European Journal of Radiology

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1 European Journal of Radiology 82 (2013) Contents lists available at SciVerse ScienceDirect European Journal of Radiology journa l h o me pa ge: Improved coronary in-stent visualization using a combined high-resolution kernel and a hybrid iterative reconstruction technique at 256-slice cardiac CT Pilot study Seitaro Oda a,, Daisuke Utsunomiya a,1, Yoshinori Funama b,1, Hiroko Takaoka c,2, Kazuhiro Katahira c,2, Keiichi Honda c,2, Katsuo Noda d,2, Shuichi Oshima d,2, Yasuyuki Yamashita a,1 a Department of Diagnostic Radiology, Faculty of Life Sciences, Kumamoto University, Honjyo, Kumamoto , Japan b Department of Medical Physics, Faculty of Life Sciences, Kumamoto University, Honjyo, Kumamoto , Japan c Department of Diagnostic Radiology, Kumamoto Chuo Hospital, Tainoshima, Kumamoto , Japan d Department of Cardiology, Kumamoto Chuo Hospital, Tainoshima, Kumamoto , Japan a r t i c l e i n f o Article history: Received 6 August 2012 Received in revised form 30 October 2012 Accepted 2 November 2012 Keywords: Cardiac CT In-stent stenosis Iterative reconstruction High-resolution kernel Kurtosis a b s t r a c t Objectives: To investigate the diagnostic performance of 256-slice cardiac CT for the evaluation of the instent lumen by using a hybrid iterative reconstruction (HIR) algorithm combined with a high-resolution kernel. Methods: This study included 28 patients with 28 stents who underwent cardiac CT. Three different reconstruction images were obtained with: (1) a standard filtered back projection (FBP) algorithm with a standard cardiac kernel (CB), (2) an FBP algorithm with a high-resolution cardiac kernel (CD), and (3) an HIR algorithm with the CD kernel. We measured image noise and kurtosis and used receiver operating characteristics analysis to evaluate observer performance in the detection of in-stent stenosis. Results: Image noise with FBP plus the CD kernel (80.2 ± 15.5 HU) was significantly higher than with FBP plus the CB kernel (28.8 ± 4.6 HU) and HIR plus the CD kernel (36.1 ± 6.4 HU). There was no significant difference in the image noise between FBP plus the CB kernel and HIR plus the CD kernel. Kurtosis was significantly better with the CD- than the CB kernel. The kurtosis values obtained with the CD kernel were not significantly different between the FBP- and HIR reconstruction algorithms. The areas under the receiver operating characteristics curves with HIR plus the CD kernel were significantly higher than with FBP plus the CB- or the CD kernel. The difference between FBP plus the CB- or the CD kernel was not significant. The average sensitivity, specificity, and positive and negative predictive value for the detection of in-stent stenosis were 83.3, 50.0, 33.3, and 91.6% for FBP plus the CB kernel, 100, 29.6, 40.0, and 100% for FBP plus the CD kernel, and 100, 54.5, 40.0, and 100% for HIR plus the CD kernel. Conclusions: The HIR algorithm combined with the high-resolution kernel significantly improved diagnostic performance in the detection of in-stent stenosis Elsevier Ireland Ltd. All rights reserved. 1. Introduction Coronary stent placement is a common and effective way to open a blocked artery. Although stent implantation greatly reduces Corresponding author. Tel.: ; fax: addresses: seisei0430@nifty.com (S. Oda), utsunomi@kumamoto-u.ac.jp (D. Utsunomiya), funama@kumamoto-u.ac.jp (Y. Funama), hiroko takayoka@yahoo.co.jp (H. Takaoka), yy26kk@yahoo.co.jp (K. Katahira), k-book@osu.bbiq.jp (K. Honda), k-noda@kumachu.gr.jp (K. Noda), shuoshima@ .jp (S. Oshima), yama@kumamoto-u.ac.jp (Y. Yamashita). 1 Tel.: ; fax: Tel.: ; fax: the incidence of restenosis after balloon angioplasty [1,2], in-stent restenosis has been reported in 20 35% and 5 10% of patients implanted with bare-metal- and drug-eluting stents, respectively [3,4]. The clinical role of multidetector computed tomography (MDCT), increasingly used for the non-invasive imaging of coronary artery disease [5], remains undefined for the evaluation of in-stent luminal narrowing. Although some previous studies evaluated stent patency by using MDCT [6 8], the in-stent lumen is markedly affected by blooming artifacts that can make the metallic stent struts appear enlarged [9], resulting in overestimation of the degree of stenosis and underestimation of the in-stent lumen [6]. By current consensus, the role of cardiac CT for the assessment of stent patency is limited [10] X/$ see front matter 2012 Elsevier Ireland Ltd. All rights reserved.

2 S. Oda et al. / European Journal of Radiology 82 (2013) Table 1 Stent characteristics. Stent name Manufacturer Material No. of stents Diameter (mm) Stent location: guidelines of SCCT (segment no.) Cypher Cordis Stainless steel 7 2.5, 2.5, 2.75, 3, 3, 3, 3.5 3, 7, 7, 6, 7, 13, 11 Endeavor Medtronic Cobalt chromium alloy , 2.75, 2.75, 3, 3 12, 2, 3, 6, 7 Micro-Driver Medtronic Cobalt alloy 4 2.5, 2.5, 2.75, , 13, 3, 8 Tsunami Terumo Stainless steel 3 2.5, 3, 3 7, 3, 3 Xience Abbott vascular Stainless steel 2 3, 3.5 2, 6 Taxus Boston Scientific Stainless steel , , 12 Vision Guidant Cobalt chromium alloy 2 3, 3.5 1, 7 Penta Guidant Stainless steel Velocity Cordis Stainless steel Driver Medtronic Cobalt alloy SCCT, Society of Cardiovascular Computed Tomography. For a more precise evaluation of the in-stent lumen at cardiac CT, use of the high-resolution cardiac kernel has been suggested. While it can reduce blooming artifacts and improve spatial resolution, the image noise is increased [11,12]. Iterative reconstruction techniques, an alternative to traditional filtered back projection (FBP) reconstruction methods, can decrease the image noise [13,14]. However, prior-generation iterative reconstruction elicited image-quality problems such as texture changes due to noise reduction that could affect diagnostic confidence [14]. The newer-generation hybrid iterative reconstruction algorithm (HIR) involves two de-noising components, an iterative maximum likelihood-type sinogram restoration method based on Poisson noise distribution and local structure model fitting on image data that iteratively decreases uncorrelated noise [15,16]. HIR provides better noise removal efficiency and produces no major image texture alterations and it preserves the spatial resolution of CT images [16]. A recent study that used an anthropomorphic moving-heart phantom showed that application of HIR and the high-resolution kernel allowed improved stent visualization at cardiac CT [17]. We hypothesized that in combination, the HIR algorithm and the high-resolution kernel would decrease image noise, facilitate visualization of coronary stents, and improve diagnostic accuracy in the detection of in-stent stenosis. The purpose of this study was to prospectively assess the diagnostic performance of 256-slice cardiac CT for the evaluation of the in-stent lumen by using different reconstruction techniques (FBP or HIR) and different cardiac kernel combinations. 2. Materials and methods This prospective study received institutional review board approval; prior informed consent was obtained from all patients. The attending physicians explained to the patients that based on the appropriate use criteria for cardiac CT published in 2010, the results of their coronary stent evaluation might be uncertain [10] Study population We prospectively enrolled 28 patients (18 men, 10 women; mean age 75.3 years, age range years) with single-vessel disease who had 28 coronary stents. They underwent cardiac CT for the evaluation of in-stent patency between June and September Based on the results of cardiac CT, myocardial scintigraphy, conventional coronary angiography, and clinical follow-up, they were divided into restenosis- and non-restenosis groups. Patients with chest pain or suspected in-stent stenosis on cardiac CT underwent conventional coronary angiography. The criterion for restenosis was significant in-stent stenosis with luminal diameter narrowing of 50% or greater on conventional coronary angiographs. The criterion for non-restenosis was (a) less than 50% stenosis on conventional coronary angiographs, or (b) negative findings on stress-rest myocardial perfusion scintigraphs with no coronary events in the course of 7-month follow-up after cardiac CT study. We identified 6 stents with significant in-stent restenosis and 22 patent stents. All 6 patients with- and 14 of the 22 patients without restenosis underwent coronary angiography for the evaluation of in-stent luminal narrowing. Cardiac CT and conventional coronary angiography and/or stress-rest myocardial scintigraphy were performed within 14 days. The mean interval between stent implantation and cardiac CT studies was 40.6 months (range months). Exclusion criteria for coronary CT were allergy to contrast medium, renal insufficiency (serum creatinine concentration > 1.5 mg/dl), unstable clinical condition, and inability to perform a breath-hold. The labeled diameter of the stents was 2.5 mm (n = 5), 2.75 mm (n = 9), 3.0 mm (n = 11), and 3.5 mm (n = 3), their length ranged from 8 to 30 mm (Table 1) Acquisition of cardiac CT images All patients were examined on a 256-slice CT system (Brilliance ict; Philips Healthcare, Cleveland, OH) using retrospectively gated helical data acquisition. The parameters were detector configuration, mm (detector collimation); slice thickness, 0.67 mm; gantry rotation time, 0.27 s; beam pitch, 0.16; tube voltage, 120 kv; tube current time product, 850 mas without the ECG-dependent tube current modulation technique. The calculated CTDI vol was 54.7 mgy and the mean estimated effective radiation dose was 12.1 msv. A dose of mg of the beta-adrenergic blocking agent propranolol was administered orally min before CT examination if the patient s resting heart rate exceeded 65 beats per minute and there was no contraindication to the use of beta-adrenergic blockers. At 5 min before data acquisition, each patient received 0.3 mg of nitroglycerin sublingually to dilate the coronary arteries. In all patients, iopamidol with an iodine concentration of 370 mg/ml (Iopamiron-370; Bayer HealthCare) was delivered via a 20-gauge catheter inserted into an antecubital vein using a double-head power injector (Autoenhance A-250; Nemoto Kyorindo). The amount of contrast material was adjusted to the body weight of each patient (300 mgi/kg) and injected at a fixed injection duration of 12 s. Contrast administration was followed by the injection of 40 ml of a saline solution delivered at the same injection rate as the contrast medium. The start time of data acquisition was determined with a computer-assisted bolus-tracking program (Bolus Pro Ultra; Philips Medical Systems) [18] with a trigger threshold of 100 Hounsfield units (HU) in the ascending aorta. Data acquisition started 6 s after triggering CT image reconstruction We reconstructed axial source images with a section thickness of 0.67 mm and a section interval of 0.33 mm by using a standard

3 290 S. Oda et al. / European Journal of Radiology 82 (2013) FBP algorithm with a standard- or a high-resolution cardiac kernel/filter (CB, CD, respectively) during the mid-diastolic phase. We also reconstructed images using the HIR algorithm (idose: 4th generation iterative reconstruction, Philips Healthcare) with the CD kernel. The system offers 7 levels of iteration to control or reduce the amount of image noise at a given tube output; levels 1 7 provided noise reduction factors of We applied high-level iterative reconstruction (idose level 7); it corresponded with a high noise reduction factor of Measurement of image noise, contrast enhancement and kurtosis Two of the authors (K.H. and Y.F.) consensually measured the image noise and contrast enhancement on each reconstruction image. The mean CT attenuation of the ascending aorta was measured by placing a round region of interest (ROI) in the aortic lumen. Image noise was determined as the standard deviation of the attenuation value in a single round ROI placed in the ascending aorta. The ROI was drawn as large as the aortic lumen diameter, carefully avoiding the wall. Stent mesh delineation was quantitatively analyzed by defining a dissecting line that originated and terminated on opposite sides of the stent and crossed the center of the lumen on the longitudinal stent plane. To analyze the longitudinal stent plane, 0.67-mm multiplanar reformations (MPRs) were reconstructed on a workstation (EBW, Philips Healthcare, Cleveland, OH). We adopted a kurtosis () value as the quantitative in-stent visualization index. The attenuation value of pixels along the dissecting line was transferred onto a profile plot and of the resulting curve was calculated using the equation: = N(P last) i P first (HU i HU) 4 (N 1) SD 4 3, where i is the index of summation, P-last the upper boundary of summation or the last pixel on the dissection line, P-first the lower boundary of summation or the 1st pixel on the dissection line, HU i the attenuation of the ith pixel on the dissection line, and HU the average of all attenuation values on the dissecting line. is a dimensionless mathematical parameter that subsumes the difference in maximal and minimal pixel attenuation values as well as the ascending and descending slope of the measured profile plot. For measurements we used portions free of stenosis on MPR images acquired with the FBP and the HIR algorithm (Fig. 1). values greater than 0 are termed leptokurtic; they represent steep, well-marginated bell-shaped curves that indicate small blooming artifacts that allow the clear depiction of the stent lumen. values of less than 0 are termed platykurtic; they represent a shallow curve as is observed in the presence of substantial blooming artifacts that obscure the stent lumen Observer performance study Receiver operating characteristic (ROC) analysis was applied to compare the diagnostic performance of each reconstruction image for the detection of in-stent stenosis by comparing the respective areas under the ROC curve (AUCs) [19]. For ROC analysis the two observers visually graded the likelihood of in-stent stenosis on cardiac CT angiograms on a scale where 1 = absent, 2 = probably absent, 3 = possibly present, 4 = probably present, and 5 = definitely present. The observers were allowed to change the level and width of the window on the monitor; reading time was not limited. No patient information was provided to the observers. Fig. 1. Computed tomography voxel attenuation profiles across the stent for FBP with the CB kernel, FBP with the CD kernel and HIR and the CD kernel using multiplanar reformation images Statistical analysis To determine the appropriate sample size for the statistical analysis of our results we performed a power analysis that used preliminary measurements of the value obtained in 5 subjects who were not included in the final study population. The difference in the mean value under the 3 reconstruction methods was 0.79, 0.14 and The parameters for the power analysis were effect size f = 0.80, = 0.05, power = We determined that at least 21 study subjects were required for a meaningful statistical analysis of our findings. Numeric data were expressed as the mean ± SD. The Tukey Kramer test [20] was used to compare mean image noise, contrast enhancement, and the value among the 3 reconstructions. For each observer we compared the ROC curves and AUCs using a Z test [21]. For ROC analysis, the degree of agreement between the observers with respect to the grade they assigned to visualization of the in-stent stenosis was measured with the kappa statistic where a value of 0 = no-, more than 0 but less than 0.20 = poor-, = fair-, = moderate-, = substantial-, and = excellent agreement. A p value of less than 0.05 was considered to indicate statistically significant differences. We used software for power analysis (G-Power version available at and for statistical analyses (MedCalc, version ; MedCalc, Mariakerke, Belgium). 3. Results 3.1. Image noise, contrast enhancement, and kurtosis evaluation With FBP reconstruction the image noise on the CB- and CDkernel images was 28.8 ± 4.6 HU and 80.2 ± 15.5 HU, respectively; it was 36.1 ± 6.4 HU with HIR and the CD kernel (Fig. 2). The image noise was significantly greater with FBP and the CD kernel than with FBP and the CB kernel and HIR and the CD kernel (p < 0.01). There was no statistically significant difference in image noise between FBP and the CB kernel, and HIR and the CD kernel (p = 0.10). Thus, HIR reduced the image noise due to the high-resolution cardiac kernel. There was no statistically significant difference in the mean CT attenuation of the ascending aorta (FBP and the CB kernel, 352 ± 68 HU; FBP and the CD kernel, 366 ± 76 HU; HIR and the CD kernel, 362 ± 72 HU; p = 0.55).

4 S. Oda et al. / European Journal of Radiology 82 (2013) Table 2 Observers detection of in-stent stenosis with FBP and the CB kernel, FBP and the CD kernel, and HIR and the CD kernel. Sensitivity (%) Specificity (%) Accuracy (%) PPV (%) NPV (%) FBP and CB kernel Observer Observer FBP and CD kernel Observer Observer HIR and CD kernel Observer Observer PPV, positive predictive value; NPV, negative predictive value; FBP, filtered back projection; HIR, hybrid iterative reconstruction. Table 3 Visual scores for the likelihood of in-stent stenosis assigned by two observers to images acquired with FBP and the CB kernel, FBP and the CD kernel, and HIR and the CD kernel. Visual scores (observer 1/observer 2) Score 1 Score 2 Score3 Score4 Score 5 Total (n = 28) FBP with CB kernel (n) 0/1 13/10 12/16 3/1 0/0 FBP with CD kernel (n) 0/0 6/7 15/17 7/4 0/0 HIR with CD kernel (n) 2/3 10/9 9/9 6/6 1/1 In-stent stenosis (n = 6) FBP with CB kernel (n) 0/0 1/1 3/4 2/1 0/0 FBP with CD kernel (n) 0/0 0/0 3/4 3/2 0/0 HIR with CD kernel (n) 0/0 0/0 1/1 4/4 1/1 Non restenosis (n = 22) FBP with CB kernel (n) 0/1 12/9 9/12 1/0 0/0 FBP with CD kernel (n) 0/0 6/7 12/13 4/2 0/0 HIR with CD kernel (n) 2/3 10/9 8/8 2/2 0/0 Fig. 2. Box plot showing the image noise obtained with 3 reconstruction algorithms. Image noise with FBP and the CD kernel was significantly higher than with FBP and the CB kernel or HIR and the CD kernel. There was no statistically significant difference in image noise between FBP and the CB kernel, and HIR and the CD kernel. values obtained with the 3 reconstruction algorithms are shown in Fig. 3. With FBP and HIR reconstruction, the mean value was significantly better with the CD- than the CB kernel and blooming artifacts were reduced when the CD kernel was applied (p < 0.01). The difference in the values obtained with the CD kernel and the FBP- or HIR reconstruction algorithms was not statistically significant (p = 0.14). 1, absent; 2, probably absent; 3, possibly present; 4, probably present; 5, definitely present. FBP, filtered back projection; HIR, hybrid iterative reconstruction Visual evaluation For the detection of in-stent stenosis, the AUC value obtained with FBP and the CB kernel, FBP and the CD kernel, and HIR and the CD kernel was 0.75, 0.73, 0.92, respectively, for observer 1; it was 0.69, 0.72, and 0.92, respectively, for observer 2. For both observers the AUC values with HIR and the CD kernel were significantly higher than with FBP and the CB kernel, and with FBP and the CD kernel; the difference in values obtained with FBP and the CB kernel and FBP and the CD kernel was not significant (Fig. 4). In cases where in-stent stenosis was identified correctly and a confidence level of 3 or more was assigned, the sensitivity, specificity, accuracy, and positive and negative predictive value (PPV, NPV) for FBP and the CB kernel were 83.3, 54.5, 60.7, 33.3, and 92.3%, respectively (observer 1), and 83.3, 45.5, 53.6, 33.3, and 90.9% (observer 2). For FBP and the CD kernel these values were 100, 27.3, 42.9, 40.0, and 100% for observer 1 and 100, 31.8, 46.2, 40.0, and 100% for observer 2. With HIR and the CD kernel, these values were 100, 54.5, 42.9, 40.0, and 100% for both observers (Tables 2 and 3). Interobserver agreement (kappa value) for the visual grading of in-stent stenosis was 0.75, 0.56, and 0.83 for FBP and the CB kernel, FBP and the CD kernel, and HIR and the CD kernel, respectively. Representative cases are shown in Figs. 5 and Discussion Fig. 3. Box plot showing the image noise with 3 reconstruction algorithms. With FBP and HIR reconstruction, kurtosis was significantly better with the CD- than the CB kernel and blooming artifacts were reduced when the CD kernel was applied. The difference in the kurtosis values obtained with the CD kernel plus FBP- or HIR reconstruction algorithms was not statistically significant. As metallic struts tend to produce severe blooming artifacts on CT images, the in-stent luminal diameter is easily underestimated [9,22]. This is due to beam hardening. As the X-ray beam passes through hyperattenuating structures, its energy spectrum

5 292 S. Oda et al. / European Journal of Radiology 82 (2013) Fig. 4. Graphs showing the receiver operating characteristics curves of the diagnostic performance of observer 1 (a) and 2 (b) in the detection of in-stent stenosis. The AUC value with FBP and the CB kernel, FBP and the CD kernel, and HIR and the CD kernel was 0.75, 0.73, and 0.92, respectively, for observer 1; it was 0.69, 0.72, and 0.92, respectively, for observer 2. For both observers, the AUC values with HIR and the CD kernel were significantly higher than with FBP and the CB kernel, or FBP and the CD kernel. The difference was not significant when FBP with the CB- or the CD kernel was used. Fig. 5. A patent stent in the middle segment of the right coronary artery in a 77-year-old man. Curved multi-planar reconstruction images obtained with FBP and the CB kernel (a), FBP and the CD kernel (b), and HIR and the CD kernel (c). Blooming artifacts were reduced more when the CD- rather than the CB kernel was applied. Moreover, HIR reduced the image noise due to the CD kernel and improved visualization of the stent lumen.

6 S. Oda et al. / European Journal of Radiology 82 (2013) Fig. 6. In-stent restenosis (arrow) in the middle segment of the left anterior descending artery in an 82-year-old man. Curved multi-planar reconstruction images with FBP and the CB kernel (a), FBP and the CD kernel (b), and HIR and the CD kernel (c). Application of HIR with the CD kernel (c) facilitated image noise reduction, improved visualization of the stent lumen due to a reduction in blooming artifacts, and delineated in-stent stenosis more clearly. Conventional coronary angiogram of the left anterior descending artery (d) confirms in-stent restenosis (arrow). increases because lower- are absorbed more rapidly than higherenergy photons. Consequently, the beam is more intense when it reaches the detectors. Blooming artifacts often compromise assessment of the stent lumen, this may be one of the most important limitations of cardiac CT for the evaluation of in-stent patency. In a meta-analysis of the diagnostic accuracy of 64-slice cardiac CT by Kumbhani et al. [23], its sensitivity and specificity for the detection of in-stent stenosis were estimated as 87% and 84%, respectively. The positive and negative predictive values were 53% and 97% when segments that were not assessable were included. They concluded that 64-slice CT is of relatively high diagnostic accuracy for the detection of in-stent stenosis although its precise quantification is not possible due to its low positive predictive value, which they attributed to blooming artifacts. We found that application of the HIR algorithm with the high-reconstruction kernel at cardiac CT decreased the image noise and improved visualization of the stent lumen due to a reduction in blooming artifacts. This improved diagnostic accuracy for the detection of in-stent restenosis. Hong et al. [12] who used a 16-slice CT instrument evaluated the accuracy of the in-stent luminal diameter measured in 19 patients. They reported that accuracy was improved with use of the high-resolution kernel at image reconstruction. However, at 52.0 ± 4.9 HU, the mean image noise measured on high-resolution kernel images was significantly higher than on medium-soft kernel images (26.9 ± 3.2 HU) and in-stent visibility was lower. Maintz et al. [11] also reported that the high-resolution kernel offered significantly better lumen visualization of coronary artery stents than the soft- and medium-soft kernel, however, it came at the expense of increased noise. They suggested that although the highresolution kernel alone may raise the issue of a higher noise level, this may be solved with the addition of specific noise reduction

7 294 S. Oda et al. / European Journal of Radiology 82 (2013) techniques. We propose that iterative reconstruction algorithms can overcome the image noise issues associated with the highresolution cardiac kernel. While the traditional FBP reconstruction technique is fast, mathematically simple, and requires little computational power, image noise presents a problem. An iterative reconstruction algorithm for CT was introduced to help reduce the quantum noise associated with standard convolution-fbp reconstruction algorithms [24]. HIR (idose), a new type of iterative reconstruction algorithm, employs a hybrid iterative reconstruction technique that uses a complicated mathematical model that includes projection and image spaces. First, the projection data per se are denoised and then the image is compared to a noiseless ideal anatomical model by HIR, enabling a noise reduction without a shift in the noise spectrum. Unlike earlier types of iterative reconstruction algorithms such as the adaptive statistical iterative reconstruction (ASIR) technique, HIR removes noise from the raw data. Leipsic et al. [14] who used ASIR suggested that highly iterative reconstructions produce images that are significantly different in appearance from images acquired with the FBP reconstruction algorithm; their noise texture appears different and their borders manifest a higher degree of smoothness. HIR provides better noise removal efficiency across the noise spectrum and preserves the natural image texture in high-level iterative settings [16]. Utsunomiya et al. [25] who evaluated the effect of HIR on qualitative and quantitative image quality at 256-slice cardiac CT reported that high- (idose level 7) and moderate-level iterative reconstructions (idose level 3) yield better image quality than FBP. We applied high-level iterative reconstruction and found that kurtosis remained the same as with the FBP technique. Thus, HIR has little influence on image blurring and resolution degradation. Moreover, the interobserver agreement for visual grading of in-stent stenosis was higher with HIR than FBP reconstruction. We posit that the increase in the image noise produced by the high-resolution kernel and FBP reconstruction lowers interobserver agreement. These findings suggest that the HIR technique yields more stable and better cardiac CT images. Our study has some limitations. First, because ours was a pilot study, it included a small number of patients. Our techniques must be rigorously evaluated in large-scale clinical studies. Second, we did not assess diagnostic performance using different stent sizes and types although the visibility of the stent lumen is affected by these factors [11,26]. The usefulness of the HIR algorithm with the high-reconstruction kernel must be evaluated using different sizes and types of stent. Third, we did not address the relationship between heart rate and stent visualization. Our new-generation 256-slice CT machine has a rotation time of 270 ms and may be able to image more accurately at higher heart rates than the 64- slice CT instrument. A higher heart rate may have a negative effect on diagnostic accuracy although in an in vitro experimental study it had no significant effect on in-stent visualization [27]. Fourth, we used retrospectively gated helical data acquisition which is associated with high radiation exposure. Prospective triggering data acquisition may be appropriate for radiation exposure saving. Fifth, in 8 of the 22 patients with no restenosis, in-stent patency was not confirmed by invasive coronary angiography. However, we think that the combination of negative findings on stress-rest myocardial perfusion scintigraphy and the absence of coronary events in the course of 7-month follow-up is highly suggestive of no significant in-stent restenosis. Lastly, we did not compare HIR and other types of iterative reconstruction. The recently introduced sinogram-affirmed iterative reconstruction (SAFIRE)- [28] and model-based iterative reconstruction (MBIR) techniques [29] are the latest advances in the field of iterative reconstruction. Studies are underway to determine which type of iterative reconstruction is more useful for the diagnosis of stent restenosis. 5. Conclusion As the HIR technique combined with high-resolution kernels helps to reduce the image noise and coronary stent blooming artifacts, it significantly improves diagnostic performance for the detection of in-stent stenosis. Disclosure We have no conflicts of interest in the products under investigation or subject matter discussed in this manuscript. References [1] Serruys PW, de Jaegere P, Kiemeneij F, et al. A comparison of balloonexpandable-stent implantation with balloon angioplasty in patients with coronary artery disease. Benestent Study Group. New England Journal of Medicine 1994;331: [2] Fischman DL, Leon MB, Baim DS, et al. A randomized comparison of coronarystent placement and balloon angioplasty in the treatment of coronary artery disease. Stent Restenosis Study Investigators. New England Journal of Medicine 1994;331: [3] Holmes Jr DR, Leon MB, Moses JW, et al. Analysis of 1-year clinical outcomes in the SIRIUS trial: a randomized trial of a sirolimus-eluting stent versus a standard stent in patients at high risk for coronary restenosis. Circulation 2004;109: [4] Morice MC, Colombo A, Meier B, et al. Sirolimus- vs paclitaxel-eluting stents in de novo coronary artery lesions: The REALITY trial: a randomized controlled trial. JAMA 2006;295: [5] Miller JM, Rochitte CE, Dewey M, et al. Diagnostic performance of coronary angiography by 64-row CT. New England Journal of Medicine 2008;359: [6] Kerl JM, Schoepf UJ, Vogl TJ, et al. In vitro evaluation of metallic coronary artery stents with 64-MDCT using an ECG-gated cardiac phantom: relationship between in-stent visualization, stent type, and heart rate. American Journal of Roentgenology 2010;194:W [7] Oncel D, Oncel G, Karaca M. Coronary stent patency and in-stent restenosis: determination with 64-section multidetector CT coronary angiography initial experience. Radiology 2007;242: [8] Schuijf JD, Pundziute G, Jukema JW, et al. Evaluation of patients with previous coronary stent implantation with 64-section CT. Radiology 2007;245: [9] Pugliese F, Cademartiri F, van Mieghem C, et al. Multidetector CT for visualization of coronary stents. Radiographics 2006;26: [10] Taylor AJ, Cerqueira M, Hodgson JM, et al. ACCF/SCCT/ACR/AHA/ASE/ASNC/ NASCI/SCAI/SCMR 2010 appropriate use criteria for cardiac computed tomography. A report of the American College of Cardiology Foundation Appropriate Use Criteria Task Force, the Society of Cardiovascular Computed Tomography, the American College of Radiology, the American Heart Association, the American Society of Echocardiography, the American Society of Nuclear Cardiology, the North American Society for Cardiovascular Imaging, the Society for Cardiovascular Angiography and Interventions, and the Society for Cardiovascular Magnetic Resonance. Circulation 2010;122:e [11] Maintz D, Seifarth H, Raupach R, et al. 64-Slice multidetector coronary CT angiography: in vitro evaluation of 68 different stents. European Radiology 2006;16: [12] Hong C, Chrysant GS, Woodard PK, Bae KT. Coronary artery stent patency assessed with in-stent contrast enhancement measured at multi-detector row CT angiography: initial experience. Radiology 2004;233: [13] Leipsic J, Labounty TM, Heilbron B, et al. Estimated radiation dose reduction using adaptive statistical iterative reconstruction in coronary CT angiography: the ERASIR study. American Journal of Roentgenology 2010;195: [14] Leipsic J, Labounty TM, Heilbron B, et al. Adaptive statistical iterative reconstruction: assessment of image noise and image quality in coronary CT angiography. American Journal of Roentgenology 2010;195: [15] Noël PB, Fingerle AA, Renger B, Rummeny EJ, Dobritz M. A clinical comparison study of a novel statistical iterative and filtered backprojection reconstruction. Physics of medical imaging. Proceedings of the SPIE 2011:7961. [16] Funama Y, Taguchi K, Utsunomiya D, et al. Combination of a low-tube-voltage technique with hybrid iterative reconstruction (idose) algorithm at coronary computed tomographic angiography. Journal of Computer Assisted Tomography 2011;35: [17] Funama Y, Oda S, Utsunomiya D, et al. Coronary artery stent evaluation by combining iterative reconstruction and high-resolution kernel at coronary CT angiography. Academic Radiology 2012;19: [18] Cademartiri F, Nieman K, van der Lugt A, et al. Intravenous contrast material administration at 16-detector row helical CT coronary angiography: test bolus versus bolus-tracking technique. Radiology 2004;233: [19] Obuchowski NA. Receiver operating characteristic curves and their use in radiology. Radiology 2003;229:3 8. [20] Hayter J. A proof of the conjecture that the Tukey Kramer multiple comparisons procedure is conservative. The Annals of Statistics 1984;12:61 75.

8 S. Oda et al. / European Journal of Radiology 82 (2013) [21] Hanley JA, McNeil BJ. A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 1983;148: [22] Mahnken AH, Buecker A, Wildberger JE, et al. Coronary artery stents in multislice computed tomography: in vitro artifact evaluation. Investigative Radiology 2004;39: [23] Kumbhani DJ, Ingelmo CP, Schoenhagen P, Curtin RJ, Flamm SD, Desai MY. Meta-analysis of diagnostic efficacy of 64-slice computed tomography in the evaluation of coronary in-stent restenosis. American Journal of Cardiology 2009;103: [24] Silva AC, Lawder HJ, Hara A, Kujak J, Pavlicek W. Innovations in CT dose reduction strategy: application of the adaptive statistical iterative reconstruction algorithm. American Journal of Roentgenology 2010;194: [25] Utsunomiya D, Weigold WG, Weissman G, Taylor AJ. Effect of hybrid iterative reconstruction technique on quantitative and qualitative image analysis at 256-slice prospective gating cardiac CT. European Radiology 2012;22: [26] de Graaf FR, Schuijf JD, van Velzen JE, et al. Diagnostic accuracy of 320-row multidetector computed tomography coronary angiography to noninvasively assess in-stent restenosis. Investigative Radiology 2010;45: [27] Utsunomiya D, Awai K, Sakamoto T, et al. In vitro evaluation of metallic coronary artery stents with sub-millimeter multi-slice computed tomography using an ECG-gated cardiac phantom: relationship between in-stent visualization and stent type. Cardiology 2007;107: [28] Winklehner A, Karlo C, Puippe G, et al. Raw data-based iterative reconstruction in body CTA: evaluation of radiation dose saving potential. European Radiology 2011;21: [29] Scheffel H, Stolzmann P, Schlett CL, et al. Coronary artery plaques: cardiac CT with model-based and adaptive-statistical iterative reconstruction technique. European Journal of Radiology 2012;81:e363 9.