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AJR 2004; 182:1313-1317
© American Roentgen Ray Society

Measurement of Vascular Diameter In Vitro by Automated Software for CT Angiography: Effects of Inner Diameter, Density of Contrast Medium, and Convolution Kernel

Shigeru Suzuki1, Shigeru Furui, Tatsuro Kaminaga and Teiyu Yamauchi

1 All authors: Department of Radiology, Teikyo University School of Medicine, 2-11-1 Kaga, Itabashi-ku, Tokyo 173-8605, Japan.

Received September 19, 2003; accepted after revision November 7, 2003.

 
Address correspondence to S. Suzuki.


Abstract
Top
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 References
 
OBJECTIVE. This investigation was performed to evaluate the accuracy of diameter measurement of vessels in vitro by automated software for CT angiography.

MATERIALS AND METHODS. Vascular models with three inner diameters ({approx} 3, 4, and 6 mm) filled with contrast medium of three different densities ({approx} 460, 350, and 210 H) were scanned with helical CT. Five convolution kernels (soft, standard, detail, bone, and lung) were used. We evaluated the measurement error, defined as the difference between the diameter measured by the automated software and the true inner diameter of the vascular model. Statistical analysis involved three-way analysis of variance with repeated measures.

RESULTS. Significant differences occurred in measurement error among the three vascular model inner diameters, among the three densities of intravascular contrast medium, and among the five convolution kernels (p < 0.01). In all the convolution kernels except lung, measurement errors progressively decreased with higher densities of intravascular contrast medium (p < 0.01). In vascular models filled with contrast medium of 350 H, measurement errors were significantly smaller in soft (mean ± standard deviation [SD], 0.29 ± 0.16 mm) and bone (0.23 ± 0.05 mm) than in other convolution kernels (p < 0.01).

CONCLUSION. The accuracy of diameter measurement was affected by the vascular model inner diameter, the density of contrast medium, and the convolution kernel. A higher density of intravascular contrast medium and selection of the proper convolution kernel will improve accuracy.


Introduction
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 References
 
Clinical applications of CT angiography have increased recently. Generally, CT angiography has been performed using such display techniques as cross-sectional, multiplanar reconstruction, maximum intensity projection, shaded-surface display, and volume rendering [1–26]. The accuracy of vascular measurement with these reconstruction methods has been evaluated in the literature. However, the measurement was operator-dependent because it was accomplished without automated software. Automated software is necessary to decrease operator dependence and improve objectivity. Although some automated software has been provided on CT workstations and will become an accepted method for vascular measurement, a few authors have reported data that focus on the accuracy of diameter measurement by automated software for CT angiography [27, 28].

In this study, we evaluated the accuracy in diameter measurement in vitro by automated software for CT angiography and assessed the effect of the vascular model inner diameter, intravascular density of contrast medium, and convolution kernel. To our knowledge, this study is the first to assess the effect of convolution kernel on the accuracy of the measurement.


Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 References
 
Phantom Design
For the vascular models, we used acrylic cylinders (attenuation: mean ± standard deviation [SD], 114 ± 4 H) with three inner diameters ({approx} 3, 4, and 6 mm) filled with contrast medium diluted to three densities (high, intermediate, and low). The cylinder walls were approximately 1 mm thick. The inner diameters of the cylinders were measured 10 times with a micrometer to the nearest twentieth of a millimeter. The average inner diameters were 3.0, 4.0, and 5.9 mm. For the contrast medium, we used iohexol (300 mg I/mL). The attenuation values of the contrast medium of the three densities were 462 ± 8 H, 346 ± 5 H, and 205 ± 4 H.

Two physical phantoms were used. The first one, called P1, was made of six acrylic cylinders with a 3- or 4-mm inner diameter filled with contrast medium of all three densities. The six cylinders were fixed in a columnar styrene container (diameter, {approx} 5 cm) filled with salad oil (attenuation value, –120 ± 4 H). The cylinders were located parallel to the central axis of the styrene container. We fixed the styrene container in a water-filled columnar polyethylene container (diameter, {approx} 10 cm) with their central axes overlapping (Fig. 1). The second phantom, called P2, was made of three acrylic cylinders with a 6-mm inner diameter filled with contrast medium of all three densities. The three cylinders were fixed in a columnar styrene container inside a columnar polyethylene container in the same way as with P1.



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  		Fig. 1. —Schema of P1 phantom. P1 phantom was made of six acrylic cylinders with 3- or 4-mm inner diameters filled with contrast medium of three dilutions. Six cylinders were fixed in columnar styrene container filled with salad oil. Cylinders were placed parallel to central axis of styrene container. Styrene container was fixed in water-filled columnar polyethylene container, with central axes overlapping.

 

Helical CT
We scanned each phantom, overlapping the central axis of the columnar container of the phantoms on the axis of the gantry rotation. Single-detector helical CT was performed with a HiSpeed Advantage SG (General Electric Medical Systems). Parameters were 1-mm collimation, pitch of 1.0, 1 sec per gantry rotation, 48.0-cm field of view, 200 mA, and 120 kV.

Measurement
The raw data were reconstructed in 9.8-cm field of view with a 512 x 512 pixel matrix at a 0.5-mm interval using five convolution kernels. The convolution kernels were soft, standard, detail, bone, and lung. According to the information provided by General Electric Medical Systems, contrast resolution tends to decrease in the following order: soft, standard, detail, lung, and bone. The reconstructed images were transferred to a workstation (Advantage Workstation, version 4.0; General Electric Medical Systems) and analyzed by automated software (Advanced Vessel Analysis, General Electric Medical Systems). Ten mean diameter measurements were obtained in all vascular models using the five convolution kernels.

The diameter measurement was composed of three main steps. In the first step, we defined the segment of interest by designating its starting and ending points in planar cross sections. In the second step, the centerline of the vascular model was automatically tracked between the two defined points. In the last step, diameter measurements were performed for the 10 points that we defined along the centerline. At each point, the area of the vascular model cross section was measured in the plane orthogonal to the centerline. The mean diameter was defined as the diameter of the circle that would have the same area as the vascular model cross section.

The measurement error was defined as the difference between the measured diameter minus the true inner diameter of the vascular model. Figures 2 and 3 show the cross section and CT attenuation profile of the 3- and 6-mm models filled with the contrast medium of intermediate density for the five convolution kernels. Figure 4 shows the cross section and CT attenuation profile of the 6-mm models filled with the contrast medium of low density for the five convolution kernels.



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  		Fig. 2. —Cross section (top) and CT attenuation profile (bottom) of 3-mm inner diameter model filled with contrast medium of intermediate density (350 H). Circle on cross section corresponds to contour recognized by automated software. Straight lines inside circle correspond to minimal and maximal diameters in cross section. Increments of attenuation profile curve are 2 mm. A = soft, B = standard, C = detail, D = bone, and E = lung convolution kernels.

 


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  		Fig. 3. —Cross section (top) and CT attenuation profile (bottom) of 6-mm inner diameter model filled with contrast medium of intermediate density (350 H). Circle on cross section corresponds to contour recognized by automated software. Straight lines inside circle correspond to minimal and maximal diameters in cross section. Increments of attenuation profile curve are 2 mm. A = soft, B = standard, C = detail, D = bone, and E = lung convolution kernels.

 


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  		Fig. 4. —Cross section (top) and CT attenuation profile (bottom) of 6-mm inner diameter model filled with contrast medium of low density (210 H). Circle on cross section corresponds to contour recognized by automated software. Straight lines inside circle correspond to minimal and maximal diameters in cross section. Increments of attenuation profile curve are 2 mm. A = soft, B = standard, C = detail, D = bone, and E = lung convolution kernels.

 

Statistical Analysis
Statistical analysis involved three-way analysis of variance with repeated measures to assess interactions among combinations of factors and main effects of single factors. The Student's t test for paired samples was used to compare parameters, with a p value of less than 0.01 considered to represent a statistically significant result.


Results
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 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 References
 
The effects of the vascular model inner diameter and convolution kernel on the measurement error in each intravascular density of contrast medium are shown in Figures 5, 6, 7.



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  		Fig. 5. —Bar graph shows effect of vascular model inner diameter and convolution kernel on measurement error in 210-H model. Bars from left to right in each group indicate 3-mm, 4-mm, and 6-mm inner diameter models.

 


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  		Fig. 6. —Bar graph shows effect of vascular model inner diameter and convolution kernel on measurement error in 350-H model. Bars from left to right in each group indicate 3-mm, 4-mm, and 6-mm inner diameter models.

 


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  		Fig. 7. —Bar graph shows effect of vascular model inner diameter and convolution kernel on measurement error in 460-H model. Bars from left to right in each group indicate 3-mm, 4-mm, and 6-mm inner diameter models.

 

Significant differences were detected in measurement error among the three vascular model inner diameters, among the three densities of intravascular contrast medium, and among the five convolution kernels (Table 1). Because significant interactions were present among all combinations of the factors, the Student's t test for unpaired samples was used to compare parameters.


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  		TABLE 1 Interactions Among Combinations of Factors and Main Effects of Factors: Three-Way Analysis of Variance with Repeated Measures

 

Effect of Densities of Intravascular Contrast Medium
In all the convolution kernels except lung, measurement errors progressively decreased with higher densities of the intravascular contrast medium (p < 0.01). In these four convolution kernels, the absolute measurement errors were more than 0.8 mm for low-density vascular models, less than 0.5 mm for intermediate-density vascular models, and less than 0.3 mm for high-density vascular models (Table 2). In lung, the absolute measurement errors progressively decreased with higher densities of the intravascular contrast medium only in the 3-mm inner diameter models. On the other hand, the absolute measurement errors in lung were about 2 mm in the 4- and 6-mm inner diameter models, regardless of the density of intravascular contrast medium (Figs. 5, 6, 7).


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  		TABLE 2 Overall Measurement Error for Each of Three Density Models and Each of Five Convolution Kernels

 

Effect of Convolution Kernels
For low-density vascular models, the overall measurement error increased in the following order: soft, standard, detail, lung, and bone (p < 0.01) (Table 2). The overall measurement error was 0.84 mm for soft and 1.00 mm for standard.

For intermediate-density vascular models, the overall measurement errors were significantly smaller for soft (mean error, 0.29 mm) and bone (mean error, 0.23 mm) than for the other convolution kernels (Table 2). No significant difference occurred between soft and bone, but the measurement errors were more affected by the inner diameter for soft than for bone (Fig. 6). The error range was 0.10–0.45 mm for soft and 0.18–0.28 mm for bone.

For high-density vascular models, the overall measurement error increased in the following order: soft, bone, standard, detail, and lung (p < 0.01) (Table 2). The absolute overall measurement error was 0.05 mm for soft and 0.19 mm for bone.


Discussion
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 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
References
 
Although automated software improves objectivity in vascular measurement on CT angiography, many factors are thought to affect the accuracy of the measurement. This study shows that the vascular inner diameter, intravascular density of contrast medium, and convolution kernel affect the accuracy of diameter measurement on phantoms.

Some investigators have proposed the optimal value for arterial contrast enhancement for CT angiography [16, 24, 29]. Claves et al. [24] found that attenuation values of 150 and 200 H produced the best results in vessel measurement without automated software on CT angiography, and a density of 100 H or greater than 250 H significantly increased the error. However, in our study with automated software, measurement errors progressively decreased with higher densities of the intravascular contrast medium in all the convolution kernels except lung. The automated software recognizes the vascular inner contour by application of watershed transformation. In this process, the original images are transformed into gradient images. The measurement error is probably affected by the slope of the profile curves corresponding to the vascular inner contour. We believe that the slope of the profile curve probably becomes progressively steeper with higher densities of intravascular contrast medium, and therefore the measurement accuracy is improved.

Standard is commonly used as the convolution kernel in vascular measurement with the automated software provided by General Electric Medical Systems. When standard was used as the convolution kernel, the overall measurement error was 1.00 mm for the 210-H models, 0.45 mm for the 350-H models, and 0.25 mm for the 460-H models in our study. These results may exert significant practical impact on the diameter estimation of small arteries.

In the 210-H models, the overall measurement error in soft was 0.84 mm and was significantly smaller in standard. When the density of intravascular contrast medium is not sufficient, measurement error may be decreased using a convolution kernel with more emphasis on contrast resolution such as soft.

In bone and lung, the measurement errors were approximately 2 mm in the 210-H models. Taking into account the wall thickness of the vascular models ({approx} 1 mm), the automated software seemed to measure the outer diameter with these convolution kernels (Fig. 4). The automated software probably missed the difference in attenuation values between the vascular wall and lumen with a convolution kernel that emphasizes edge recognition such as bone or lung.

In vascular models filled with contrast medium of 350 or 460 H, the overall measurement errors were significantly smaller in bone than in standard, as shown in Table 2. When the density of intravascular contrast medium is adequate, the automated software seems to recognize more exactly the vascular inner contour with bone than with standard, because the slope of the profile curve is steeper with the former convolution kernel (Figs. 2 and 3). However, these results may be affected by the thickness of the vascular wall.

Although lung also emphasizes edge recognition, the measurement errors in lung were larger than in the other convolution kernels. In the 4- and 6-mm diameter models, lung has such large overshoots on the profile curve that the automated software seems to recognize the plateau between the overshoots as the vascular lumen and underestimates the vascular diameter, as shown in Figure 3. In the 3-mm diameter models, the overshoots are close to each other, and no plateau exists between the overshoots, as shown in Figure 2. Overall, underestimation did not occur in the 3-mm diameter models.

In this study, soft was better than standard in vascular measurement for 350- and 460-H models. These results are difficult to explain by only the slope of the profile curve. These results may be affected by the thickness and density of the vascular wall and material around the vessel.

This study has some limitations. First, our CT scanner was a single-detector unit, although MDCT is generally used for CT angiography at present [30–33]. We intentionally used single-detector helical CT, considering that measurement accuracy would be affected by the "corn beam" artifact peculiar to MDCT.

Second, we only used automated software provided by General Electric Medical Systems. Recently, some automated software has been provided on CT workstations, and the difference in software probably affects the measurement accuracy. However, as mentioned previously, the accuracy of diameter measurement is probably affected by the slope of profile curves corresponding to the vascular inner contour. As a result, a higher density of intravascular contrast medium and use of the proper convolution kernel will improve the accuracy with another automated software.

Third, we did not sufficiently assess the effect of noise on measurement accuracy. The noise increases with convolution kernels that emphasize edge recognition and will affect the recognition of the vessel boundaries. Further limiting factors are vascular wall thickness, vascular orientation to the axial plane, and size of field of view. The next step will be to evaluate the effect of these factors on measurement accuracy.

In conclusion, the accuracy of diameter measurement using automated software for CT angiography is affected by the vessel inner diameter, intravascular density of contrast medium, and convolution kernel. A higher density of intravascular contrast medium and selection of the proper convolution kernel will improve the accuracy.


References
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 

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