James K. Min, MD a, *, Rajesh V. Swaminathan a, Melissa Vass b, Scott Gallagher b, Jonathan W. Weinsaft, MD a. Original Research Article.

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1 Journal of Cardiovascular Computed Tomography (2009) 3, Original Research Article High-definition multidetector computed tomography for evaluation of coronary artery stents: Comparison to standard-definition 64-detector row computed tomography James K. Min, MD a, *, Rajesh V. Swaminathan a, Melissa Vass b, Scott Gallagher b, Jonathan W. Weinsaft, MD a a The Greenberg Division of Cardiology, Weill Medical College of Cornell University, The New York Presbyterian Hospital, 520 E 70th Street, K415, New York, NY 10021, USA and b Computed Tomography Engineering, GE Healthcare, Princeton, NJ, USA KEYWORDS: Computed tomography; Iterative reconstruction; Spatial resolution BACKGROUND: The assessment of coronary stents with present-generation 64-detector row computed tomography scanners that use filtered backprojection and operating at standard definition of mm (standard definition, SDCT) is limited by imaging artifacts and noise. OBJECTIVES: We evaluated the performance of a novel, high-definition 64-slice CT scanner (HDCT), with improved spatial resolution (0.23 mm) and applied statistical iterative reconstruction (ASIR) for evaluation of coronary artery stents. METHODS: HDCT and SDCT stent imaging was performed with the use of an ex vivo phantom. HDCT was compared with SDCT with both smooth and sharp kernels for stent intraluminal diameter, intraluminal area, and image noise. Intrastent visualization was assessed with an ASIR algorithm on HDCT scans, compared with the filtered backprojection algorithms by SDCT. RESULTS: Six coronary stents (2.5, 2.5, 2.75, 3.0, 3.5, 4.0 mm) were analyzed by 2 independent readers. Interobserver correlation was high for both HDCT and SDCT. HDCT yielded substantially larger luminal area visualization compared with SDCT, both for smooth ( versus ; P, 0.001) and sharp ( versus ; P, 0.001) kernels. Stent diameter was higher with HDCT compared with SDCT, for both smooth ( versus ; P, ) and detailed ( versus ; P, ) kernels. With detailed kernels, HDCT scans that used algorithms showed a trend toward decreased image noise compared with SDCTfiltered backprojection algorithms. CONCLUSIONS: On the basis of this ex vivo study, HDCT provides superior detection of intrastent luminal area and diameter visualization, compared with SDCT. ASIR image reconstruction techniques for HDCT scans enhance the in-stent assessment while decreasing image noise. Ó 2009 Society of Cardiovascular Computed Tomography. All rights reserved. Conflict of interest: Dr Min serves on the speaker s bureau for GE Healthcare. M. Voss and S. Gallagher are employees of GE Healthcare. The remaining authors report no conflicts of interest. * Corresponding author. address: jkm2001@med.cornell.edu Submitted February 10, Accepted for publication June 6, Introduction The recent introduction of 64-slice multidetector row computed tomography (MDCT), with submillimeter spatial resolution and subsecond gantry rotation, now permits consistent and successful native coronary artery evaluation /$ -see front matter Ó 2009 Society of Cardiovascular Computed Tomography. All rights reserved. doi: /j.jcct

2 Min et al HDCT to evaluate coronary artery stents 247 Prior studies evaluating the diagnostic performance of MDCT for coronary artery evaluation have shown high accuracy for the detection and exclusion of obstructive coronary artery stenosis in general patient populations. 1,2 Despite high overall accuracy, MDCT image quality can be compromised in selected patients. For example, characteristics such as highly calcified vessels, high body mass index, and higher heart rates decrease diagnostic accuracy of MDCT because of partial volume and beam hardening artifacts, excessive image noise, and limited temporal resolution. 3 For similar reasons, diagnostic performance of MDCT for evaluation of coronary artery stents can also be compromised. Although single-center studies of 64-slice MDCT stent assessment have reported diagnostic sensitivity of up to 100%, 2 recent pooled analyses have reported lower overall sensitivity of in-stent restenosis detection of 84% with unassessable stent evaluation in up to 13% of cases. 4 7 Prior analyses from both in vivo and in vitro studies have related stent assessability to size, length, strut design, and alloy type. 8,9 Although decreased collimator width and use of dedicated convolution kernels have improved stent visualization by MDCT, these advances have not been sufficient to overcome artifacts of MDCT related to limitations in spatial resolution. In part because of these limitations, coronary artery stent evaluation by MDCT is considered as yet uncertain for routine clinical use by current appropriateness criteria. 10 Recently, a high-definition CT (HDCT) scanner, with improved in-plane spatial resolution of 230 mm and the ability to reconstruct images with the use of a novel applied statistical iterative reconstruction (ASIR) algorithm, has been developed. We hypothesized that the high spatial resolution provided by HDCT would be particularly useful for coronary stent assessment. To directly test this hypothesis, without the confounding variables of temporal resolution or in vivo stent positioning, an ex vivo phantom model was constructed for dedicated stent imaging. The aim of the present study was to compare stent assessment by HDCT with traditional 64-slice MDCT operating with a standard definition of mm (SDCT). Methods Ex vivo stent phantom model and HDCTversus SDCT scanning protocol In this study, we used a nonbeating, nonmoving stent phantom (Fig. 1). Stent phantoms were cylindrical in shape and composed of nylon. Cylinders were constructed with various diameters, with an internal dimension ranging from 2.4 to 3.5 mm, to closely match nominal stent diameters. Coronary artery stents were inserted into the individual cylinders and inflated to nominal pressures (9 11 atmospheres) in accordance with manufacturer recommendations. Cylinders were filled with iodixanol 270 mg/ml (GE Healthcare, Princeton, NJ) diluted with deionized water at a concentration of 20:1, resulting in target attenuation of approximately 350 Hounsfield units (HU) to best match intraluminal attenuation densities during clinical coronary MDCT angiography. Phantom vessel cylinders were then closed with rubber stoppers and positioned in water-filled containers such that the cylindrical stent phantoms were parallel to the scan plane, ie, perpendicular to the z-axis. The water-filled containers holding the stent phantoms were positioned level on the MDCT table to move into the scan field of view during image acquisition. MDCT scan parameters were matched between HDCT and SDCT. SDCT images were acquired with the use of a MDCT scanner with smooth HiLite detector arrays. Scan parameters for SDCT included 64! collimation, tube voltage of 120 kv, pitch of 0.2, gantry rotation time of 350 milliseconds, scan field of view of 32 cm. Tube current values were controlled for SDCT and HDCT scans, with 250 ma used for all scans. Images were reconstructed at 25 cm and 3.3 cm display field of view filling a 512! 512 reconstruction matrix, with an effective slice thickness of mm and a reconstruction increment of mm, yielding no overlap in reconstruction of the image set. All data sets were acquired with a smooth as well as a detailed convolution kernel to assess differences in optimized assessment of intrastent luminal visualization. HDCT examinations were performed with a prototype based on an MDCT scanner possessing a garnet-based multidetector array with 0.23-mm isotropic resolution. MDCT scan parameters, with the exception of the differences in isotropic resolution, were identical with SDCT. The HDCT prototype possesses the ability to permit image reconstruction with the use of traditional filtered backprojection (FBP) as well as an ASIR. As such, all HDCT examinations were performed with both FBP and ASIR. All imaging was performed on site at General Electric research facilities (GE Healthcare, Milwaukee, WI). Stent characteristics Stent characteristics are described in Table 1. Dimensions of all stents are indicated as diameter! length in millimeters. Two stents were Cypher (Cordis; Johnson & Johnson Inc, Langhome, PA) drug-eluting stents (2.75! 23 mm, 2.50! 23 mm). Four stents were BxVelocity (Cordis; Johnson & Johnson Inc) bare metal stents (2.5! 18 mm, 3.0! 13 mm, 3.5! 18 mm, 4.0! 23 mm). All stents were stainless steel 316L metal with identical platform design and strut thickness of 0.14 mm. CTstent data analysis Analysis was performed by 2 independent experienced readers (J.K.M. and J.W.W.) who were blinded to stent size and type, CT scanner type, convolution kernel, and image reconstruction method. Luminal diameter and area

3 248 Journal of Cardiovascular Computed Tomography, Vol 3, No 4, July/August 2009 Figure 1 Ex vivo stent phantom model. (A) Front view; (B) side view. measurements were performed in the axial plane that showed the greatest diameter or area or both with the use of a window width of 1500 HU and a window level of 500 HU for all stents. Luminal diameter and area measurements were performed with a zoomed display field of view of 5.0 cm. Luminal diameter was measured in 3 prespecified stent locations at both ends and in the center of the stent with an electronic caliper. Luminal area was measured with a manual region of interest (ROI) area tool with inclusion of the intracoronary stent lumen that did not exhibit high attenuation material consistent with the coronary artery stent. To render the borders of the ends of each stent in a consistent manner, experienced readers connected the opposing ends of the stent with each other by a straight line. Image noise was measured in the water surrounding the stent with a ROI of 1.0 cm 2. To minimize errors in image noise measurement, image noise was measured in 3 separate areas within the scan field of view immediately superior to each stent and reported as the mean signal intensity within these 3 ROIs. Statistical analysis SPSS 12.0 (SPSS Inc, Chicago, IL) was used for all statistical analyses. Categorical variables are presented as frequencies and continuous variables as mean 6 1 standard deviation. Variables were compared with a chi-square statistic for categorical variables and by Student s unpaired t test for continuous variables. Interobserver agreement for stent variables was calculated with k statistics. Spearman s rank correlation coefficients were performed to compare image scores for each stent. A 2-tailed P value less than 0.05 was considered statistically significant. Results Intrastent luminal evaluation (HDCTversus SDCT) In comparison to SDCT, stent visualization by HDCT showed a greater intraluminal diameter of 0.3 mm (26.9%; Table 1 Stent Comparative performance for assessment of stent dimensions Model* Diameter, mm Length mm MDCT diameter, mm MDCT area, mm 2 HDCT SDCT Percentage of change HDCT SDCT Percentage of difference 1 Cypher DES * Velocity Velocity Velocity Cypher DES Velocity Average values Cypher (DES) and BxVelocity (BMS) contain stainless steel and have identical platforms with strut thickness of 0.14 mm. All stents were manufactured by Cordis, Johnson & Johnson Inc. *Mean 6 SD (all such values). P, 0.05.

4 Min et al HDCT to evaluate coronary artery stents 249 Figure 2 (Upper panels) HDCT image of stent, with detailed kernel, for diameter (A) and intraluminal area (B) measurements. (Lower panels) SDCT image of stent, with detailed kernel, for diameter (C) and intraluminal area (D) measurements. Note the greater diameter and area calculations with HDCT images (P, 0.05). P, 0.05; Table 1). For intraluminal stent area, HDCT similarly showed greater luminal area, with an average increase of 5.3 mm 2 (23.9%; P, 0.05). Figure 2 provides a representative example of stent assessment by HDCT compared with traditional CT. When SDCT was compared with HDCT on a per stent basis, differences were noted for both stent types tested (Cypher and Velocity). Of the 6 stents tested, 5 had greater intraluminal area by HDCT (relative difference, 4.1% 54%), and 1 (Velocity 3.0 mm) was similar (Table 1). Effect of smooth versus sharp convolution kernels Table 2 presents data for HDCT and SDCT stratified by kernel type. In comparison reconstructions with smooth convolution kernels, the use of sharp kernels resulted in a larger average luminal stent diameter for SDCT (0.23 mm; 22.8%; P, 0.001) and HDCT (0.21 mm;, 13.6%; P, 0.001). Similarly, the use of sharp kernels resulted in a larger average luminal stent area for SDCT (5.31 mm 2 ; 8.0%; P, 0.001) and HDCT (2.60 mm 2 ; 8.8%; P, 0.001). Average image noise by smooth reconstruction kernels, compared with sharp kernels, was similar for SDCT ( versus ; P 5 NS) as well as HDCT ( versus ; P 5 NS). Effect of image reconstruction algorithm Because the ASIR is only available for the HDCT, we evaluated its incremental value to coronary artery stent visualization by testing it with the use of smooth and sharp kernels. The use of ASIR on HDCT resulted in greater mean luminal stent area visualization by 0.13 mm (8.4%) for smooth and 0.08 mm (4.7%) for sharp kernel reconstructions. No differences were observed for image noise between ASIR and FBP with either smooth ( versus ; P 5 NS) or sharp ( versus ; P 5 NS) kernels. Interobserver agreement Interobserver agreement was high for stent diameter (r , P, 0.001) and area (r , P, 0.001) measurements irrespective of CT scanner type, convolution kernel, and method of image reconstruction. Discussion The primary results from this study were that the use of a novel HDCT scanner with 0.23-mm spatial resolution improved coronary artery stent visualization compared with SDCT scanners with mm spatial resolution. The improvements in luminal stent diameter and area visualization ranged from 42.3% to 54.1% and from 25.8% to 54.0%, respectively, with the use of smooth convolution kernels. These improvements in spatial resolution and stent luminal visualization did not occur at the expense of increased image noise, as has been reported with the use of sharp convolution kernels. Sharp convolution kernels can be substituted for smooth kernels to improve stent visualization by HDCT. Although a smooth kernel may reduce image noise, it does so at the cost of reducing spatial resolution. A sharp kernel, aimed at edge enhancement, may result in higher spatial resolution but also with more image noise. Interestingly, in the present study, image reconstruction with the use of the HDCT scanner with a sharp kernel appeared to result in improved visualization without significantly increasing image noise. The current study also used a novel ASIR method to reconstruct CT images. In comparison to FBP techniques, ASIR techniques are theoretically more accurate in the modeling of physical noise and tissue geometries. Prior in

5 250 Journal of Cardiovascular Computed Tomography, Vol 3, No 4, July/August 2009 Table 2 Comparative effect of image reconstruction and kernel design on stent assessment Luminal diameter Image reconstruction Percentage of change Mean 6 SD P value SDCT 22.8,0.001 Smooth kernel FBP Sharp kernel FBP HDCT 13.6,0.001 Smooth FBP Sharp FBP HDCT, Smooth ASIR Sharp ASIR Luminal area SDCT 27.4,0.001 Smooth FBP Sharp FBP HDCT 8.8,0.001 Smooth FBP Sharp FBP HDCT 5.9,0.001 Smooth ASIR Sharp ASIR vitro studies have shown improved image quality for enhanced image resolution as well as lower image noise by use of these Bayesian iterative algorithms. 11 In our study, no reduction in image noise was identified; however, improved coronary artery stent luminal visualization was noted, indicating the potential for this method of image reconstruction to improve coronary artery stent imaging by CT. Note that in our study, interobserver agreement was high, irrespective of CT scanner type, convolution kernel, or image reconstruction algorithm. These data suggest that the significantly improved luminal stent diameter and area visualization was due to true increases in spatial resolution rather than to interobserver variability. The current findings have potential clinical implications. To date, coronary artery stents have been difficult to image by current-generation SDCT systems. Because of their high attenuation characteristics and the limited spatial resolution of SDCT systems, coronary artery stents are susceptible to partial volume and beam hardening artifacts. 4 7 Partial volume artifacts occur when multiple tissue types exist in such close proximity to one another such that they are represented within a common voxel in the reconstructed image. In these situations, the resulting Hounsfield unit value assigned to the voxel is a weighted average of the values of the different tissues imaged. Coronary artery stents can be associated with partial volume artifacts, which can preclude luminal assessment. The present study suggests that these artifacts are diminished with HDCT. Future studies to examine the diagnostic performance of HDCT with SDCT for coronary artery stent evaluation in vitro and in vivo will be useful for determining further clinical significance. There are several limitations of this study. First, it is important to note that, although HDCT provided improved stent visualization compared with SDCT, both techniques substantially underestimated actual stent sizes. Potential reasons include incomplete inflation during deployment, strut thickness, metal-associated image artifact (ie, blooming), and stent angulation during CT measurement. Furthermore, coronary artery stents in the present study were performed on a nonmoving phantom. We deliberately chose to use a nonmoving phantom to determine the true effect of enhanced spatial resolution. Thus, the effect of temporal resolution in relation to stent accessibility could not be examined. The data in this study do not apply to the evaluation of in-stent restenosis. Instead, we chose to evaluate the luminal area and diameter by HDCT to determine a proof of concept that enhanced spatial resolution did indeed result in higher stent diameter and area visualization. Future studies will be performed to evaluate the diagnostic accuracy of detecting gradations of in-stent restenosis. Finally, the image reconstruction evaluated with HDCT was an ASIR, rather than true iterative reconstruction. Although it is possible to perform true iterative reconstruction image reconstruction, the computational time required to achieve such reconstruction is on the order of several hours. The use of ASIR can be performed within minutes and thus is presently more clinically applicable. Conclusions and clinical implications The present study evaluated the comparative efficacy of HDCT, compared with SDCT, for coronary stent assessment.

6 Min et al HDCT to evaluate coronary artery stents 251 HDCT, with improved in-plane spatial resolution of 0.23 mm, results in improved measurements of stent diameter and area. Novel ASIR image reconstruction techniques incrementally improved assessment of stent diameter and area. Further study of these methods for in vivo coronary artery stent evaluation and more broadly for the evaluation of calcified atherosclerotic plaque within native coronary arteries and surgical grafts is warranted. References 1. Budoff MJ, Dowe D, Jollis JG, Gitter M, Sutherland J, Halamert E, Scherer M, Bellinger R, Martin A, Benton R, Delago A, Min JK: Diagnostic performance of 64-multidetector row coronary computed tomographic angiography for evaluation of coronary artery stenosis in individuals without known coronary artery disease: results from the prospective multicenter ACCURACY (Assessment by Coronary Computed Tomographic Angiography of Individuals Undergoing Invasive Coronary Angiography) trial. J Am Coll Cardiol. 2008;52: Hamon M, Biondi-Zoccai GG, Malagutti P, Agostoni P, Morello R, Valgimigli M, Hamon M: Diagnostic performance of multislice spiral computed tomography of coronary arteries as compared with conventional invasive coronary angiography: a meta-analysis. J Am Coll Cardiol. 2006;48: Raff GL, Gallagher MJ, O Neill WW, Goldstein J: Diagnostic accuracy of noninvasive coronary angiography using 64-slice spiral computed tomography. J Am Coll Cardiol. 2005;46: Manghat N, Van Lingen R, Hewson P, Syed F, Kakani N, Cox I, Roobottom C, Morgan-Hughes G: Usefulness of 64-detector row computed tomography for evaluation of intracoronary stents in symptomatic patients with suspected in-stent restenosis. Am J Cardiol. 2008;101: Hecht HS, Zaric M, Jelnin V, Lubarsky L, Prakash M, Roubin G: Usefulness of 64-detector computed tomographic angiography for diagnosing in-stent restenosis in native coronary arteries. Am J Cardiol. 2008;101: Cademartiri F, Schuijf JD, Pugliese F, Mollet NR, Jukema JW, Maffei E, Kroft LJ, Palumbo A, Ardissino D, Serruys PW, Krestin GP, Van der Wall EE, de Feyter PJ, Bax JJ: Usefulness of 64-slice multislice computed tomography coronary angiography to assess in-stent restenosis. J Am Coll Cardiol. 2007;49: Hamon M, Champ-Rigot L, Morello R, Riddell JW, Hamon M: Diagnostic accuracy of in-stent coronary restenosis detection with multislice spiral computed tomography: a meta-analysis. Eur Radiol. 2008;18: Seifarth H, Ozgun M, Raupach R, Flohr T, Heindel W, Fischbach R, Maintz D: 64- Versus 16-slice CT angiography for coronary artery stent assessment: in vitro experience. Invest Radiol. 2006;41: Maintz D, Seifarth H, Raupach R, Flohr T, Rink M, Sommer T, Ozgun M, Heindel W, Fischbach R: 64-slice multidetector coronary CT angiography: in vitro evaluation of 68 different stents. Eur Radiol. 2006;16: Hendel RC, Patel MR, Kramer CM, Poon M, Hendel RC, Carr JC, Gerstad NA, Gillam LD, Hodgson JM, Kim RJ, Kramer CM, Lesser JR, Martin ET, Messer JV, Redberg RF, Rubin GD, Rumsfeld JS, Taylor AJ, Weigold WG, Woodard PK, Brindis RG, Hendel RC, Douglas PS, Peterson ED, Wolk MJ, Allen JM, Patel MR: ACCF/ACR/SCCT/SCMR/ASNC/NASCI/SCAI/SIR 2006 appropriateness criteria for cardiac computed tomography and cardiac magnetic resonance imaging: a report of the American College of Cardiology Foundation Quality Strategic Directions Committee Appropriateness Criteria Working Group, American College of Radiology, Society of Cardiovascular Computed Tomography, Society for Cardiovascular Magnetic Resonance, American Society of Nuclear Cardiology, North American Society for Cardiac Imaging, Society for Cardiovascular Angiography and Interventions, and Society of Interventional Radiology. J Am Coll Cardiol. 2006;48: Thibault JB, Sauer KD, Bouman CA, Hsieh J: A three-dimensional statistical approach to improved image quality for multislice helical CT. Med Phys ;34:

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