importance of strict maintenance of these settings for research and within the clinical setting if any two subjects are to be compared. These findings can be explained by considering the derivation of the power Doppler signal and how it can be manipulated and displayed for clinical use.

The majority of Doppler settings are adjustable to allow the observer to maximize the power Doppler display, dependent on the depth and flow characteristics of the area of interest. Any change in the settings will induce an increase or decrease in the Doppler signal, therefore, and, in agreement with theoretical predictions based on the physical properties of power Doppler and results from other in-vitro experiments, we observed an increase in the 3D vascular indices with increases in power and color gain, and decreases in these indices with increases in the PRF and wall motion filter. There was also an increase in the vascular indices when the signal rise was reduced and when the persistence of the signal was increased. This might be expected, as these values will lengthen the time that the signal is on-screen and the time of its capture during the 3D sweep.

While the integrated power spectrum displayed using power Doppler is proportionate to the total number of scatterers and their mean speed, the nature of this relationship is also affected by several other factors9. The autocorrelation function of power Doppler is proportionate not only to the physical properties of the BMF but also to the properties of the Doppler imaging system10. The findings in this experiment were expected, therefore, but the relationship between each of the three different 3D vascular indices and the various Doppler settings was more complex than that predicted. A fairly linear relationship was seen between VI and VFI and power, gain and wall motion filter, while the relationship was more curvilinear between FI and these indices, with a trend towards a plateau at higher FI values. PRF was associated with complex changes in all three indices, although this probably reflected the changes in power and wall motion filter that occurred automatically with adjustment of the PRF and were not modifiable. Both increases in power that occurred over the range of PRF values examined led to an increase in the vascular indices to values above those calculated at the two previous lower PRF values, suggesting that power outweighs the effect of PRF to some degree. The relationship between signal rise and persistence was also complex and more difficult to account for. VI was relatively stable over the first several increments in signal rise before falling suddenly at a value of 0.8, while, with an increase in signal persistence, it increased more linearly, albeit somewhat erratically. In contrast to the other parameters, FI had a more consistently linear relationship with both variables.

Jain et al.6 also demonstrated a negative effect of PRF and wall motion filter on the strength of the power Doppler signal, using an in-vitro left heart pulse duplicator system. The flow area derived from the power Doppler spectrum was reduced by successive increases in PRF and color filter, while it increased with a decrease in the frame rate. Yoon et al.7 used microspheres as BMF to examine the effect of flow velocity, wall filter, PRF and gain on power Doppler signal intensity and background noise, using computerized quantitative analysis. In keeping with our results, they reported that the intensity of both the flow signal and background noise was proportionate to flow velocity and gain but inversely proportionate to PRF and wall motion filter level. These parameters also appear to have an important influence on background noise and the potential for artifacts, as increases in both PRF and Doppler gain produced a high signal-to-noise ratio when the wall filter was kept constant, as did increments in the wall filter and gain at a constant PRF. This relationship was reversed when a high constant Doppler gain was maintained and the wall filter and PRF were reduced. Gudmundsson et al.11 also showed a significant effect of several instrument settings on the power Doppler display using offline analysis of signals derived following the digital conversion of data captured from video recordings of flow within a test device The most important variable was fluid flow velocity, although depth and angle of insonation were also found to be important determinants of signal intensity. However, in contrast to our findings, Doppler gain and power had limited influence. Mizushige et al.12 showed that the intensity of the power Doppler image was dependent on flow velocity and the wall motion filter under steady flow conditions within a single acrylic tube phantom. They used a saline solution containing 40% glycerol and cornstarch as the BMF, finding a high degree of correlation between the Doppler shift frequency, as measured by conventional pulsed wave Doppler, and the power Doppler image intensity at the center of the flow image. The power Doppler signal was also affected by the beam incident angle, which, along with velocity, was closely related to properties of the filter, suggesting this may be an important determinant at low velocity flow rates. While the wall motion filter did reduce the vascular indices as it was increased, the effect was less than that of many of the other variables, despite the use of the relatively low flow rate of 9.8 mL/s.

Most of these experiments examined 2D power Doppler data and very few have examined 3D power Doppler data, despite the fact that a flow phantom provides volumetric information. Li et al.13 reported a high degree of correlation between true flow rate and calculated flow rate using a combination of 3D ultrasound and 2D color Doppler in the assessment of flow convergence and quantification of regurgitant flow within an in-vitro flow system. Cloutier et al.14 used 3D power Doppler ultrasound to investigate the performance of different segmentation algorithms in the delineation of the lumen of a simulated, stenosed artery. They found that the wall motion filter, type of flow (steady or pulsatile) and flow rate all affected the accuracy of the power Doppler image in determining the degree of stenosis. Guo et al.15 used 3D power Doppler ultrasound to examine the degree of vessel stenosis in several wall-less vessel systems. The degree of stenosis was quantified with an overall accuracy of 8.3% and precision of 7.0% over a range of area reductions and different flow rates under both steady and pulsatile flow conditions. These studies used the 3D aspect more for spatial orientation than for volume calculation and none examined the 3D vascular indices calculated in our present study.

Quantification of the power Doppler signal within a 3D area using the histogram facility involves the detection and weighting of color voxels, otherwise known as 3D pixels, relative to the total volume being considered. VI reflects the relative proportion of color voxels within the user-defined area and FI their mean signal intensity. VFI represents a combination of these two measurements, obtained through their multiplication. These indices represent power Doppler information distributed within a volume during the period over which the acquisition was obtained. This introduces two significant variables: movement and time. Power Doppler is more sensitive to movement artifact than are other forms of Doppler imaging, but this may be limited by avoiding inappropriate transducer movement and by increasing the speed of data acquisition. When artifactual information is present it is usually readily distinguishable from true flow data by its non-physiological appearance. Time is more difficult to account for and, because all forms of 3D data acquisition involve the serial acquisition of 2D planes, the effect of the cardiac cycle must be considered. Data are being acquired during both systole and diastole, which produce different power Doppler maps; the power Doppler signal is increased during systole, due to vessel expansion and higher flow volume rates, and then reduces with diastole. In real time, the pulsatile nature of blood flow within the uterus is readily evident. However, during 3D data acquisition, the pulsatile effect is less apparent and appears ‘averaged’ throughout the sweep of the ultrasound beam. In this study, the fast sweep mode was actually associated with a significant fall in VFI relative to the medium and slow sweep speeds. It is possible, therefore, that the degree of any averaging of cardiac activity is reduced as the acquisition speed is increased. This is totally hypothetical, of course, and must be considered against the fact that only continuous flow was used in our study. Also, we used a Voluson 530 ultrasound machine, one of the first machines in this series. The newer Voluson systems (Voluson Pro, Expert and E8) offer improved power Doppler sensitivity and may further accentuate the effect of different Doppler settings, but should reduce the loss of information with faster acquisition speeds as more information is obtained within any acquired volume. Future work examining the effect of acquisition speed and variable pulsatile flow rates is warranted, particularly as this reflects the physiological environment in which 3D power Doppler is being applied clinically.

Another interesting observation was the generation of an FI value when no VI value was returned. This was evident during the gain experiment, when a FI of 10 was seen in association with a VI of 0 at a gain setting of 26.4 dB. We have consulted the GE Medical Systems Research and Development team in Kretz, who confirmed that this may occur when the number of color voxels is very low. Under these circumstances, the VI is displayed as 0 because the Doppler signal is below the precision of the display and cannot be seen. Power Doppler information is there, however, and an FI can be generated from this. There are other, more obvious, limitations to this study. We deliberately used a simple design for the flow phantom as this was intended as a preliminary study and we wanted to focus on machine settings only. We did not examine the effect of pulsatile flow or look at more complex arrangements such as multiple vessels with variable diameters. This will be the subject of future work which will also consider the effect of different ultrasound machines and flow phantom devices and designs. While our conclusions are therefore limited, and must be taken in the context of the study design, our results show that the 3D vascular indices are mostly affected in a predictable manner by serial adjustments in any Doppler parameter, emphasizing the importance of maintaining and reporting machine settings.

In conclusion, in this study we quantified the effect of different Doppler settings on the vascular indices obtained through the quantification of 3D power Doppler data and demonstrated that significant changes occur with subtle changes in these settings. We have shown how the vascular indices are affected by serial changes in different Doppler settings and that the indices follow a fairly uniform and predictable pattern with such changes. This work emphasizes the importance of maintaining Doppler settings between subjects to facilitate intersubject and intrasubject comparison within the research setting and clinical environment. Futher work is required to examine how pulsatile flow and more complex vessel arrangements affect these indices.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. References
  • 1
    Ritchie CJ, Edwards WS, Mack LA, Cyr DR, Kim Y. Three-dimensional ultrasonic angiography using power-mode Doppler. Ultrasound Med Biol 1996; 22: 277286.
  • 2
    Raine-Fenning N. The role of three-dimensional ultrasound in assisted reproduction treatment. Ultrasound Obstet Gynecol 2004; 23: 317322.
  • 3
    Raine-Fenning NJ, Campbell BK, Clewes JS, Kendall NR, Johnson IR. The reliability of virtual organ computer-aided analysis (VOCAL) for the semiquantification of ovarian, endometrial and subendometrial perfusion. Ultrasound Obstet Gynecol 2003; 22: 633639.
  • 4
    Pairleitner H, Steiner H, Hasenoehrl G, Staudach A. Three-dimensional power Doppler sonography: imaging and quantifying blood flow and vascularization. Ultrasound Obstet Gynecol 1999; 14: 139143.
  • 5
    Rubin JM. Power Doppler. Eur Radiol 1999; 9((Suppl 3)): S318S322.
  • 6
    Jain SP, Fan PH, Philpot EF, Nanda NC, Aggarwal KK, Moos S, Yoganathan AP. Influence of various instrument settings on the flow information derived from the power mode. Ultrasound Med Biol 1991; 17: 4954.
  • 7
    Yoon DY, Choi BI, Kim TK, Han JK, Yeon KM. Influence of instrument settings on flow signal and background noise in power Doppler US. An experimental study using a flow phantom with hyperechoic background. Invest Radiol 1999; 34: 781784.
  • 8
    Raine-Fenning NJ, Nordin NM, Ramnarine KV, Campbell BK, Clewes JS, Perkins A, Johnson IR. Determining the relationship between three-dimensional power Doppler data and true blood flow characteristics: an in-vitro flow phantom experiment. Ultrasound Obstet Gynecol 2008; 32: 540550.
  • 9
    Adler RS, Rubin JM, Fowlkes JB, Carson PL, Pallister JE. Ultrasonic estimation of tissue perfusion: a stochastic approach. Ultrasound Med Biol 1995; 21: 493500.
  • 10
    Chen JF, Fowlkes JB, Carson PL, Rubin JM, Adler RS. Autocorrelation of integrated power Doppler signals and its application. Ultrasound Med Biol 1996; 22: 10531057.
  • 11
    Gudmundsson S, Valentin L, Pirhonen J, Olofsson PA, Dubiel M, Marsal K. Factors affecting color Doppler energy ultrasound recordings in an in-vitro model. Ultrasound Med Biol 1998; 24: 899902.
  • 12
    Mizushige K, Ueda T, Yuba M, Seki M, Ohmori K, Nozaki S, Matsuo H. Dependence of power Doppler image on a high pass filter instrumented in ultrasound machine. Ultrasound Med Biol 1999; 25: 13891393.
  • 13
    Li X, Shiota T, Delabays A, Teien D, Zhou X, Sinclair B, Pandian NG, Sahn DJ. Flow convergence flow rates from 3-dimensional reconstruction of color Doppler flow maps for computing transvalvular regurgitant flows without geometric assumptions: An in vitro quantitative flow study. J Am Soc Echocardiogr 1999; 12: 10351044.
  • 14
    Cloutier G, Qin Z, Garcia D, Soulez G, Oliva V, Durand LG. Assessment of arterial stenosis in a flow model with power Doppler angiography: accuracy and observations on blood echogenicity. Ultrasound Med Biol 2000; 26: 14891501.
  • 15
    Guo Z, Fenster A. Three-dimensional power Doppler imaging: a phantom study to quantify vessel stenosis. Ultrasound Med Biol 1996; 22: 10591069.