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Initial Clinical Experience of Virtual Monoenergetic Imaging Improves Stent Visualization in Lower Extremity Run-Off CT Angiography by Dual-Layer Spectral Detector CT
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Original Investigation
Initial Clinical Experience of Virtual Monoenergetic Imaging Improves Stent Visualization in Lower Extremity Run-Off CT Angiography by DualLayer Spectral Detector CT Daming Zhang, MD, Yanting Xie, MD, Yining Wang, MD, Ning Guo, PhD, Yun Wang, Bachelor, Zhengyu Jin, MD, Huadan Xue, MD
Abbreviations CNR contrast-to-noise ratio CTDIvol CT dose index volume DECT dual-energy CT DLP dose-length product ICC intraclass correlation coefficient IQ image quality keV kiloelectron volt MDCTA multidetector computed tomography angiography PAD peripheral artery disease ROI region of interest
Rationale and Objectives: Virtual monoenergetic imaging (VMI) may improve stent visualization in lower extremity run-off computed tomography angiography. The purpose of this study was to evaluate the image quality (IQ) of stents and to determine the optimal kiloelectron volt (keV) level of VMI images for stent evaluation compared to conventional CT images. Materials and Methods: This study included 32 patients with prior stent placement who underwent run-off computed tomography angiography on a dual-layer spectral detector CT scanner. Thirteen image series were evaluated for each stent, including conventional CT and 12 VMI datasets from 40 keV to 150 keV obtained in 10-keV intervals. Attenuation, SD, contrast-to-noise ratio, and signal-tonoise ratio of the native vessel and the vessel with a stent were evaluated. The diameter of the stent was measured in all 13 image series. The IQ was evaluated by two readers using a five-point scale (1 = poor IQ, 5 = excellent IQ). Results: A total of 39 stents in 29 patients were evaluated. Compared to conventional CT, attenuation of the native vessel and the vessel with a stent was higher at 4060 keV, and the SD was equal or lower at 50150 keV. Based on the attenuation and SD of VMI images, the contrast-to-noise ratio and signalto-noise ratio were higher at 4070 keV, among which the highest ratios were obtained at 40 keV. The stent diameter was equal or larger at 60150 keV, and the lowest stent diameter underestimation occurred at 100 keV. The IQ was equal or higher, ranging from 60 to 100 keV in comparison with conventional CT, and the highest IQ score occurred at 90 keV. Conclusion: This quantitative and qualitative assessment of VMI images and conventional images indicated that IQ improvement and more accurate stent lumen evaluation on lower extremity run-off CT angiography can be achieved by dual-layer spectral detector CT. Key Words: Dual-energy CT; Peripheral artery disease; Virtual monoenergetic imaging. © 2019 Published by Elsevier Inc. on behalf of The Association of University Radiologists.
SNR signal-to-noise ratio VMI virtual monoenergetic imaging Acad Radiol 2019; &:1–8 From the Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 1# Shuaifuyuan, Dongcheng District, Beijing, China (D.Z., Y.W., Y.W., Z.J., H.X.); Department of Radiology, Fuwai Hospital Chinese Academy of Medical Sciences, Shenzhen, China (Y.X.); Clinical Research, Philips Healthcare, Beijing, China (N.G.). Received February 23, 2019; revised July 6, 2019; accepted July 22, 2019. Address correspondence to: Z.J. and H.X. e-mail: [email protected] © 2019 Published by Elsevier Inc. on behalf of The Association of University Radiologists. https://doi.org/10.1016/j.acra.2019.07.022
INTRODUCTION
L
ower extremity peripheral artery disease (PAD) is the third leading cause of atherosclerotic vascular morbidity after coronary heart disease and stroke (1). The number of patients with PAD increased by 23.5% in only a decade, occurring between 2000 and 2010 (2). The increase
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of PAD represents a major public health challenge. Endovascular revascularization for the treatment of patients with PAD has developed rapidly as a less invasive treatment option. Currently, multidetector computed tomography angiography is recommended as a diagnostic tool in patients with lower extremity PAD, as it is a noninvasive and an accurate modality (3). The advantages of computed tomography angiography (CTA) include fast speed, convenience, and the ability for cross-sectional imaging of the vessel and visualization of calcifications, clips, stents, and bypasses (4,5). However, in long-term patency follow-up after stent placement, some blooming and metallic artifacts may be present (5). Stent lumen visualization can be improved by many methods in multidetector computed tomography angiography, such as using iterative reconstruction (6), dedicated convolution kernels (7,8), and a virtual monoenergetic imaging (VMI) algorithm (911). VMI images at high kiloelectron volt (keV) levels simulate increased penetration of the X-ray beam and are beneficial for reducing blooming and metallic artifacts (12). Mangold et al. evaluated the stent visualization in lower extremity run-off CTA using a VMI reconstruction algorithm in a third-generation dual-source dual-energy CT (DECT); in this study, it was found that 80 keV VMI images improved the image quality (IQ), diagnostic confidence, and accuracy of stent assessment in lower extremity CTA (13). Almutairi et al. concluded that 72 keV with 50% ASiR leads to better IQ for stent visualization in the peripheral artery when using DECT with a fast kilovoltageswitching mode (14). Recently, a dual-layer spectral detector CT has become commercially available. This system uses a detector-based dual energy separation technique and simultaneously acquires low and high energy projection data in two detector layers with the same spatial and temporal resolution (15), which favors beam-hardening correction (16), material decomposition (17), and image noise reduction on VMI images (1820). This system allows the use of dual-energy analysis on every data set acquired and does not require prospective patient selection for DECT (21). Several studies have reported that VMI reconstruction on dual-layer spectral detector CT improved IQ and reduced beam hardening artifacts of metal implants and coronary stents in vitro (15,22,23). However, the potential benefits of VMI reconstruction advances in the analysis of lower extremity vessels with a stent have not yet been evaluated. Therefore, the purpose of this study was to evaluate the value of VMI images on the lower extremity run-off CTA stent imaging performed by dual-layer spectral detector CT and to determine the optimal keV level of VMI imaging for stent evaluation.
MATERIALS AND METHODS Patient Population
This was a retrospective study, and the local institutional review board waived the informed consent. From September 1, 2016 to May 31, 2017, 32 patients (24 men and 8 women) 2
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with prior stent placement who underwent lower extremity run-off CTA on a dual-layer spectral detector CT scanner were enrolled in this study. Exclusion criteria were deviation from the standard scan protocol, insufficient arterial contrast enhancement, which was defined as attenuation of the distal abdominal aorta < 150 Hounsfield units (HU) and severity stenosis in stent >20%. CT Acquisition Parameters
All examinations were performed on a dual-layer spectral detector CT scanner (IQon, Philips Healthcare, USA). The scan length was defined from the distal abdominal aorta to the toes, and the scans were conducted in the cranio-caudal direction. Arterial enhancement was achieved with 90 mL of iodinated contrast agent (370 mgI/mL, Iopromide, Ultravist, Bayer Healthcare, Germany) administered intravenously at a flow rate of 4 mL/seconds. Scans were triggered with a bolus tracking technique; a region of interest (ROI) was placed on the distal abdominal aorta, and the scan automatically initiated after 6 seconds when a threshold of 150 HU was achieved. Routine scanning parameters for lower extremity run-off CTA were adopted without modification, including slice collimation: 64 £ 0.625 mm, rotation time: 0.27 seconds, pitch: 0.6, tube current: 125 mA, and tube voltage: 120 kVp. The CT dose index volume and dose-length product were recorded for each examination. The effective radiation dose was calculated based on the conversion factor reported by Saltybaeva et al. (24). Image Reconstruction
The display field of view was set to 250 mm with a pixel matrix of 512 £ 512. The slice thickness was 1.5 mm with an interval of 0.75 mm. Conventional images were reconstructed using iDose4 (Level 3, Philips Healthcare, USA) and a filter with Standard B. In addition, spectral base images were reconstructed using Spectral Recon (Level 3, Philips Healthcare, USA) and a filter with Standard B. All images were sent to a commercial workstation (IntelliSpace Portal 6.5, Philips Healthcare, Israel) for image evaluation. Image Analysis
Thirteen image series, including the conventional CT and 12 VMI datasets from 40 keV to 150 keV with 10-keV intervals, were reconstructed and evaluated by two readers with 3 years (X.Y.) and 6 years (Z.D.) of experience in vascular diagnosis, independently. All quantitative analyses were performed with a fixed window width of 1500 HU and a window center of 300 HU as previously recommended. The in-stent luminal diameter measurements were performed manually on the vertical plane (Fig 1) of arteries at the proximal (first 3 mm), middle, and distal levels (last 3 mm) of the stent using electronic diameter caliper (13). Every diameter was measured three times by two readers.
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VIRTUAL MONOENERGETIC IMAGING FOR STENT VISUALIZATION
Figure 1. The image example of slice selection for the measurement of attenuation, image noise, and stent diameter on conventional CT and virtual monoenergetic imaging (VMI) at virtual photon energies from 40 to 150 keV in 10 keV increments.
Mean diameter measurements and the mean underestimation of stent diameter versus the true stent diameter were calculated. In-stent luminal attenuation measurements were performed manually at the same sites as the measurements of instent luminal diameter by placing an ROI within the stent, avoiding vessel walls, plaques, stents, and blooming artifacts. ROIs were also placed in native vessels proximal to the stent (average distance of 5 mm). Measurements of adjacent muscle and fatty tissue at the same level as the stents were also performed to evaluate the contrast and noise. The standard deviation (SD) of the arterial attenuation was defined as vessel noise, and the SD in the surrounding fat was defined as image noise. The contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) were calculated for each measurement as follows: CNR ¼ HUartery HUmuscle =SDfat SNR ¼ HUartery =SDartery Qualitative measurements were rated by two observers who assessed attenuation, noise, and stent-related artifacts in each image dataset. All the axial slices, coronal slices, and sagittal slices, as well as three-dimensional reconstruction images, were used for qualitative image evaluation and the window level was freely adjusted. The IQ was rated from 1 to 5, as follows: 1 = poor IQ; 2 = suboptimal IQ; 3 = moderateIQ; 4 = good IQ; and 5 = excellent IQ.
Statistical Analysis
Commercially available software (SPSS 19.0) was used for the statistical analysis. A p value < 0.05 was considered as statistically significant. Continuous variables were expressed as the mean § SD. Objective measurements (attenuation, image noise, SNR and CNR, luminal stent diameter) for each dataset were pretested for a Gaussian distribution using the Kolmogorov-Smirnov test, and a paired t test was applied if the data had a normal distribution; otherwise, the Wilcoxon test was used. To compare the subjective IQ score, the Wilcoxon test was applied. Interobserver agreement for in-stent luminal diameter was assessed using an intraclass correlation coefficient analysis. For the subjective IQ score interobserver agreement assessment, the Kendall rank correlation coefficient was used. RESULTS Patient Population
A total of 32 patients were identified, and three patients were excluded due to stenosis of the stents >20%. Thus, a total of 29 patients (mean age, 60.4 § 9.8 years; 22 men, mean age 58.0 § 8.0 years, range 3373 years; 7 women, mean age 68.0 § 11.8 years, range 5083 years) were included. The indication for CTA was limb ischemia (n = 28: Fontaine Stage I, n = 2; Fontaine Stage II, n = 22; Fontaine Stage III, n= 3; Fontaine Stage
TABLE 1. The Stent Material, Brand, and Number Stent Material
Brand
Stainless steel
Express LD Iliac, Boston Scientific, Maple Grove, MN, USA Palmaz Genesis, Cordis Europa N.V., Roden, Netherlands Protege EverFlex Self-expanding Peripheral Stent System, Covidien, Plymouth, MN, USA Complete SE Iliac Stent System, Medtronic Inc., Santa Rosa, CA, USA Zilver Flex Vascular Self-Expanding Stent, Cook Medical Inc., Bloomington, IN, USA Astron, Biotronik GmbH Co., Berlin, Germany Zilver 635 Vascular Self-Expanding Stent, Cook Medical Inc., Bloomington, IN, USA PTA Balloon-Expendable Stent, Biotronik AG, Prague, Switzerland SMART Control Nitinol Stent System, Cordis Co., Miami, FL, USA Invatec ScubaCobalt Chromium Stent, Medtronic Inc., Santa Rosa, CA, USA
Nitinol
Cobalt chromium
Number 4 1 10 7 4 3 2 1 1 3
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4 TABLE 2. Attenuation, Image Noise, CNR, and SNR of Native Vessels and Stent Vessel Segments 120 kV
40 keV
50 keV 4
60 keV 4
70 keV 4
80 keV 5
90 keV 5
100 keV 5
110 keV 5
120 keV 5
130 keV 5
140 keV 5
150 keV 5
110.4 § 21.55 16.3 § 4.85 15.4 § 4.05 5.3 § 2.65 6.8 § 3.15 7.9 § 5.15 8.9 § 4.15
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Values are mean § standard deviation. CNR, contrast-to-noise ratio; keV, kiloelectron volt; SNR, signal-to-noise ratio. 4 Indicates the comparison with conventional CT images and that the attenuation, image noise, CNR, and SNR of VMI images were significantly increased. 5 Indicates the comparison with conventional CT images and that the attenuation, image noise, CNR, and SNR of VMI images were significantly decreased.
109.8 § 19.65
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Attenuation 331.9 § 72.9 915.9 § 223.7 607.4 § 142.4 425.4 § 95.2 317.1 § 67.3 248.4 § 49.7 203.8 § 38.8 173.6 § 31.8 152.3 § 27.2 136.9 § 24.2 125.4 § 22.1 116.6 § 20.7 native vessel Attenuation 327.1 § 86.8 990.4 § 215.54 652.9 § 138.64 455.1 § 93.94 336.5 § 66.8 260.8 § 50.05 214.6 § 40.05 180.1 § 34.05 156.9 § 29.95 139.9 § 26.45 137.8 § 66.35 127.7 § 62.45 in stent SD native 22.7 § 9.0 31.7 § 22.64 24.9 § 14.5 21.4 § 10.1 19.3 § 7.75 18.1 § 6.55 17.5 § 5.85 17.0 § 5.45 16.7 § 5.25 16.6 § 5.05 16.4 § 4.95 15.8 § 5.35 vessel SD in stent 21.4 § 6.6 33.3 § 15.04 25.0 § 10.04 20.8 § 7.3 18.6 § 6.35 17.3 § 4.95 16.5 § 4.75 16.1 § 4.45 15.8 § 4.35 15.6 § 4.25 15.5 § 4.15 15.4 § 4.05 CNR native 17.5 § 8.0 62.0 § 34.34 42.9 § 22.74 30.0 § 15.14 21.8 § 10.74 16.6 § 8.15 13.0 § 6.25 10.6 § 5.05 8.8 § 4.15 7.5 § 3.65 6.6 § 3.15 5.9 § 2.85 vessel CNR in 17.8 § 7.6 69.3 § 29.34 50.9 § 20.74 33.4 § 14.54 23.8 § 10.84 18.4 § 7.5 14.5 § 6.05 11.2 § 5.65 10.8 § 7.65 9.1 § 3.85 8.0 § 6.55 7.2 § 6.05 stent SNR native 18.7 § 13.5 47.8 § 46.64 35.7 § 32.84 26.8 § 23.24 21.1 § 16.94 17.1 § 12.85 14.4 § 10.75 12.2 § 8.35 10.8 § 7.25 9.8 § 6.65 8.9 § 5.65 9.4 § 7.65 vessel SNR in stent 21.1 § 13.4 47.0 § 30.24 37.2 § 21.24 29.3 § 15.54 23.5 § 11.74 18.7 § 8.0 16.5 § 7.85 14.0 § 6.55 12.4 § 6.05 11.2 § 5.45 12.0 § 14.05 11.2 § 12.95
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Figure 2. Comparison of attenuation, image noise, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) of native vessels and stent vessel segments between conventional CT and virtual monoenergetic imaging (VMI) at virtual photon energies from 40 to 150 keV with 10 keV increments.
IV, n = 1) and follow-up examination after recent iliac stent placement (n = 1). The mean body mass index was 24.7 § 3.3 kg/m2. The average CT dose index volume and dose-length product were 2.6 § 0.4 mGy and 342.1 § 57.4 mGy*cm, respectively. The effective radiation dose was 2.2 § 0.4 mSv. In 10 patients, two stents were present, resulting in a total of 39 stented vessel segments for analysis. Stent Characteristics
Information about stent material and size was available for 36/39 stents. Ten different stent types were present, and stent material was either stainless steel, nitinol, or cobalt chromium. The detail information was shown in Table 1. The mean stent diameter was 7.5 § 1.0 mm (range 59 mm). Stents were present in the iliac (n = 28) and femoral (n = 11) arteries. Image Quality Analysis
Results of the quantitative IQ analysis of attenuation, image noise, CNR, and SNR are shown in Table 2 and Figure 2. In general, the attenuation, image noise of native vessel, and in-stent lumen decreased as the VMI energy level increased. Compared to conventional CT, there was no statistical
difference of the attenuation at 70keV (p = 0.124) and image noise at 60 keV (p = 0.180) of in-stent lumen. Figure 2 showed that CNR and SNR in both native vessel and vessel segment with stent were higher at low VMI energy level (4070keV). The CNR (p = 0.209) and SNR (p = 0.489) of stent lumen equal to conventional CT at 80 keV. Interobserver agreement for luminal stent diameter measurements was good (intraclass correlation coefficient: 0.948). Compared to conventional CT, luminal stent diameters were increased at energy levels 80 keV (each p < 0.05). The measurement of stent diameter which was closest to real stent diameter was at 100 keV. The diameter underestimation at 100 keV was 25.53% (in all location), 24.7% (in iliac artery), and 28.5% (in femoral artery) separately. There was no significant difference at energy levels of 70 keV (p = 0.132) for iliac artery or 60 keV (p = 0.944) for femoral artery. The stent diameters are shown in Table 3 and Figure 3. In comparison to conventional CT, the subjective IQ score was rated higher between energy levels of 7090 keV (each p< 0.05). There were no significant differences in subjective IQ scores between conventional CT and VMI at 60 keV (p = 0.115) or 100 keV (p = 0.076). The result of IQ score is shown in Table 4. The interobserver agreement for IQ assessment was good (coefficient: 0.814, p< 0.001). 5
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DISCUSSION
Values are mean § standard deviation. The diameter underestimation was calculated based on the true stent diameter.
Stent diameter in all location 5.37 § 1.20 4.84 § 1.22 5.21 § 1.23 5.33 § 1.12 5.41 § 1.14 5.50 § 1.15 5.56 § 1.17* 5.56 § 1.15* 5.53 § 1.15 5.53 § 1.17 5.49 § 1.19 5.49 § 1.22 5.46 § 1.23 Stent diameter in iliac artery 5.61 § 1.18 5.03 § 1.26 5.45 § 1.15 5.60 § 1.14 5.74 § 1.18 5.83 § 1.19 5.88 § 1.22 5.88 § 1.23 5.86 § 1.23 5.84 § 1.24 5.82 § 1.23 5.82 § 1.24 5.80 § 1.23 Stent in femoral artery 4.10 § 0.64 3.72 § 0.64 4.03 § 0.62 4.10 § 0.64 4.20 § 0.67 4.26 § 0.66 4.29 § 0.70 4.31 § 0.69 4.25 § 0.71 4.24 § 0.73 4.21 § 0.72 4.18 § 0.72 4.14 § 0.71 Diameter underestimation 28.93 36.51 30.82 29.16 27.34 26.12 25.57 25.53 25.86 26.08 26.40 26.61 26.99 in all location (%) Diameter underestimation 28.1 36.0 30.1 28.4 26.5 25.3 24.7 24.7 25.0 25.3 25.5 25.7 26.0 in iliac artery (%) Diameter underestimation 32.2 38.5 33.4 32.2 30.4 29.3 28.7 28.5 29.1 29.1 29.7 30.2 30.7 in femoral artery (%)
80 keV 70 keV 60 keV 50 keV 40 keV 120 kV
TABLE 3. Stent Diameters and Diameter Underestimation in Conventional CT and VMI Images
90 keV
100 keV
110 keV
120 keV
130 keV
140 keV
150 keV
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This is the first study about lower extremity artery stent evaluation using dual layer spectral detector CT. In this study, the results showed that stent lumen evaluation under spectral CT needed consider different VMI energy level together and get the finest stent lumen illustration. In the current study, attenuation of native vessel and vessel segment with stent increased greatly as the energy level decreased, while the image noise varied mildly across different energy levels; the image noise at 40 keV and 50 keV was slightly larger than that at other energy levels, which led to a higher CNR from 40 to 80 keV and a higher SNR from 40 to 70 keV (Table 2). In other dual energy CT, including dual source scanners and rapid kVp-switching scanners, large noise in low-energy VMI limits its utility and diagnostic capabilities (2527). In the dual-layer CT system, due to perfectly registered spatial data, basis decomposition is performed in the projection domain. The anticorrelated noise is reduced by using an anticorrelated noise reduction algorithm in the reconstruction process (27). A direct benefit of the reduction of anticorrelated noise is the reduction of the noise variation across the energies of VMI, especially at low and high keVs. VMI images of stents from 40 keV to 150 keV were evaluated, and the image noise decreased as the VMI energy level increased. The results were comparable with a previous in vivo study on VMI images improving the IQ of the head (28), while the image noise was more homogenous to the results of in vitro studies on coronary stents (15) and hip prostheses (22). The difference in the image noise trend between the in vivo and in vitro studies may be explained by the deviation of the simulation of the in vitro study. Although the highest CNR and SNR for 12 VMI datasets were at 40 keV in our study, which benefit from the low image noise of dual-layer spectral detector CT, the relatively large blooming artifact at low energy levels may hinder the visualization of stent lumen. In this study, to evaluate the effect of the energy level on stent lumen visualization, the stent diameter, diameter underestimation, and subjective IQ were measured. For the stent lumen diameter evaluation, the smallest underestimation (in other words, the largest stent lumen diameter) was found at 100 keV no matter for iliac artery or femoral artery. Based on the above discussion, the illustration of peripheral artery stent was not only influenced by CNR and SNR for the lumen enhancement, but also by the stent beam hardening. For CNR and SNR, low VMI energy level which was equal or lower than 70 keV was better than conventional CT; while for beam hardening reduction, the stent diamerter underestimation results showed that high VMI energy level which was equal or higher than 80 keV was better than conventional CT. This result was consistent with a previous in vitro study conducted by Hickethier et al. on coronary stents, in which images at higher keV levels improved the visible stent diameter. However, Hickethier et al. (15) only conducted an objective assessment; thus, a comparison of the adequate keV level between peripheral and
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Figure 3. Comparison of stent diameters measured in conventional CT and virtual monoenergetic imaging (VMI) at virtual photon energies from 40 to 150 keV with 10 keV increments in iliac and femoral arteries.
coronary stents could not be completed here. CNR, SNR, and stent diameter were all objective. The IQ score was subjective and was rated by two observers who assessed attenuation, noise, and stent-related artifacts. The highest IQ score was at 90 keV in this study. The dual-layer detector spectral CT offered wide-range VMI with low image noise, and gave peripheral artery stent evaluation more operational VMI energy levels. Taking the objective and subjective stent evaluation results together, combined low VMI energy level to assess stent lumen enhancement, as well as high VMI energy level to reduce beam hardening effect seemed a comprehensive solution. Dual-layer spectral detector CT allowed for a retrospective dual-energy analysis (29), which was important for stent evaluation. Although it has been a while about the introduction of the DECT, the clinical application was restricted by the specific scan protocol. To detector based DECT, the scan protocol is same as routine CT scan, which means when the stent was shown in peripheral arteries, VMI images can be reconstructed without worrying lacking spectral data. This study has several limitations. First, the study population was relatively small, and the results should be
confirmed in a larger patient population. Second, the visibility of the stent lumen varies greatly depending on the type of stent and material used (7,30), but given the diversity of stent manufacturing and the small number of each stent material in our research, subgroups of different types of stents were not analyzed. Third, although the optimal keV level for peripheral artery stents was concluded at 90 keV based on the quantitative and qualitative assessment, the diagnostic accuracy of stent restenosis in 90 keV VMI images still needs to be confirmed by comparison with digital subtraction angiography. CONCLUSION In summary, VMI of dual-layer spectral detector CT improves the lower extremity artery stent visualization and it needs taking different VMI energy level together to make a comprehensive evaluation. The retrospective spectral analysis favors the clinical application of the detector based DECT. The diagnostic accuracy of stent restenosis discrimination, as well as the influence of VMI images on different material stents, still needs to be confirmed by further studies.
TABLE 4. Subjective IQ Scores for Conventional CT and VMI Images
IQ score
120 kV
40 keV
50 keV
60 keV
70 keV
80 keV
90 keV
100 keV
110 keV
120 keV
130 keV
140 keV
150 keV
3.38 § 0.38
1.88 § 0.32
2.46 § 0.42
3.24 § 0.41
3.74 § 0.35
4.01 § 0.25
4.04 § 0.14
3.58 § 0.44
3.15 § 0.26
2.51 § 0.47
2.29 § 0.40
2.07 § 0.21
2.01 § 0.08
Values are mean § standard deviation.
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ACKNOWLEDGMENTS This research was supported by the National Public Welfare Basic Scientific Research Program of Chinese Academy of Medical Sciences (2018PT32003 and 2017PT32004). All the authors have reported that they have no relationships relevant to the contents of this paper to disclose. REFERENCES 1. Aiman U, Haseen MA, Beg MH, et al. Profile of atherosclerotic risk factors and management in patients of peripheral arterial disease at a tertiary care teaching hospital of north India. Indian J Pharm Sci 2014; 76(6):504–509. 2. Fowkes FG, Rudan D, Rudan I, et al. Comparison of global estimates of prevalence and risk factors for peripheral artery disease in 2000 and 2010: a systematic review and analysis. Lancet 2013; 382(9901):1329–1340. 3. Kayhan A, Palabiyik F, Serinsoz S, et al. Multidetector CT angiography versus arterial duplex USG in diagnosis of mild lower extremity peripheral arterial disease: is multidetector CT a valuable screening tool? Eur J Radiol 2012; 81(3):542–546. 4. Norgren L, Hiatt WR, Dormandy JA, et al. Inter-Society consensus for the management of peripheral arterial disease (TASC II). J Vasc Surg 2007; 45(Suppl S):S5–S67. 5. European Stroke O, Tendera M, Aboyans V, et al. ESC guidelines on the diagnosis and treatment of peripheral artery diseases: document covering atherosclerotic disease of extracranial carotid and vertebral, mesenteric, renal, upper and lower extremity arteries: the Task Force on the Diagnosis and Treatment of Peripheral Artery Diseases of the European Society of Cardiology (ESC). Eur Heart J 2011; 32(22):2851–2906. 6. Funama Y, Oda S, Utsunomiya D, et al. Coronary artery stent evaluation by combining iterative reconstruction and high-resolution kernel at coronary CT angiography. Acad Radiol 2012; 19(11):1324–1331. 7. Maintz D, Burg MC, Seifarth H, et al. Update on multidetector coronary CT angiography of coronary stents: in vitro evaluation of 29 different stent types with dual-source CT. Eur Radiol 2009; 19(1):42–49. 8. Oda S, Utsunomiya D, Funama Y, et al. Improved coronary in-stent visualization using a combined high-resolution kernel and a hybrid iterative reconstruction technique at 256-slice cardiac CT-Pilot study. Eur J Radiol 2013; 82(2):288–295. 9. Mangold S, Cannao PM, Schoepf UJ, et al. Impact of an advanced image-based monoenergetic reconstruction algorithm on coronary stent visualization using third generation dual-source dual-energy CT: a phantom study. Eur Radiol 2016; 26(6):1871–1878. 10. Stehli J, Fuchs TA, Singer A, et al. First experience with single-source, dual-energy CCTA for monochromatic stent imaging. Eur Heart J Cardiovasc Imaging 2015; 16(5):507–512. 11. Wichmann JL, Gillott MR, De Cecco CN, et al. Dual-energy computed tomography angiography of the lower extremity runoff: impact of noiseoptimized virtual monochromatic imaging on image quality and diagnostic accuracy. Invest Radiol 2016; 51(2):139–146. 12. Machida H, Tanaka I, Fukui R, et al. Dual-energy spectral CT: various clinical vascular applications. Radiographics 2016; 36(4):1215–1232. 13. Mangold S, De Cecco CN, Schoepf UJ, et al. A noise-optimized virtual monochromatic reconstruction algorithm improves stent visualization and diagnostic accuracy for detection of in-stent re-stenosis in lower extremity run-off CT angiography. Eur Radiol 2016; 26(12):4380–4389.
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14. Almutairi A, Al Safran Z, AlZaabi SA, et al. Dual energy CT angiography in peripheral arterial stents: optimal scanning protocols with regard to image quality and radiation dose. Quant Imaging Med Surg 2017; 7 (5):520–531. 15. Hickethier T, Baessler B, Kroeger JR, et al. Monoenergetic reconstructions for imaging of coronary artery stents using spectral detector CT: invitro experience and comparison to conventional images. J Cardiovasc Comput Tomogr 2017; 11(1):33–39. 16. Maass C, Baer M, Kachelriess M. Image-based dual energy CT using optimized precorrection functions: a practical new approach of material decomposition in image domain. Med Phys 2009; 36(8):3818–3829. 17. van Hamersvelt RW, Willemink MJ, de Jong PA, et al. Feasibility and accuracy of dual-layer spectral detector computed tomography for quantification of gadolinium: a phantom study. Eur Radiol 2017; 27 (9):3677–3686. 18. Haneder S, Siedek F, Doerner J, et al. Thoracic-abdominal imaging with a novel dual-layer spectral detector CT: intra-individual comparison of image quality and radiation dose with 128-row single-energy acquisition. Acta Radiol 2018; 59(12):1458–1465. 19. Hickethier T, Byrtus J, Hauger M, et al. Utilization of virtual mono-energetic images (MonoE) derived from a dual-layer spectral detector CT (SDCT) for the assessment of abdominal arteries in venous contrast phase scans. Eur J Radiol 2018; 99:28–33. 20. Zhao XM, Wang M, Wu RZ, et al. Dual-layer spectral detector CT monoenergetic reconstruction improves image quality of non-contrast cerebral CT as compared with conventional single energy CT. Eur J Radiol 2018; 103:131–138. 21. Johnson TR. Dual-energy CT: general principles. Am J Roentgenol 2012; 199(5 Suppl):S3–S8. 22. Wellenberg RH, Boomsma MF, van Osch JA, et al. Quantifying metal artefact reduction using virtual monochromatic dual-layer detector spectral CT imaging in unilateral and bilateral total hip prostheses. Eur J Radiol 2017; 88:61–70. 23. Halpern EJ, Halpern DJ, Yanof JH, et al. Is coronary stent assessment improved with spectral analysis of dual energy CT? Acad Radiol 2009; 16(10):1241–1250. 24. Saltybaeva N, Jafari ME, Hupfer M, et al. Estimates of effective dose for CT scans of the lower extremities. Radiology 2014; 273(1):153–159. 25. Yu L, Christner JA, Leng S, et al. Virtual monochromatic imaging in dualsource dual-energy CT: radiation dose and image quality. Med Phys 2011; 38(12):6371–6379. 26. Yamada Y, Jinzaki M, Tanami Y, et al. Virtual monochromatic spectral imaging for the evaluation of hypovascular hepatic metastases: the optimal monochromatic level with fast kilovoltage switching dual-energy computed tomography. Invest Radiol 2012; 47(5):292–298. 27. Brown K, Zabic S, Shechter G. Impact of spectral separation in dualenergy CT with anti-correlated statistical reconstruction. In: In: Proceedings of the 13th fully three-dimensional image reconstruction in radiology and nuclear medicine; 2015. p. 491–494. 28. Neuhaus V, Abdullayev N, Grosse Hokamp N, et al. Improvement of image quality in unenhanced dual-layer CT of the head using virtual monoenergetic images compared with polyenergetic single-energy CT. Invest Radiol 2017; 52(8):470–476. 29. Rajiah P, Abbara S, Halliburton SS. Spectral detector CT for cardiovascular applications. Diagn Interv Radiol 2017; 23(3):187–193. 30. Almutairi A, Sun Z, Al Safran Z, et al. Optimal scanning protocols for dual-energy CT angiography in peripheral arterial stents: an in vitro phantom study. Int J Mol Sci 2015; 16(5):11531–11549.
Original Investigation
Initial Clinical Experience of Virtual Monoenergetic Imaging Improves Stent Visualization in Lower Extremity Run-Off CT Angiography by DualLayer Spectral Detector CT Daming Zhang, MD, Yanting Xie, MD, Yining Wang, MD, Ning Guo, PhD, Yun Wang, Bachelor, Zhengyu Jin, MD, Huadan Xue, MD
Abbreviations CNR contrast-to-noise ratio CTDIvol CT dose index volume DECT dual-energy CT DLP dose-length product ICC intraclass correlation coefficient IQ image quality keV kiloelectron volt MDCTA multidetector computed tomography angiography PAD peripheral artery disease ROI region of interest
Rationale and Objectives: Virtual monoenergetic imaging (VMI) may improve stent visualization in lower extremity run-off computed tomography angiography. The purpose of this study was to evaluate the image quality (IQ) of stents and to determine the optimal kiloelectron volt (keV) level of VMI images for stent evaluation compared to conventional CT images. Materials and Methods: This study included 32 patients with prior stent placement who underwent run-off computed tomography angiography on a dual-layer spectral detector CT scanner. Thirteen image series were evaluated for each stent, including conventional CT and 12 VMI datasets from 40 keV to 150 keV obtained in 10-keV intervals. Attenuation, SD, contrast-to-noise ratio, and signal-tonoise ratio of the native vessel and the vessel with a stent were evaluated. The diameter of the stent was measured in all 13 image series. The IQ was evaluated by two readers using a five-point scale (1 = poor IQ, 5 = excellent IQ). Results: A total of 39 stents in 29 patients were evaluated. Compared to conventional CT, attenuation of the native vessel and the vessel with a stent was higher at 4060 keV, and the SD was equal or lower at 50150 keV. Based on the attenuation and SD of VMI images, the contrast-to-noise ratio and signalto-noise ratio were higher at 4070 keV, among which the highest ratios were obtained at 40 keV. The stent diameter was equal or larger at 60150 keV, and the lowest stent diameter underestimation occurred at 100 keV. The IQ was equal or higher, ranging from 60 to 100 keV in comparison with conventional CT, and the highest IQ score occurred at 90 keV. Conclusion: This quantitative and qualitative assessment of VMI images and conventional images indicated that IQ improvement and more accurate stent lumen evaluation on lower extremity run-off CT angiography can be achieved by dual-layer spectral detector CT. Key Words: Dual-energy CT; Peripheral artery disease; Virtual monoenergetic imaging. © 2019 Published by Elsevier Inc. on behalf of The Association of University Radiologists.
SNR signal-to-noise ratio VMI virtual monoenergetic imaging Acad Radiol 2019; &:1–8 From the Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 1# Shuaifuyuan, Dongcheng District, Beijing, China (D.Z., Y.W., Y.W., Z.J., H.X.); Department of Radiology, Fuwai Hospital Chinese Academy of Medical Sciences, Shenzhen, China (Y.X.); Clinical Research, Philips Healthcare, Beijing, China (N.G.). Received February 23, 2019; revised July 6, 2019; accepted July 22, 2019. Address correspondence to: Z.J. and H.X. e-mail: [email protected] © 2019 Published by Elsevier Inc. on behalf of The Association of University Radiologists. https://doi.org/10.1016/j.acra.2019.07.022
INTRODUCTION
L
ower extremity peripheral artery disease (PAD) is the third leading cause of atherosclerotic vascular morbidity after coronary heart disease and stroke (1). The number of patients with PAD increased by 23.5% in only a decade, occurring between 2000 and 2010 (2). The increase
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of PAD represents a major public health challenge. Endovascular revascularization for the treatment of patients with PAD has developed rapidly as a less invasive treatment option. Currently, multidetector computed tomography angiography is recommended as a diagnostic tool in patients with lower extremity PAD, as it is a noninvasive and an accurate modality (3). The advantages of computed tomography angiography (CTA) include fast speed, convenience, and the ability for cross-sectional imaging of the vessel and visualization of calcifications, clips, stents, and bypasses (4,5). However, in long-term patency follow-up after stent placement, some blooming and metallic artifacts may be present (5). Stent lumen visualization can be improved by many methods in multidetector computed tomography angiography, such as using iterative reconstruction (6), dedicated convolution kernels (7,8), and a virtual monoenergetic imaging (VMI) algorithm (911). VMI images at high kiloelectron volt (keV) levels simulate increased penetration of the X-ray beam and are beneficial for reducing blooming and metallic artifacts (12). Mangold et al. evaluated the stent visualization in lower extremity run-off CTA using a VMI reconstruction algorithm in a third-generation dual-source dual-energy CT (DECT); in this study, it was found that 80 keV VMI images improved the image quality (IQ), diagnostic confidence, and accuracy of stent assessment in lower extremity CTA (13). Almutairi et al. concluded that 72 keV with 50% ASiR leads to better IQ for stent visualization in the peripheral artery when using DECT with a fast kilovoltageswitching mode (14). Recently, a dual-layer spectral detector CT has become commercially available. This system uses a detector-based dual energy separation technique and simultaneously acquires low and high energy projection data in two detector layers with the same spatial and temporal resolution (15), which favors beam-hardening correction (16), material decomposition (17), and image noise reduction on VMI images (1820). This system allows the use of dual-energy analysis on every data set acquired and does not require prospective patient selection for DECT (21). Several studies have reported that VMI reconstruction on dual-layer spectral detector CT improved IQ and reduced beam hardening artifacts of metal implants and coronary stents in vitro (15,22,23). However, the potential benefits of VMI reconstruction advances in the analysis of lower extremity vessels with a stent have not yet been evaluated. Therefore, the purpose of this study was to evaluate the value of VMI images on the lower extremity run-off CTA stent imaging performed by dual-layer spectral detector CT and to determine the optimal keV level of VMI imaging for stent evaluation.
MATERIALS AND METHODS Patient Population
This was a retrospective study, and the local institutional review board waived the informed consent. From September 1, 2016 to May 31, 2017, 32 patients (24 men and 8 women) 2
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with prior stent placement who underwent lower extremity run-off CTA on a dual-layer spectral detector CT scanner were enrolled in this study. Exclusion criteria were deviation from the standard scan protocol, insufficient arterial contrast enhancement, which was defined as attenuation of the distal abdominal aorta < 150 Hounsfield units (HU) and severity stenosis in stent >20%. CT Acquisition Parameters
All examinations were performed on a dual-layer spectral detector CT scanner (IQon, Philips Healthcare, USA). The scan length was defined from the distal abdominal aorta to the toes, and the scans were conducted in the cranio-caudal direction. Arterial enhancement was achieved with 90 mL of iodinated contrast agent (370 mgI/mL, Iopromide, Ultravist, Bayer Healthcare, Germany) administered intravenously at a flow rate of 4 mL/seconds. Scans were triggered with a bolus tracking technique; a region of interest (ROI) was placed on the distal abdominal aorta, and the scan automatically initiated after 6 seconds when a threshold of 150 HU was achieved. Routine scanning parameters for lower extremity run-off CTA were adopted without modification, including slice collimation: 64 £ 0.625 mm, rotation time: 0.27 seconds, pitch: 0.6, tube current: 125 mA, and tube voltage: 120 kVp. The CT dose index volume and dose-length product were recorded for each examination. The effective radiation dose was calculated based on the conversion factor reported by Saltybaeva et al. (24). Image Reconstruction
The display field of view was set to 250 mm with a pixel matrix of 512 £ 512. The slice thickness was 1.5 mm with an interval of 0.75 mm. Conventional images were reconstructed using iDose4 (Level 3, Philips Healthcare, USA) and a filter with Standard B. In addition, spectral base images were reconstructed using Spectral Recon (Level 3, Philips Healthcare, USA) and a filter with Standard B. All images were sent to a commercial workstation (IntelliSpace Portal 6.5, Philips Healthcare, Israel) for image evaluation. Image Analysis
Thirteen image series, including the conventional CT and 12 VMI datasets from 40 keV to 150 keV with 10-keV intervals, were reconstructed and evaluated by two readers with 3 years (X.Y.) and 6 years (Z.D.) of experience in vascular diagnosis, independently. All quantitative analyses were performed with a fixed window width of 1500 HU and a window center of 300 HU as previously recommended. The in-stent luminal diameter measurements were performed manually on the vertical plane (Fig 1) of arteries at the proximal (first 3 mm), middle, and distal levels (last 3 mm) of the stent using electronic diameter caliper (13). Every diameter was measured three times by two readers.
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Figure 1. The image example of slice selection for the measurement of attenuation, image noise, and stent diameter on conventional CT and virtual monoenergetic imaging (VMI) at virtual photon energies from 40 to 150 keV in 10 keV increments.
Mean diameter measurements and the mean underestimation of stent diameter versus the true stent diameter were calculated. In-stent luminal attenuation measurements were performed manually at the same sites as the measurements of instent luminal diameter by placing an ROI within the stent, avoiding vessel walls, plaques, stents, and blooming artifacts. ROIs were also placed in native vessels proximal to the stent (average distance of 5 mm). Measurements of adjacent muscle and fatty tissue at the same level as the stents were also performed to evaluate the contrast and noise. The standard deviation (SD) of the arterial attenuation was defined as vessel noise, and the SD in the surrounding fat was defined as image noise. The contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) were calculated for each measurement as follows: CNR ¼ HUartery HUmuscle =SDfat SNR ¼ HUartery =SDartery Qualitative measurements were rated by two observers who assessed attenuation, noise, and stent-related artifacts in each image dataset. All the axial slices, coronal slices, and sagittal slices, as well as three-dimensional reconstruction images, were used for qualitative image evaluation and the window level was freely adjusted. The IQ was rated from 1 to 5, as follows: 1 = poor IQ; 2 = suboptimal IQ; 3 = moderateIQ; 4 = good IQ; and 5 = excellent IQ.
Statistical Analysis
Commercially available software (SPSS 19.0) was used for the statistical analysis. A p value < 0.05 was considered as statistically significant. Continuous variables were expressed as the mean § SD. Objective measurements (attenuation, image noise, SNR and CNR, luminal stent diameter) for each dataset were pretested for a Gaussian distribution using the Kolmogorov-Smirnov test, and a paired t test was applied if the data had a normal distribution; otherwise, the Wilcoxon test was used. To compare the subjective IQ score, the Wilcoxon test was applied. Interobserver agreement for in-stent luminal diameter was assessed using an intraclass correlation coefficient analysis. For the subjective IQ score interobserver agreement assessment, the Kendall rank correlation coefficient was used. RESULTS Patient Population
A total of 32 patients were identified, and three patients were excluded due to stenosis of the stents >20%. Thus, a total of 29 patients (mean age, 60.4 § 9.8 years; 22 men, mean age 58.0 § 8.0 years, range 3373 years; 7 women, mean age 68.0 § 11.8 years, range 5083 years) were included. The indication for CTA was limb ischemia (n = 28: Fontaine Stage I, n = 2; Fontaine Stage II, n = 22; Fontaine Stage III, n= 3; Fontaine Stage
TABLE 1. The Stent Material, Brand, and Number Stent Material
Brand
Stainless steel
Express LD Iliac, Boston Scientific, Maple Grove, MN, USA Palmaz Genesis, Cordis Europa N.V., Roden, Netherlands Protege EverFlex Self-expanding Peripheral Stent System, Covidien, Plymouth, MN, USA Complete SE Iliac Stent System, Medtronic Inc., Santa Rosa, CA, USA Zilver Flex Vascular Self-Expanding Stent, Cook Medical Inc., Bloomington, IN, USA Astron, Biotronik GmbH Co., Berlin, Germany Zilver 635 Vascular Self-Expanding Stent, Cook Medical Inc., Bloomington, IN, USA PTA Balloon-Expendable Stent, Biotronik AG, Prague, Switzerland SMART Control Nitinol Stent System, Cordis Co., Miami, FL, USA Invatec ScubaCobalt Chromium Stent, Medtronic Inc., Santa Rosa, CA, USA
Nitinol
Cobalt chromium
Number 4 1 10 7 4 3 2 1 1 3
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4 TABLE 2. Attenuation, Image Noise, CNR, and SNR of Native Vessels and Stent Vessel Segments 120 kV
40 keV
50 keV 4
60 keV 4
70 keV 4
80 keV 5
90 keV 5
100 keV 5
110 keV 5
120 keV 5
130 keV 5
140 keV 5
150 keV 5
110.4 § 21.55 16.3 § 4.85 15.4 § 4.05 5.3 § 2.65 6.8 § 3.15 7.9 § 5.15 8.9 § 4.15
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Values are mean § standard deviation. CNR, contrast-to-noise ratio; keV, kiloelectron volt; SNR, signal-to-noise ratio. 4 Indicates the comparison with conventional CT images and that the attenuation, image noise, CNR, and SNR of VMI images were significantly increased. 5 Indicates the comparison with conventional CT images and that the attenuation, image noise, CNR, and SNR of VMI images were significantly decreased.
109.8 § 19.65
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Attenuation 331.9 § 72.9 915.9 § 223.7 607.4 § 142.4 425.4 § 95.2 317.1 § 67.3 248.4 § 49.7 203.8 § 38.8 173.6 § 31.8 152.3 § 27.2 136.9 § 24.2 125.4 § 22.1 116.6 § 20.7 native vessel Attenuation 327.1 § 86.8 990.4 § 215.54 652.9 § 138.64 455.1 § 93.94 336.5 § 66.8 260.8 § 50.05 214.6 § 40.05 180.1 § 34.05 156.9 § 29.95 139.9 § 26.45 137.8 § 66.35 127.7 § 62.45 in stent SD native 22.7 § 9.0 31.7 § 22.64 24.9 § 14.5 21.4 § 10.1 19.3 § 7.75 18.1 § 6.55 17.5 § 5.85 17.0 § 5.45 16.7 § 5.25 16.6 § 5.05 16.4 § 4.95 15.8 § 5.35 vessel SD in stent 21.4 § 6.6 33.3 § 15.04 25.0 § 10.04 20.8 § 7.3 18.6 § 6.35 17.3 § 4.95 16.5 § 4.75 16.1 § 4.45 15.8 § 4.35 15.6 § 4.25 15.5 § 4.15 15.4 § 4.05 CNR native 17.5 § 8.0 62.0 § 34.34 42.9 § 22.74 30.0 § 15.14 21.8 § 10.74 16.6 § 8.15 13.0 § 6.25 10.6 § 5.05 8.8 § 4.15 7.5 § 3.65 6.6 § 3.15 5.9 § 2.85 vessel CNR in 17.8 § 7.6 69.3 § 29.34 50.9 § 20.74 33.4 § 14.54 23.8 § 10.84 18.4 § 7.5 14.5 § 6.05 11.2 § 5.65 10.8 § 7.65 9.1 § 3.85 8.0 § 6.55 7.2 § 6.05 stent SNR native 18.7 § 13.5 47.8 § 46.64 35.7 § 32.84 26.8 § 23.24 21.1 § 16.94 17.1 § 12.85 14.4 § 10.75 12.2 § 8.35 10.8 § 7.25 9.8 § 6.65 8.9 § 5.65 9.4 § 7.65 vessel SNR in stent 21.1 § 13.4 47.0 § 30.24 37.2 § 21.24 29.3 § 15.54 23.5 § 11.74 18.7 § 8.0 16.5 § 7.85 14.0 § 6.55 12.4 § 6.05 11.2 § 5.45 12.0 § 14.05 11.2 § 12.95
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Figure 2. Comparison of attenuation, image noise, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) of native vessels and stent vessel segments between conventional CT and virtual monoenergetic imaging (VMI) at virtual photon energies from 40 to 150 keV with 10 keV increments.
IV, n = 1) and follow-up examination after recent iliac stent placement (n = 1). The mean body mass index was 24.7 § 3.3 kg/m2. The average CT dose index volume and dose-length product were 2.6 § 0.4 mGy and 342.1 § 57.4 mGy*cm, respectively. The effective radiation dose was 2.2 § 0.4 mSv. In 10 patients, two stents were present, resulting in a total of 39 stented vessel segments for analysis. Stent Characteristics
Information about stent material and size was available for 36/39 stents. Ten different stent types were present, and stent material was either stainless steel, nitinol, or cobalt chromium. The detail information was shown in Table 1. The mean stent diameter was 7.5 § 1.0 mm (range 59 mm). Stents were present in the iliac (n = 28) and femoral (n = 11) arteries. Image Quality Analysis
Results of the quantitative IQ analysis of attenuation, image noise, CNR, and SNR are shown in Table 2 and Figure 2. In general, the attenuation, image noise of native vessel, and in-stent lumen decreased as the VMI energy level increased. Compared to conventional CT, there was no statistical
difference of the attenuation at 70keV (p = 0.124) and image noise at 60 keV (p = 0.180) of in-stent lumen. Figure 2 showed that CNR and SNR in both native vessel and vessel segment with stent were higher at low VMI energy level (4070keV). The CNR (p = 0.209) and SNR (p = 0.489) of stent lumen equal to conventional CT at 80 keV. Interobserver agreement for luminal stent diameter measurements was good (intraclass correlation coefficient: 0.948). Compared to conventional CT, luminal stent diameters were increased at energy levels 80 keV (each p < 0.05). The measurement of stent diameter which was closest to real stent diameter was at 100 keV. The diameter underestimation at 100 keV was 25.53% (in all location), 24.7% (in iliac artery), and 28.5% (in femoral artery) separately. There was no significant difference at energy levels of 70 keV (p = 0.132) for iliac artery or 60 keV (p = 0.944) for femoral artery. The stent diameters are shown in Table 3 and Figure 3. In comparison to conventional CT, the subjective IQ score was rated higher between energy levels of 7090 keV (each p< 0.05). There were no significant differences in subjective IQ scores between conventional CT and VMI at 60 keV (p = 0.115) or 100 keV (p = 0.076). The result of IQ score is shown in Table 4. The interobserver agreement for IQ assessment was good (coefficient: 0.814, p< 0.001). 5
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6
DISCUSSION
Values are mean § standard deviation. The diameter underestimation was calculated based on the true stent diameter.
Stent diameter in all location 5.37 § 1.20 4.84 § 1.22 5.21 § 1.23 5.33 § 1.12 5.41 § 1.14 5.50 § 1.15 5.56 § 1.17* 5.56 § 1.15* 5.53 § 1.15 5.53 § 1.17 5.49 § 1.19 5.49 § 1.22 5.46 § 1.23 Stent diameter in iliac artery 5.61 § 1.18 5.03 § 1.26 5.45 § 1.15 5.60 § 1.14 5.74 § 1.18 5.83 § 1.19 5.88 § 1.22 5.88 § 1.23 5.86 § 1.23 5.84 § 1.24 5.82 § 1.23 5.82 § 1.24 5.80 § 1.23 Stent in femoral artery 4.10 § 0.64 3.72 § 0.64 4.03 § 0.62 4.10 § 0.64 4.20 § 0.67 4.26 § 0.66 4.29 § 0.70 4.31 § 0.69 4.25 § 0.71 4.24 § 0.73 4.21 § 0.72 4.18 § 0.72 4.14 § 0.71 Diameter underestimation 28.93 36.51 30.82 29.16 27.34 26.12 25.57 25.53 25.86 26.08 26.40 26.61 26.99 in all location (%) Diameter underestimation 28.1 36.0 30.1 28.4 26.5 25.3 24.7 24.7 25.0 25.3 25.5 25.7 26.0 in iliac artery (%) Diameter underestimation 32.2 38.5 33.4 32.2 30.4 29.3 28.7 28.5 29.1 29.1 29.7 30.2 30.7 in femoral artery (%)
80 keV 70 keV 60 keV 50 keV 40 keV 120 kV
TABLE 3. Stent Diameters and Diameter Underestimation in Conventional CT and VMI Images
90 keV
100 keV
110 keV
120 keV
130 keV
140 keV
150 keV
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This is the first study about lower extremity artery stent evaluation using dual layer spectral detector CT. In this study, the results showed that stent lumen evaluation under spectral CT needed consider different VMI energy level together and get the finest stent lumen illustration. In the current study, attenuation of native vessel and vessel segment with stent increased greatly as the energy level decreased, while the image noise varied mildly across different energy levels; the image noise at 40 keV and 50 keV was slightly larger than that at other energy levels, which led to a higher CNR from 40 to 80 keV and a higher SNR from 40 to 70 keV (Table 2). In other dual energy CT, including dual source scanners and rapid kVp-switching scanners, large noise in low-energy VMI limits its utility and diagnostic capabilities (2527). In the dual-layer CT system, due to perfectly registered spatial data, basis decomposition is performed in the projection domain. The anticorrelated noise is reduced by using an anticorrelated noise reduction algorithm in the reconstruction process (27). A direct benefit of the reduction of anticorrelated noise is the reduction of the noise variation across the energies of VMI, especially at low and high keVs. VMI images of stents from 40 keV to 150 keV were evaluated, and the image noise decreased as the VMI energy level increased. The results were comparable with a previous in vivo study on VMI images improving the IQ of the head (28), while the image noise was more homogenous to the results of in vitro studies on coronary stents (15) and hip prostheses (22). The difference in the image noise trend between the in vivo and in vitro studies may be explained by the deviation of the simulation of the in vitro study. Although the highest CNR and SNR for 12 VMI datasets were at 40 keV in our study, which benefit from the low image noise of dual-layer spectral detector CT, the relatively large blooming artifact at low energy levels may hinder the visualization of stent lumen. In this study, to evaluate the effect of the energy level on stent lumen visualization, the stent diameter, diameter underestimation, and subjective IQ were measured. For the stent lumen diameter evaluation, the smallest underestimation (in other words, the largest stent lumen diameter) was found at 100 keV no matter for iliac artery or femoral artery. Based on the above discussion, the illustration of peripheral artery stent was not only influenced by CNR and SNR for the lumen enhancement, but also by the stent beam hardening. For CNR and SNR, low VMI energy level which was equal or lower than 70 keV was better than conventional CT; while for beam hardening reduction, the stent diamerter underestimation results showed that high VMI energy level which was equal or higher than 80 keV was better than conventional CT. This result was consistent with a previous in vitro study conducted by Hickethier et al. on coronary stents, in which images at higher keV levels improved the visible stent diameter. However, Hickethier et al. (15) only conducted an objective assessment; thus, a comparison of the adequate keV level between peripheral and
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Figure 3. Comparison of stent diameters measured in conventional CT and virtual monoenergetic imaging (VMI) at virtual photon energies from 40 to 150 keV with 10 keV increments in iliac and femoral arteries.
coronary stents could not be completed here. CNR, SNR, and stent diameter were all objective. The IQ score was subjective and was rated by two observers who assessed attenuation, noise, and stent-related artifacts. The highest IQ score was at 90 keV in this study. The dual-layer detector spectral CT offered wide-range VMI with low image noise, and gave peripheral artery stent evaluation more operational VMI energy levels. Taking the objective and subjective stent evaluation results together, combined low VMI energy level to assess stent lumen enhancement, as well as high VMI energy level to reduce beam hardening effect seemed a comprehensive solution. Dual-layer spectral detector CT allowed for a retrospective dual-energy analysis (29), which was important for stent evaluation. Although it has been a while about the introduction of the DECT, the clinical application was restricted by the specific scan protocol. To detector based DECT, the scan protocol is same as routine CT scan, which means when the stent was shown in peripheral arteries, VMI images can be reconstructed without worrying lacking spectral data. This study has several limitations. First, the study population was relatively small, and the results should be
confirmed in a larger patient population. Second, the visibility of the stent lumen varies greatly depending on the type of stent and material used (7,30), but given the diversity of stent manufacturing and the small number of each stent material in our research, subgroups of different types of stents were not analyzed. Third, although the optimal keV level for peripheral artery stents was concluded at 90 keV based on the quantitative and qualitative assessment, the diagnostic accuracy of stent restenosis in 90 keV VMI images still needs to be confirmed by comparison with digital subtraction angiography. CONCLUSION In summary, VMI of dual-layer spectral detector CT improves the lower extremity artery stent visualization and it needs taking different VMI energy level together to make a comprehensive evaluation. The retrospective spectral analysis favors the clinical application of the detector based DECT. The diagnostic accuracy of stent restenosis discrimination, as well as the influence of VMI images on different material stents, still needs to be confirmed by further studies.
TABLE 4. Subjective IQ Scores for Conventional CT and VMI Images
IQ score
120 kV
40 keV
50 keV
60 keV
70 keV
80 keV
90 keV
100 keV
110 keV
120 keV
130 keV
140 keV
150 keV
3.38 § 0.38
1.88 § 0.32
2.46 § 0.42
3.24 § 0.41
3.74 § 0.35
4.01 § 0.25
4.04 § 0.14
3.58 § 0.44
3.15 § 0.26
2.51 § 0.47
2.29 § 0.40
2.07 § 0.21
2.01 § 0.08
Values are mean § standard deviation.
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ACKNOWLEDGMENTS This research was supported by the National Public Welfare Basic Scientific Research Program of Chinese Academy of Medical Sciences (2018PT32003 and 2017PT32004). All the authors have reported that they have no relationships relevant to the contents of this paper to disclose. REFERENCES 1. Aiman U, Haseen MA, Beg MH, et al. Profile of atherosclerotic risk factors and management in patients of peripheral arterial disease at a tertiary care teaching hospital of north India. Indian J Pharm Sci 2014; 76(6):504–509. 2. Fowkes FG, Rudan D, Rudan I, et al. Comparison of global estimates of prevalence and risk factors for peripheral artery disease in 2000 and 2010: a systematic review and analysis. Lancet 2013; 382(9901):1329–1340. 3. Kayhan A, Palabiyik F, Serinsoz S, et al. Multidetector CT angiography versus arterial duplex USG in diagnosis of mild lower extremity peripheral arterial disease: is multidetector CT a valuable screening tool? Eur J Radiol 2012; 81(3):542–546. 4. Norgren L, Hiatt WR, Dormandy JA, et al. Inter-Society consensus for the management of peripheral arterial disease (TASC II). J Vasc Surg 2007; 45(Suppl S):S5–S67. 5. European Stroke O, Tendera M, Aboyans V, et al. ESC guidelines on the diagnosis and treatment of peripheral artery diseases: document covering atherosclerotic disease of extracranial carotid and vertebral, mesenteric, renal, upper and lower extremity arteries: the Task Force on the Diagnosis and Treatment of Peripheral Artery Diseases of the European Society of Cardiology (ESC). Eur Heart J 2011; 32(22):2851–2906. 6. Funama Y, Oda S, Utsunomiya D, et al. Coronary artery stent evaluation by combining iterative reconstruction and high-resolution kernel at coronary CT angiography. Acad Radiol 2012; 19(11):1324–1331. 7. Maintz D, Burg MC, Seifarth H, et al. Update on multidetector coronary CT angiography of coronary stents: in vitro evaluation of 29 different stent types with dual-source CT. Eur Radiol 2009; 19(1):42–49. 8. Oda S, Utsunomiya D, Funama Y, et al. Improved coronary in-stent visualization using a combined high-resolution kernel and a hybrid iterative reconstruction technique at 256-slice cardiac CT-Pilot study. Eur J Radiol 2013; 82(2):288–295. 9. Mangold S, Cannao PM, Schoepf UJ, et al. Impact of an advanced image-based monoenergetic reconstruction algorithm on coronary stent visualization using third generation dual-source dual-energy CT: a phantom study. Eur Radiol 2016; 26(6):1871–1878. 10. Stehli J, Fuchs TA, Singer A, et al. First experience with single-source, dual-energy CCTA for monochromatic stent imaging. Eur Heart J Cardiovasc Imaging 2015; 16(5):507–512. 11. Wichmann JL, Gillott MR, De Cecco CN, et al. Dual-energy computed tomography angiography of the lower extremity runoff: impact of noiseoptimized virtual monochromatic imaging on image quality and diagnostic accuracy. Invest Radiol 2016; 51(2):139–146. 12. Machida H, Tanaka I, Fukui R, et al. Dual-energy spectral CT: various clinical vascular applications. Radiographics 2016; 36(4):1215–1232. 13. Mangold S, De Cecco CN, Schoepf UJ, et al. A noise-optimized virtual monochromatic reconstruction algorithm improves stent visualization and diagnostic accuracy for detection of in-stent re-stenosis in lower extremity run-off CT angiography. Eur Radiol 2016; 26(12):4380–4389.
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