optical coherence tomography for patient-specific 3d artery

Optical Coherence Tomography for Patient-specific3D Artery Reconstruction and Evaluation of Wall Shear

Stress in a Left Circumflex Coronary Artery

LAURA M. ELLWEIN,1 HIROMASA OTAKE,2,3 TIMOTHY J. GUNDERT,1 BON-KWON KOO,4 TOSHIRO SHINKE,3

YASUHIRO HONDA,2 JUNYA SHITE,3 and JOHN F. LADISA JR.1,5,6

1Department of Biomedical Engineering, Marquette University, 1515 West Wisconsin Ave, Milwaukee, WI 53233, USA;2Stanford University, 300 Pasteur Drive H3554, Palo Alto, CA 94305, USA; 3Kobe University Graduate School of Medicine,

7-5-1 Kusunoki-cho, Chuo-ku, Kobe 650-0017, Japan; 4Seoul National University College of Medicine, 101 Daehang-ro,Chongno-gu, Seoul 110-744, Korea; 5Department of Medicine, Division of Cardiovascular Medicine, Medical College of

Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA; and 6Department of Pediatrics, Division of Pediatrics,Childrens Hospital and the Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA

(Received 3 January 2011; accepted 2 May 2011; published online 18 May 2011)

Associate Editor Stephen B. Knisley oversaw the review of this article.

AbstractImage-based computational models for quantify-ing hemodynamic indices in stented coronary arteries oftenemploy biplane angiography and intravascular ultrasoundfor 3D reconstruction. Recent advances in guidewire simu-lation algorithms and the rise of optical coherence tomog-raphy (OCT) suggest more precise coronary arteryreconstruction may be possible. We developed a patient-specific method that combines the superior resolution ofOCT with techniques for imaging wire pathway reconstruc-tion adopted from graph theory. The wire pathway withminimum bending energy was determined by applying ashortest path algorithm to a graph representation of theartery based on prior studies indicating a wire adopts thestraightest configuration within a tortuous vessel. Segmentsfrom OCT images are then registered orthogonal to the wirepathway using rotational orientation consistent with geom-etry delineated by computed tomography (CT). To demon-strate applicability, OCT segments within the stented regionwere combined with proximal and distal CT segments andimported into computational fluid dynamics software toquantify indices of wall shear stress (WSS). The method wasapplied to imaging data of a left circumflex artery withthrombus acquired immediately post-stenting and after a6-month follow-up period. Areas of stent-induced low WSSreturned to physiological levels at follow-up, but correlatedwith measurable neointimal thickness in OCT images.Neointimal thickness was negligible in areas of elevatedWSS due to thrombus. This novel methodology capable ofreconstructing a stented coronary artery may ultimatelyenhance our knowledge of deleterious hemodynamic indicesinduced by stenting after further investigation in a largerpatient population.

KeywordsComputational fluid dynamics, Coronary artery

reconstruction, Wall shear stress, Optical coherence tomog-

raphy, Neointimal thickness, Numerical modeling.

INTRODUCTION

One in three adults in the U.S. have some form ofcardiovascular disease, of which 51% suffer fromcoronary artery disease.38 The development of stentinghas revolutionized treatment of coronary artery dis-ease, but restenosis has limited the success of baremetal stents (BMS) in ~3051% of patients.13,41 Drug-eluting stents (DES) that decrease rates of restenosiswere approved by the FDA in 2003 as an alternative toBMS, but may be more susceptible to late stentthrombosis20 due to several factors including subopti-mal endothelial cell coverage.10,26 Although the inci-dence of late stent thrombosis is just 12% after DESimplantation, ~5070% of these patients may experi-ence myocardial infarction as a result.21,28

Interestingly, stent implantation causes changes invascular geometry resulting in altered local blood flowvelocity and distributions of wall shear stress (WSS)that have been shown to correlate with NH35 and theability of endothelial cells to migrate onto stent sur-faces.17,23,47 Computational fluid dynamics (CFD)simulations performed with patient-specific 3D modelsof the coronary arteries play a key role in quantifyingindices of WSS.27,61 CFD models have previouslybeen constructed using combinations of angiographyand intravascular ultrasound (IVUS),11,27,66 but the

Address correspondence to Laura M. Ellwein, Department of

Biomedical Engineering, Marquette University, 1515 West Wiscon-

sin Ave, Milwaukee, WI 53233, USA. Electronic mail: laura.

[email protected]

Cardiovascular Engineering and Technology, Vol. 2, No. 3, September 2011 ( 2011) pp. 212227DOI: 10.1007/s13239-011-0047-5

1869-408X/11/0900-0212/0 2011 Biomedical Engineering Society

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resolution of these imaging modalities (e.g., IVUS at~150 lm)45 compared to the intricacies of a stentedartery may cause the loss of spatial informationimportant for accurate model reconstruction7,14,46,55

and CFD analysis. Optical coherence tomography(OCT) has been used in coronary arteries since 2007 inJapan and 2010 in the United States to analyze pre-procedural lesion characteristics and the efficacy ofstent implantation, offering 10 times the resolution ofIVUS.5,8,45 Coupling OCT data with image registra-tion and vessel reconstruction methods to create 3Dcoronary artery models for CFD analysis may there-fore provide critical insight into the relationshipbetween deleterious indices of WSS established afterDES and their influence on thrombus or NT.

Accurately reconstructing the pullback pathway ofan intravascular imaging device and subsequent ori-entation of images along the pathway are arguably themost critical aspects of 3D artery reconstruction usingintravascular imaging modalities. Biplane angio-graphic imaging data was the primary source forextracting the imaging pathway in earlier stud-ies,29,48,58 but its use can introduce complications suchas non-orthogonal planes, an undetectable imagingwire, and the need to compensate for motion or addcalibration steps beyond the clinical norm.6,9,24,48,50,55

The rotational orientation of images along a curvedpathway estimated with points extracted from biplaneangiography has also depended on the curves func-tional representation, potentially causing inaccuracieswith image orientation stemming from multiple func-tions closely representing the same curve or numericalinstability of the functions derivatives.27,29,49,55,58,64

Further orientation issues including rotational invari-ance about the wire pathway and an inability to cap-ture vessel irregularities have occurred when usingangiographic images for lumen matching.40,48,55,64

These obstacles suggest that a method independent ofbiplane angiography is desirable, particularly for ret-rospective use.

Several recent studies have focused on predicting thepathway of a guidewire in a tortuous vessel based onthe principle of minimum bending energy. Early workwas conducted in the context of simulating the guide-wire insertion pathway for intervention simulation1,25

but later studies focusing on pullback pathway repro-ducibility52,53 are more applicable to the currentinvestigation. Applicable techniques are taken fromthe field of graph theory and produce a unique path-way for a guidewire that could be further used in vesselreconstruction algorithms. If this approach wereimplemented within a CFD modeling process it wouldbe possible to create patient-specific models usingclinical imaging data independent of the limitationsmentioned above.

The objective of the current investigation was todevelop an alternative method for patient-specificcoronary artery reconstruction using the superiorspatial resolution of OCT and a novel implementationof graph theory. Specifically, this investigation focusedon OCT image segment registration onto an imagingwire pullback pathway determined by implementingDijkstras algorithm on a graph representation of theleft circumflex artery (LCX). The method was appliedin a retrospective manner using computed tomography(CT) and OCT data of a single patients LCX acquiredimmediately after stenting (post-stent) in the pres-ence of thrombus, and again after a 6-month period(follow-up). These arterial reconstructions were usedwith CFD to demonstrate the utility of quantifyingindices of WSS for comparison with NT measuredfrom OCT images.

METHODS

The methods developed to complete our objectiveare divided into four processes as illustrated by theworkflow displayed in Fig. 1: Data Collection, ModelConstruction, Simulation, and Results Quantification.Process 2, Model Construction, consists of five stageslabeled A. Image processing, B. Wire pathway recon-struction, C. Segment registration, D. Model assembly,and E. Stent implantation. Stage 2B is the focus of thisinvestigation and consists of four steps that are com-pleted before Stage 2C commences.

Data Collection

This investigation was approved by the ethicalcommittee of Kobe University and InstitutionalReview Board at Marquette University. Informedconsent was obtained for the use of data in this casestudy. Coronary angiography and OCT imaging wereperformed immediately and 6 months after DESimplantation as previously reported.46 OCT imageswere acquired with a time-domain M2 OCT ImagingSystem (LightLab Imaging Inc., Westford, MA, USA)having axial and lateral resolutions of 15 and 25 lm,respectively.5 A 0.016-inch OCT fiber-optic imagingwire with a photodetector at its tip (ImageWire,LightLab Imaging, Westford, MA, USA) wasadvanced to the distal end of the stented lesion throughan occlusion balloon catheter (HeliosTM, LightLabImaging Inc., Westford, MA, USA). The occlusionballoon was inflated to 0.5 atm at the proximal site ofthe stented lesion, and Lactated Ringers solution wasinfused into the coronary artery from the distal tip ofthe occlusion balloon catheter at 0.5 mL/s to flush thearea clear of blood. The entire stented length was then

OCT for Patient-specific 3D Artery Reconstruction 213

imaged using an automatic pullback speed of 1 mm/sat 15 frames/s. The lesion was also evaluated beforestent implantation with a 64-slice multidetector CTscanner resulting in a voxel size of 0.39 9 0.39 90.5 mm (Aquillion 64, Toshiba Medical Systems,Tokyo, Japan). Volume-rendered CT data and a cor-responding longitudinal OCT cross-section are shownin Fig. 2.

Analysis of OCT images was performed by anindependent observer who was blinded to clinicalpresentation and lesion characteristics. Cross-sectionalOCT images were quantified at 1-mm intervals. Lumenarea was determined by tracing the leading edge of theblood/intima border. Stent area was delineated by firstmarking the center of each stent strut, then automati-cally interpolating between strut locations using theintegrated Lightlab image analysis software.43 Thesetracings (hereafter referred to as segments) are markedby white and cyan contours, respectively. Intracoro-nary thrombus was defined as a protruding massbeyond the stent strut into the lumen with significantattenuation behind the mass.46

Model Construction

The model construction process contains five dis-tinct stages: A. Image processing, B. Wire pathwayreconstruction, C. Segment registration, D. Modelassembly, and E. Stent implantation. The first fourstages are common to both the post-stent and follow-up OCT data sets, but are implemented separately oneach. The final stent implantation stage is applicable tothe post-stent data only. Resulting computationalmodels represent the LCX from ~10 mm proximal to

the stent (just distal to the 1st obtuse marginal artery)to ~10 mm distal to the bifurcation at the secondobtuse marginal (OM2), including portions of both

FIGURE 1. Diagram of the workflow developed and implemented for the current investigation. The workflow is divided into fourprocesses including 1. Data Collection, 2. Model Construction, 3. Simulation, and 4. Results Quantification. Process 2, ModelConstruction, consists of five stages labeled A. Image processing, B. Wire pathway reconstruction, C. Segment registration,D. Model assembly, and E. Stent implantation. Stage 2B is the focus of this investigation and consists of four steps that arecompleted before Stage 2C commences.

FIGURE 2. Left: Volume rendering of computed tomographydata obtained from the left circumflex coronary artery (LCX)prior to stent implantation. The thrombus, evident by a lack ofbright blood flow in the LCX, is located in the distal half of thestented region. Right: Longitudinal view of OCT data from post-stent assessment. A similar image was also acquired during thefollow-up assessment. OM1: first obtuse marginal artery. OM2:2nd obtuse marginal artery. pLCX: LCX proximal to the stentedregion. dLCX: LCX distal to the stented region and the OM2.

ELLWEIN et al.214

bifurcating arteries. The distal end of the stent waslocated just proximal to the OM2 bifurcation. Specificcomputations used for these stages discussed indetail below were performed in Matlab (MathWorks;Natick, MA) unless otherwise specified.

Stage A. Image Processing

OCT images were processed in Matlab using algo-rithms provided in the Image Processing Toolbox.Images were converted to grayscale, masked to removemeasurement markings, and thresholded relative to thecolor of segments, resulting in binary images of theoriginal OCT images with a black background andsegment pixels isolated in white. Pixels were convertedto Cartesian coordinates with the z-coordinate for eachimage segment representing its pullback distance.Segments were then aligned vertically in succession andspaced using the known pullback rate of 15 frames/mm, resulting in a vertical stack of OCT segmentspositioned with the z-axis intersecting each segment atits centroid.

Stage B. Wire Pathway Reconstruction

During OCT, images are acquired orthogonal to thephotodetector at the tip of the imaging wire and do notnecessarily lie on the vessel centerline, thus requiringdetermination of the wire pullback pathway to ensureprecise segment registration. As discussed in the Intro-duction, the wire follows the straightest pathway withinthe tortuous vessel (i.e. it minimizes its total bendingenergy) provided the vessel tortuosity is above a value of1.2, defined as the ratio between the total centerline arclength and the direct distance between centerline startand end points.53 The pathway is therefore determinedby framing the problem as an optimization for whichboth a spatial network representation of the set ofpossible wire pathways and an associated graph repre-sentation are required. This network-graph dual repre-sentation is adopted from Schafer et al.53 and isdemonstrated below for a simple case.

Demonstration of ConceptThe simple case of a vessel comprised of threeorthogonal planes Pi and two points Pij per plane canbe expressed using the network-graph dual represen-tation introduced above. Sets of coplanar points areconnected such that a joint e = (v1, v2) at a candidatepoint is defined to be a composition of two vectorsv = a...l connecting the point with candidate points insuccessive proximal and distal sets of points (Fig. 3a).For example, v = e and v = k connect points P12, P22,P32 in consecutive planes P1, P2, and P3. This networkof joints connected by vectors encompasses a spectrumof possible wire pathways, and the wire pullback

pathway as determined by the optimization algorithmpasses through one point in each set of points on eachplane starting at one end of the vessel.

The network of possible pathways is further repre-sented for the simple case as a directed graph G = (V,E), with joints now indicated as edges (E) and theconnecting vectors as vertices (V) (Fig. 3b). Graphvertices, v 2 V, correspond to network vector connec-tions between joints that comprise the possible wirepathways, labeled as a...l as were the vectors of thenetwork. Graph edges, e 2 E, represent the jointsconnecting vectors between Pi, e.g., e = (e, k). Repre-senting the network as a graph allows us to implementa minimization algorithm that does not search over allconnections between all candidate points, but insteadconsiders only connections that make valid pathwaysbased on previous connections and pathway direction.In the simple case, a-e-k-l is a valid pathway but a-e-g-land l-k-e-a are not. The vertices a and l are added toensure single source and destination vertices for thegraph and vectors defining joints for the first and lastplanes of the network. The resulting start and endpoints created in the network are outside of the planesand do not affect the minimization.

The pathway with minimum bending energy com-posed of an ordered set of joints is determined by

FIGURE 3. (a) Network and (b) graph representations of asimple case vessel with three planes and two points perplane. (a) Vectors in the network diagram are labeled as a...land colored light grey. Joints e are connections between twovectors and colored dark grey. The joint e 5 (e, k) is indicatedby dotted lines with its angle measurement hek. Segments areshown as black ellipses and the centerline is dotted grey,passing through centroids marked with a grey x. (b) Verticesand edges of the graph correspond to vectors and joints ofthe network, with vertices labeled a...l in light grey and edgesin dark grey. The weight h2ek is assigned to edge e 5 (e, k).Vertices a and l are added to the graph to ensure singlesource and destination vertices for the shortest path algo-rithm. All edges adjacent to these vertices a and l are givenequal weights because these vertices lack physical meaningbut are included for completeness.


minimizing total bending energy, Utotal =P

Ubend,over all pathways. Bending energy, Ubend, at each jointis assigned as the weight for each corresponding graphedge. It is defined as Ubend = (1/2)Ch

2 according to anadaptation of Hookes law for molecules as is cus-tomary in previous studies,1,4 where h is the supple-ment of the angle of the joint (i.e. an angle of 0 is astraight joint) and the parameter C comprises severalconstant spring parameters. The minimization problemis independent of the value of any multiplied constants,thus the contribution of the constants is negligible andthe square of the joint angles, h2, were assigned asedge weights. The angle h was calculated from thestandard cosine formula, e.g.,hek 180 cos1ve vk=jvejjvkj between vectors e and k (see Fig. 3b).

The minimum path of the graph is calculated withthe aid of numerical methods. These methods requirean adjacency matrix with ones indicating a directededge between two vertices and zeros indicating noedge, as well as a cost matrix with the calculatedweights that are assigned to each edge includedas matrix entries where ones are located. Rows ofthe matrix indicate the starting vertex and columns theending vertex for each edge. Figure 4 shows thematrices that correspond to the simple case detailed inFig. 3 with entries for edge e-k highlighted. The firstrow, last column, and block matrices in the interiorhave a structure directly related to the network,e.g., the 4 9 4 block matrix for the simple case indi-cates the 8 joints on plane P2. All edges adjacent tovertices a and l are given equal weights because thesevertices lack physical meaning but are included for

completeness. The output of the numerical method isthe succession of joints that make up the minimumbending energy pathway.

ImplementationThe imaging wire pathway for the coronary vessel inthis investigation was determined using the procedureoutlined above. OCT image segments processed inStage A were used to determine the necessary orthog-onal planes and sets of coplanar candidate points be-cause they contain 2D lumen centroid and wireposition information that lends greater precision to theanalysis. The steps for obtaining the imaging wirepathway are listed below followed by a detailedexposition:

Step 1. Identify planes orthogonal to the vesselcenterline using CT data and OCT segments.

Step 2. Identify sets of coplanar candidate pointslying on the orthogonal planes that will comprise thejoints of the possible wire pathways.

Step 3. Formulate adjacency and cost matrices thatdescribe the graph of the network formed by the sets ofcoplanar candidate points.

Step 4. Apply Dijsktras algorithm to the graphusing its adjacency and cost matrices to obtain thepathway of minimum bending energy.

Step 1. Identify orthogonal planes. As a firstapproximation of OCT segment registration and todesignate orthogonal planes within the imaged region,segments are placed longitudinally on the LCX cen-terline at their centroids. The centerline was generatedfrom the CT data using the open-source softwarepackages ITK-Snap (www.itksnap.org) and VMTK (www.vmtk.org) as previously described.3 Briefly, 3Dsegmentation in ITK-Snap was performed usingintensity regions with lower threshold of 175 and noupper threshold, and snake propagation of 25 itera-tions. The length of the centerline used for the analysisextended from the 1st obtuse marginal artery toapproximately 60 mm distal to the OM2. Longitudinalspacing along the centerline was ensured by firstanchoring image segments acquired at two landmarks,the OM2 and a smaller (side) branch originating fromthe middle of the stented region, with lumen centroidcoordinates at these positions extracted using ITK-Snap. Remaining segments were distributed accordingto the wire pullback speed.

Segments (S) are transformed from the vertical (v)stack to the centerline (c) by using the inverse of therotation matrix (R) that aligns the centerline tangent(t) at each longitudinally spaced centerline registrationpoint with the z-axis of the vertical stack (Fig. 5). Thispair of operations is represented mathematically by theequations Rtc = tv and R

1Sv = Sc, where R isconstructed such that the x and y coordinates of

FIGURE 4. Examples of adjacency and cost matrices for asimple case vessel with three planes, two points per plane (seeFig. 3), required for the numerical implementation of a shortestpath algorithm. Rows indicate the starting vertex and columnsthe ending vertex for each edge. The 4 3 4 matrix in the blackbox indicates the 8 joints found on plane P2, showing also thestructure of the block matrix exploited for automated matrixconstruction for a larger network. The smaller dotted boxhighlights the e-k joint/edge with weight hek.

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http://www.itksnap.orghttp://www.vmtk.org

tv equal 0. The resulting segments Sc are orthogonal tothe vessel centerline, with segment normals originatingat the segment centroid aligned with centerline tan-gents. Sc determines orthogonal planes Pi in theimaged region used for the remaining steps. In addi-tion, a sufficient number of orthogonal planes proxi-mal and distal to the region imaged by OCT werecalculated using centerline tangents to ensure accuracywhile minimizing computation time,53 resulting in atotal of 33 planes.

Step 2. Identify sets of coplanar points. Orthogonalplanes identified in the previous step were used todefine sets of 13 coplanar candidate points,Pij, j = 1...13.Proximal and distal to the region imaged by OCT, eachset ofPij included the centroid, 4 points equally spaced at1/3 radius from the centroid, and 8 points equally spaced2/3 radius from the centroid (Fig. 6a), inorder to span thecross-section. For the planes in the imaged region, thedistance between each segments centroid and wire loca-tion was known by direct measurement from the OCTimages. Therefore the Pij were created within the planesegments using 13 equally spaced points surrounding thecentroid at a radius equal this distance (Fig. 6b). Finally,for the two segment planes anchored at branch land-marks, coordinates for the wire points were explicitlydetermined from ITK-Snap, similar to what was done inStep 1 for anchoring centroid coordinates. Note thataside from the two anchored segments, rotational posi-tioning of remaining segments is unknown at this step,thus the need forPij that circumferentially span the lumencross-section.

Step 3. Formulate adjacency and cost matrices. In thisinvestigation, adjacency and cost matrices were gener-ated using a custom automated script that exploited thefact that the same number of joints exist on each plane.Matrices were square with size (N 1)M2 + 2, whereN is the number of defined planes and M is the number

of points per plane (33 and 13 for this investigation,respectively). Similar to what was shown in Fig. 4, thefirst row of the adjacencymatrix consists of one zero,M2

ones, and the remaining entries as zeros, while the lastcolumn begins with zeros and ends with M2 ones andone zero. The (N 2)M2 columns prior to the lastcolumn consist of block matrices of size M2. The fixedpoint in each of the two anchor planes described inStep 2 was treated as if it was 13 identical points in orderto simplify matrix generation.

Step 4. Solve shortest path problem to minimizebending energy. A validated numerical Dijkstrasalgorithm script (MathWorks, www.mathworks.com)was implemented in Matlab to evaluate the shortestpath from the source to the destination vertex using theadjacency and cost matrices determined in Step 3. Theoutput of the script is the succession of joints, and thusthe candidate points, that make up the minimumbending energy pathway. To refine the location ofthese points, Dijkstras algorithm was implemented asecond time on a new set of 13 candidate points withineach plane that included each respective point foundfrom the first implementation of the algorithm. Theencompassing radius for the new set of candidatepoints was less than 1/3 of the lumen radius at eachlocation to ensure the new points remained within thevessel. The final set of pathway points was interpolatedto the nearest 0.1 mm to determine the entire pathway.

Stage C. Segment Registration

Registration of OCT segments on the wire pathwaywas performed in a manner similar to what was done

FIGURE 5. Diagram of OCT segment placement onto vesselcenterline. (a) A matrix R is calculated at each longitudinallyspaced centerline registration point that rotates centerlinetangents tc to align with the z-axis of the vertical stack suchthat the x and y coordinates of the resulting tv equal 0. (b) Thematrix R21 is applied to the segments Sv in the vertical stack,creating orthogonal segments Sc on the vessel centerline.

FIGURE 6. (a) Representative orthogonal set of 13 coplanarwire pathway candidate points Pij proximal to the imaged re-gion, including the point at the lumen centroid marked with ablack x, 4 points equally spaced at 1/3 radius from the cen-troid, and 8 points equally spaced 2/3 radius from the cen-troid. Points are shown within a volume rendering of CT dataused for centerline determination. (b) Representative post-stent OCT image with lumen (white) and stent (cyan) segmentshowing locations of 13 Pij marked with white dots and lumencentroid with a white x. Points are equally spaced sur-rounding the centroid at a radius equal to the distancebetween the centroid and wire location at the image center(white arrow).


http://www.mathworks.com

to identify orthogonal planes as described in Step 1 ofStage B, noting that the wire pathway passes throughthe center of each image. Segments from images at theOM2 and side branch were anchored according to thefixed coordinates of where the wire passes througheach segment. Remaining segments were spaced lon-gitudinally by their image centers along the wirepathway determined in Stage B according to wirepullback speed. Segments are transformed onto thewire pathway by applying the inverse of a new rotationmatrix that aligned the tangent at each longitudinallyspaced pathway registration point with the z-axis of avertical stack of the images. As a result, segment nor-mals originating where the wire intersects the segmentplane align coincident with pathway tangents, thusplacing segments orthogonally on the wire pathway atthese intersection points. To account for rotationalorientation, each image segment was rotated within itsorthogonal plane such that the wire pathway point(image center), the segment centroid, and the point ofintersection between the centerline and the orthogonalplane were aligned. Final segment alignment for seg-ments in the stented region is shown in Fig. 7a.

Stage D. Model Assembly

To be consistent across the two data sets for thesingle patient and to include the OM2 bifurcation in themodels of the flow domain, the regions proximal anddistal to the stented regions were created with the CTdata in ITK-Snap and combined with the stentedregion to form the entire vessel. These regions includethe LCX proximal to the stented region (pLCX) andthe bifurcation of the distal LCX (dLCX) and OM2.Evaluation of the flow waveform used as the inflowboundary condition indicated that the pLCX shouldbe extended to accommodate an entrance length of11 mm and bifurcating artery branches were similarlyextended. 3D segmentation in ITK-Snap was per-formed using intensity regions with lower thresholds of125 (OM2) or 175 (pLCX, dLCX) and no upperthreshold. Snake propagation continued for 10 (OM2)or 25 iterations (pLCX and dLCX). 3D representationsof the pLCX and dLCX were queried using customizedscripts in Matlab to create orthogonal segments defin-ing the vessel lumen. These segments were importedinto the open-source software Simvascular (https://simtk.org/home/simvascular) together with the pro-cessed segments from the stented region registered onthe wire pathway. The total collection of segments wasthen lofted and blended to form a Parasolid model(Siemens, Plano, TX) of the full flow domain. Thefollow-up flow domain model reconstructed fromlumen segments in follow-up OCT images was completeafter these first four steps described above (Fig. 7b).

Stage E. Stent Implantation

To create the post-stent LCX flow domain model,a stent was virtually implanted proximal to thedLCX/OM2 bifurcation using a method similar tothat described by Gundert et al.,16 which was modifiedto account for a patient-specific flow domain thatmay contain plaque or thrombus in the stentedregion (Fig. 8). Briefly, a first-generation drug-elutingCYPHER stent (Cordis Corporation, Bridgewater,NJ) that was used clinically for this patient was createdin Solidworks (Solidworks Corp. Concord, MA) fromthe known geometry of the stent but with accentuatedradial thickness. Separate solid models of the vessellumen and the lofted outer surface of the stent wereconstructed from the white and cyan segments of theOCT images (Fig. 8a), respectively, using the proceduredescribed in Stages A-D above (Figs. 8b and 8e). Thestent radial thickness was then subtracted from the solidmodel of the lofted stent to create a thinner lofted stentmodel (Fig. 8b). Subtracting the thinner lofted stentmodel from the thick stent yielded a patient-specificstent model that mimicked the inner surface of the stent(Figs. 8c and 8d). The final subtraction of this stentmodel from the lumenmodel (Fig. 8f) generated the flowdomain used for subsequent CFD simulations (Figs. 8gand 7c). Because the stent and lumen are created sepa-rately, thrombus and other features of the vessel wallthat partially cover stent struts are represented in theresulting flow domain (dashed box, Fig. 7c).

FIGURE 7. (a) Final alignment of OCT segments in thestented region (dark grey) in 3D space with the centerline inlight grey and the wire pathway in black. (b), (c) Computa-tional solid model representations of vessel flow domains forthe follow-up and post-stent phases, respectively. Thethrombus area is outlined by the black dashed box.

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https://simtk.org/home/simvascularhttps://simtk.org/home/simvascular

Simulation: Boundary Conditions and Parameters

Post-stent and follow-up vessels were subjected to acanine pulsatile blood flow waveform contourobtained from the left anterior descending coronaryartery32 similar in size to the current patients LCX,and with features including Reynolds and Womersleynumbers which have allowed the waveform to bepreviously applied in many computational stud-ies.31,33,34,69 Based on the patients cardiac output(Table 1), mean blood flow for the LCX was deter-mined assuming the coronary arteries receive 5% ofcardiac output, 84% of which is allocated to the leftmain coronary artery from which the LCX receives33%.36 The flow waveform contour was then scaled tothe mean LCX flow and imposed at the model inflowface as a time-varying Womersley velocity profile usingSimvascular. Distributions of blood flow to the OM2and dLCX branches (46 and 54%, respectively) werecalculated by assuming WSS is the same for botharteries. Blood was assumed to behave as a Newtonianfluid (4 cP) with a constant density (1.06 gm/cm3).

The physiologic influence of the arterial tree distalto the flow domain was modeled at vessel outlets usinga three-element Windkessel representation withparameters Rc, C, and Rd.

63 Rc is the characteristicimpedance representing the resistance, compliance andinertance of the proximal artery of interest, C is thearterial capacitance and accounts for the sum of thecompliance of arteries beyond the model outlets, andRd describes the distal resistance in the absence ofventricular contraction.68 Collectively, Rc, Rd and thetime-varying resistance to blood flow resulting from

ventricular contraction can be calculated as the ratio ofmean BP and mean flow. In the absence of ventricularcontraction using the impulse response method of VanHuis et al., Rc and Rd constitute the zero hertzimpedance, Z0, that is ~38% (range 2265%) less thanthe total resistance value.62 Thus, the ratio of mean BPand mean flow mentioned above was scaled by 65%,for the current work to be within this range presentedby Van Huis et al. and consistent with our previousresults using these methods.69 Rc is reflective of theaverage impedance of frequencies above 7 Hz and canbe calculated as Cphq

pr2 where cph is the wave

speed, q is the blood density, and r is the vesselradius.62Applying a wave speed of 6.77 m/s for thecanine LCX15 the Rc values for the outlet brancheswere determined and Rd was calculated as the differ-ence between Z0, and Rc. The capacitance at eachoutlet was determined prior to running simulationsusing an automated Matlab program that determinespulse BP based on an inflow waveform, the calculated

FIGURE 8. Method of virtual stent implantation. Solid models of the outer surface of the stent (b) and vessel lumen (e) were loftedfrom the white and cyan segmentations of the OCT images (a). The stent radial thickness was subtracted from the model of loftedstent segmentation (b), which was subsequently subtracted from a thick stent model (c) to create a patient-specific stent model (d).Subtracting the patient-specific stent model (d) from the lumen model (e) as shown in (f) generated a computational representationof the blood flow domain (g).

TABLE 1. Patient data used to establish boundary condi-tions for CFD simulations.

Data Value

Age (years) 65

Height (inches) 64.3

Weight (lbs) 108.7

Heart rate (bpm) 82

Blood pressure (mmHg) 131/68

Cardiac output (L/min) 4.21

Reynolds number 76

Womersley number 2.16


Rc and Rd values and an a priori estimate for C.56 The

capacitance was then adjusted to match the desiredsystolic and diastolic BP values using this pulse pres-sure method.57

Anisotropic meshes with unstructured tetrahedralelements were created for each model using an auto-mated mesh generation program (MeshSim, Simmetrix,Clifton Park, NY) capable of local adaptation.42,51 Theinitial meshes were generated with maximum elementedge equal to the width of a stent strut. Successivemeshes were adaptively refined after each pulsatilesimulation using a minimum edge size equal to one-fourth of the strut width in order to place more ele-ments where they are most needed within the flowdomain while inserting fewer elements where a coarsedensity is sufficient. Simulations were performed withthe commercially-available flow solver, LesLib (AltairEngineering Inc., Troy, MI), which uses a stabilizedfinite element method to solve equations for conserva-tion of mass (continuity) and balance of fluid momen-tum (Navier-Stokes). Three cardiac cycles ensuredsimulation convergence with maximum difference inBP between equivalent time points in successive cardiaccycles

of the vessel at that location under 50 dyn/cm2. Incontrast, the example of a non-thrombotic region(16 mm) shows several locations of moderate TAWSS6080 dyn/cm2 resulting instead from vessel narrow-ing. Superimposed is a moving average representationof the post-stent TAWSS calculated with an averagingwindow size equal to the intrastrut dimension, whichappears to closely resemble the plot of follow-upTAWSS.

For this single patient, continuous NT extractedfrom OCT images acquired at follow-up appears tocorrelate with post-stent TAWSS from CFD simula-tions at corresponding circumferential locations(Fig. 12). For example, two cross-sections in non-thrombus areas show measurable growth at specificlocations with TAWSS 80 dyn/cm2 shows minimal NT, asdoes the high TAWSS region of the non-thrombusmid-high range cross-section.

DISCUSSION

The current investigation describes a new methodof 3D coronary artery reconstruction for use in cre-ating patient-specific CFD models that serves as analternative to earlier methods in several distinct ways.The most evident of these is the use of OCT toprovide highly resolved spatial information andimproved morphological detail in regions of throm-bus within stented coronary arteries as compared toIVUS for 2D image acquisition or CT angiographyfor 3D segmentation and reconstruction. In con-junction with coronary artery reconstruction we alsoprovide an extension of a previously documentedmethod for virtual stent implantation that preservedimportant elements of LCX geometry and pathologyin the post-stent model. Perhaps most impactful,however, is the process introduced for determiningthe OCT imaging wire pathway for image segmentregistration implemented with graph theory tech-niques that alleviate issues inherent in the commonpractice of biplane angiography discussed in moredetail below.

Comparison with Biplane Angiography

Orthogonal biplane angiographic images are com-monly used to determine the intravascular imagingpathway and/or the vessel lumen itself. While biplaneangiography is a common clinical practice prior topercutaneous coronary interventions, potential issuesinclude non-orthogonality of views, alternating timingof data acquisition between planes, cardiac/respiratorymovement, imaging device visibility, and general imagequality.55,58 In contrast, exploiting the principle ofminimum bending energy using the methods describedhere allows for pathway determination for retrospec-tive studies in which biplane angiography is eitherunavailable or presents the issues discussed above.When points extracted from biplane angiography arefit to a curve using a variety of functional forms,multiple possible rotational orientations may resultfrom problems with numerical instability or multiplefunctions closely representing the same curve.29,49 Inthe current investigation, registered image segmentswere rotated such that their centroids aligned with thetwo points where the centerline and wire intersect thesegments orthogonal plane; a similar procedure wasimplemented by Wahle et al.64 Finally, it can be diffi-cult to obtain a complete picture of the lumen cross-sectional shape from biplane angiography40,55 thushindering attempts to use lumen-matching algorithms.The methods presented in the current investiga-tion produce a single wire pathway and rotational

FIGURE 9. Comparison of TAWSS and OSI on the LCXproximal to the OM2 bifurcation under post-stent and follow-up conditions. From left to right: (a) 3D myocardial view ofTAWSS on the LCX, with the thrombus area outlined by theblack dashed box. (b) Unwrapped 2D view of all surfaces. (c)3D myocardial view of surfaces exposed to TAWSS < 4 dyn/cm2. (d) Unwrapped view of OSI.


orientation of segments on this pathway that can bedetermined regardless of cross-sectional shape, wirelocation, or presence of pathological landmarks.

Wire Conformation Reconstruction Parameters

The wire pathway reconstruction process wasdesigned using parameters provided by Schafer et al.53

to ensure a unique pathway. In addition to a sug-gested minimum tortuosity threshold of 1.2 exceededin the current investigation, they also found that 16planes sufficed for a phantom of 229 mm length and4.5 mm diameter. Therefore, the 33 planes used in thisinvestigation were adequate to define the LCX withlength 107 mm and diameter ~2.5 mm. Finally,Schafer et al. suggests that 13 candidate points perplane are sufficient to estimate guidewire position withminimal deviation from its actual location. Explicitdetermination of the wire locations at branch land-marks from registration of OCT images against CTdata and the inclusion of a refinement step ensuredthe accuracy of the wire pathway determined by thesemethods.

To further determine the influence of the numberof candidate points in each plane on the wire path-way, the method was also performed with 41 pointsper plane, the maximum chosen by Schafer et al.53

The root-mean-square (RMS) distance was calculated

FIGURE 10. TAWSS (dyn/cm2) along the length of the LCX queried at four circumferential regions for post-stent (black) andfollow-up (grey) conditions. The vertical dashed line indicates the proximal end of the stent (the distal end is at 11 mm). In mostcases, low TAWSS next to stent struts is alleviated after the follow-up period. However on the myocardial wall at 24 mm and theepicardial wall at 20 mm (gray highlighted boxes), two regions of localized vessel curvature, TAWSS remains at low values. HighTAWSS in intrastrut regions at post-stent decreased at follow-up (grey dashed boxes).

FIGURE 11. TAWSS (dyn/cm2) at two high WSS longitudinallocations for post-stent (black) and follow-up (grey) conditions.Superimposed (dashed line) is a moving average of the post-stent TAWSS with an averaging window size equal to the intra-strut dimension.M: myocardial;B: branch (OM2); E:endocardial.

ELLWEIN et al.222

between pathways determined with 13 vs. 41 pointsas

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

1

N

XN

i1d13i d41i 2

vuut

where d(i) are distances between corresponding points ibetween the two pathways and N is the total number ofpoints. The RMS distance within the modeled regionwas calculated to be 0.08, though for the entire path-way extending beyond the modeled region the RMSdistance was 0.39. The two pathways placed within thecontext of the solid model are shown in Fig. 13a.Deviations of the two pathways in the proximal and

distal vessel regions can be seen, but these regions wereused solely to ensure sufficient tortuosity for thepathway determination and a similar pattern of devi-ation at pathway endpoints was found by Schaferet al.53 The percentages of the vessel diameter(~2.5 mm) that the RMS distances occupied were 3%for the modeled region and 15% for the entire path-way. It is important to note that the percent in themodeled region is lower than the 6 and 4% for the 4.5and 3.6 mm diameter phantoms used by Schaferet al.53 A visual inspection of the two pathways withinthe modeled region corroborated the quantitativeanalysis. The close match in the modeled region butgreater deviation outside of it may indicate that the

FIGURE 12. TAWSS at post-stent (black) and intimal thickness at follow-up (grey) as determined from post-stent CFD simulationresults and follow-up OCT images (right). Stent locations are marked with an asterisk and lumen centroids are marked with a whitex. Two cross-sections in non-thrombus areas show measurable growth at areas of low WSS at stent struts (top and middle,circled). Conversely a cross-section that included the proximal end of the thrombus showed minimal growth at follow-up (bottom),as did the non-thrombus cross-section with high WSS (middle). M: myocardial; B: branch (OM2); E: endocardial; OB: opposite sidefrom branch.


current method of using the wire-to-centroid distancefrom OCT images for wire location and the refinementstep helped constrain the pathway, while extending thepathway to achieve sufficient tortuosity ensured itsuniqueness in the region of interest.

Vessels reconstructed with segments registered onpathways resulting from 13 vs. 41 candidate pointswere compared in a CFD analysis to further confirmthat the number of candidate points per plane does notadversely affect model building. Visible differencesbetween the two models were negligible despite severalsegments at identical registration points having a slightangular shift, the largest of which was 4. The twomodels were then each subjected to a CFD simulationwith mean flow in order to compare WSS results(Figs. 13b and 13c). Greater than 99% of the surfacearea showed less than 47% error between the twomodels, the value of error historically attributed togeometric influences in patient-specific model crea-tion.37, The percentage of the vessel surface exposed tolow WSS (

stent artifact has been shown to hinder stent assess-ability with CT, we expect it has minimal impact oncenterline determination and proximal and distalartery reconstruction.2,54 The methods for OCTimaging wire pathway determination need only anapproximate centerline, so in cases where CT is notavailable or radiation is an issue, they could still beused in conjunction with other imaging modalities torecreate the artery using OCT images with improvedresolution over IVUS. It should be noted as well thatnewer frequency-domain OCT systems have pullbackspeeds 1020 times greater than the time-domain sys-tem used for this study.22,59 The faster system wouldundoubtedly allow for more efficient data acquisition,but the methodology proposed here should be appli-cable regardless of the speed of the OCT system used.Currently OCT is not routine coronary intervention,but it is being incorporated into clinical proceduresmore frequently, potentially making the methods pre-sented here amenable to usage in larger patient popu-lations. Finally, although NT can be quantified afterfollow-up in the stented region, it is not entirely clearfrom OCT if growth observed at follow-up is immatureintimal tissue as opposed to thrombus, since theintensity is lower than what would be expected forneointimal growth. However, the indication that thegrowth may correlate with low WSS at stent strutsmotivates future studies to apply these methods with alarger patient population, at which time the thicknesswill be investigated in more detail.

In summary, the current investigation describes amethod for 3D patient-specific coronary artery recon-struction using the favorable spatial resolution of OCTwhile exploiting a current implementation of graphtheory techniques. The reconstructed stented modelpresented in this study included a flexed stent createdwith novel methods that closely follows vessel geome-try. TAWSS and OSI obtained from CFD simulationsperformed using these models are consistent with ear-lier studies, and the use of OCT allows us to alsoquantify NT directly from images for comparison withthese WSS results. These reconstruction methods maybe used in future studies with a larger patient popu-lation to further extract precise local informationcorrelating WSS with NT.

ACKNOWLEDGMENTS

This work is supported by a Translational Oppor-tunity Grant of the Pilot and Collaborative Clinicaland Translational Research Grants program from theClinical and Translational Science Institute of South-eastern Wisconsin (to JFL) and a Junior Faculty

Award from the American Diabetes Association (toJFL). Computational support for this work was madepossible by NSF grants CTS-0521602 and OCI-0923037 and assistance from Dr. Daniel Rowe, Mar-quette University. Dr. Bon-Kwon Koo is the recipientof a research grant from the CardioVascular ResearchFoundation (CVRF), Korea.

REFERENCES

1Alderliesten, T., P. A. N. Bosman, and W. J. Niessen.Towards a real-time minimally-invasive vascular interven-tion simulation system. IEEE Trans. Med. Imaging26(1):128132, 2007.2Andreini, D., G. Pontone, S. Mushtaq, M. Pepi, and A. L.Bartorelli. Multidetector computed tomography coronaryangiography for the assessment of coronary in-stentrestenosis. Am. J. Cardiol. 105(5):645655, 2010. doi:10.1016/j.amjcard.2009.10.046.3Antiga, L., and D. A. Steinman. Robust and objectivedecomposition and mapping of bifurcating vessels. IEEETrans. Med. Imaging 23(6):704713, 2004.4Arftsen, G. Mathematical Methods for Physicists. SanDiego, CA: Academic, 1985.5Barlis, P., and J. M. Schmitt. Current and future devel-opments in intracoronary optical coherence tomographyimaging. EuroIntervention 4(4):529533, 2009.6Braunwald, E., D. P. Zipes, and P. Libby. Heart Disease: ATextbook of Cardiovascular Medicine (6th ed.). W.B.Saunders, 2001.7De Santis, G., P. Mortier, M. De Beule, P. Segers,P. Verdonck, and B. Verhegghe. Patient-specific compu-tational fluid dynamics: structured mesh generation fromcoronary angiography. Med. Biol. Eng. Comput. 48(4):371380, 2010. doi:10.1007/s11517-010-0583-4.8Dijkstra, J., G. Koning, and J. Reiber. Quantitative mea-surements in IVUS images. Int. J. Cardiac Imaging 15:513522, 1999.9Evans, J. L., K. H. Ng, S. G. Wiet, M. J. Vonesh, W. B.Burns, M. G. Radvany, et al. Accurate three-dimensionalreconstruction of intravascular ultrasound data. Spatiallycorrect three-dimensional reconstructions. Circulation93(3):567576, 1996.

10Finn, A. V., G. Nakazawa, M. Joner, F. D. Kolodgie,E. K. Mont, H. K. Gold, et al. Vascular responses to drugeluting stents: importance of delayed healing. Arterioscler.Thromb. Vasc. Biol. 27(7):15001510, 2007.

11Gijsen, F. J., F. Migliavacca, S. Schievano, L. Socci,L. Petrini, A. Thury, et al. Simulation of stent deploymentin a realistic human coronary artery. Biomed. Eng. Online7:23, 2008. doi:10.1186/1475-925x-7-23.

12Gijsen, F. J., R. M. Oortman, J. J. Wentzel, J. C. Schu-urbiers, K. Tanabe, M. Degertekin, et al. Usefulness ofshear stress pattern in predicting neointima distribution insirolimus-eluting stents in coronary arteries. Am. J. Cardiol.92(11):13251328, 2003.

13Gilbert, J., J. Raboud, and B. Zinman. Meta-analysis of theeffect of diabetes on restenosis rates among patientsreceiving coronary angioplasty stenting. Diabetes Care27(4):990994, 2004.


http://dx.doi.org/10.1016/j.amjcard.2009.10.046http://dx.doi.org/10.1007/s11517-010-0583-4http://dx.doi.org/10.1186/1475-925x-7-23

14Goubergrits, L., E. Wellnhofer, U. Kertzscher, K. Affeld,C. Petz, and H. C. Hege. Coronary artery WSS profilingusing a geometry reconstruction based on biplane angiog-raphy. Ann. Biomed. Eng. 37(4):682691, 2009. doi:10.1007/s10439-009-9656-7.

15Gow, B. S., D. Schonfeld, and D. J. Patel. The dynamicelastic properties of the canine left circumflex coronaryartery. J. Biomech. 7(5):389395, 1974. doi:10.1016/0021-9290(74)90001-3.

16Gundert, T. J., S. C. Shadden, A. R. Williams, B. K. Koo,J. A. Feinstein, and J. F. Ladisa. A rapid and computa-tionally inexpensive method to virtually implant currentand next-generation stents into subject-specific computa-tional fluid dynamics models. Ann. Biomed. Eng. 2011. doi:10.1007/s10439-010-0238-5.

17Hamuro, M., J. C. Palmaz, E. A. Sprague, C. Fuss, andJ. Luo. Influence of stent edge angle on endothelialization inan in vitro model. J. Vasc. Interv. Radiol. 12(5):607611,2001.

18He, Y., N. Duraiswamy, A. O. Frank, and J. E. Moore.Blood flow in stented arteries: a parametric comparison ofstrut design patterns in three dimensions. J. Biomech. Eng.127(4):637647, 2005.

19He, X., and D. N. Ku. Pulsatile flow in the human leftcoronary artery bifurcation: average conditions. J. Bio-mech. Eng. 118:7482, 1996.

20Holmes, Jr., D. R., D. J. Kereiakes, S. Garg, P. W. Serruys,G. J. Dehmer, S. G. Ellis, et al. Stent thrombosis. J. Am.Coll. Cardiol. 56(17):13571365, 2010. doi:10.1016/j.jacc.2010.07.016.

21Iakovou, I., T. Schmidt, E. Bonizzoni, L. Ge, G. M.Sangiorgi, G. Stankovic, et al. Incidence, predictors, andoutcome of thrombosis after successful implantation ofdrug-eluting stents. JAMA 293(17):21262130, 2005.

22Imola, F., M. T. Mallus, V. Ramazzotti, A. Manzoli,A. Pappalardo, A. Di Giorgio, et al. Safety and feasibility offrequency domain optical coherence tomography to guidedecision making in percutaneous coronary intervention.EuroIntervention 6(5):575581, 2010. doi:EIJV6I5A97[pii]10.4244/EIJV6I5A97.

23Joner, M., G. Nakazawa, A. V. Finn, S. C. Quee,L. Coleman, E. Acampado, et al. Endothelial cell recoverybetween comparator polymer-based drug-eluting stents.J. Am. Coll. Cardiol. 52(5):333342, 2008. doi:10.1016/j.jacc.2008.04.030.

24Kern, M. Biplane coronary angiography: an old dog withnew tricks. Cath. Lab. Digest. 2009.

25Konings, M. K., E. B. van de Kraats, T. Alderliesten, andW. J. Niessen. Analytical guide wire motion algorithm forsimulation of endovascular interventions. Med. Biol. Eng.Comput. 41(6):689700, 2003.

26Kotani, J., M. Awata, S. Nanto, M. Uematsu, F. Oshima,H. Minamiguchi, et al. Incomplete neointimal coverage ofsirolimus-eluting stents: angioscopic findings. J. Am. Coll.Cardiol. 47(10):21082111, 2006.

27Krams, R., J. J. Wentzel, J. A. F. Oomen, R. Vinke, J. C.H. Schuurbiers, P. J. de Feyter, et al. Evaluation of endo-thelial shear stress and 3D geometry as factors determiningthe development of atherosclerosis and remodeling inhuman coronary arteries in vivo : combining 3D recon-struction from angiography and IVUS (ANGUS) withcomputational fluid dynamics. Arterioscler. Thromb. Vasc.Biol. 17(10):20612065, 1997.

28Kuchulakanti, P. K., W. W. Chu, R. Torguson,P. Ohlmann, S. W. Rha, L. C. Clavijo, et al. Correlates and

long-term outcomes of angiographically proven stentthrombosis with sirolimus- and paclitaxel-eluting stents.Circulation 113(8):11081113, 2006.

29Laban, M., J. A. Oomen, C. J. Slager, J. J. Wentzel,R.Krams, J. C.H. Schuurbiers, et al., editors.ANGUS: a newapproach to three-dimensional reconstruction of coronaryvessels by combined use of angiography and intravascularultrasound. Computers in Cardiology, 1013 Sep. 1995.

30LaDisa, Jr., J. F., M. Bowers, L. Harmann, R. Prost, A. V.Doppalapudi, T. Mohyuddin, et al. Time-efficient patient-specific quantification of regional carotid artery fluiddynamics and spatial correlation with plaque burden. Med.Phys. 37(2):784792, 2010.

31LaDisa, Jr., J. F., I. Guler, L. E. Olson, D. A. Hettrick,J. R. Kersten, D. C. Warltier, et al. Three-dimensionalcomputational fluid dynamics modeling of alterations incoronary wall shear stress produced by stent implantation.Ann. Biomed. Eng. 31(8):972980, 2003.

32LaDisa, Jr., J. F., D. A. Hettrick, L. E. Olson, I. Guler,E. R. Gross, T. T. Kress, et al. Stent implantation alterscoronary artery hemodynamics and wall shear stress duringmaximal vasodilation. J. Appl. Physiol. 93(6):19391946,2002. doi:10.1152/japplphysiol.00544.2002.

33LaDisa, Jr., J. F., L. E. Olson, I. Guler, D. A. Hettrick,S. H. Audi, J. R. Kersten, et al. Stent design properties anddeployment ratio influence indexes of wall shear stress: athree-dimensional computational fluid dynamics investiga-tion within a normal artery. J. Appl. Physiol. 97:424430,2004.

34LaDisa, Jr., J. F., L. E. Olson, I. Guler, D. A. Hettrick, J. R.Kersten, D. C. Warltier, et al. Circumferential vasculardeformation after stent implantation alters wall shear stressevaluated using time-dependent 3D computational fluiddynamics models. J. Appl. Physiol. 98(3):947957, 2005.

35LaDisa, Jr., J. F., L. E. Olson, R. C. Molthen, D. A.Hettrick, P. F. Pratt, M. D. Hardel, et al. Alterations inwall shear stress predict sites of neointimal hyperplasiaafter stent implantation in rabbit iliac arteries. Am. J.Physiol. Heart Circ. Physiol. 288(5):H2465H2475, 2005.

36Leaman, D., R. Brower, G. Meester, P. Serruys, andM. van den Brand. Coronary artery atherosclerosis: severityof the disease, severity of angina pectoris and compromisedleft ventricular function. Circulation 63(2):285299, 1981.

37Lee, S. W., and D. A. Steinman. On the relative importanceof rheology for image-based CFD models of the carotidbifurcation. J. Biomech. Eng. 129(2):273278, 2007. doi:10.1115/1.2540836.

38Lloyd-Jones, D., R. J. Adams, T. M. Brown, M. Carne-thon, S. Dai, G. De Simone, et al. Heart disease and strokestatistics-2010 update: a report from the American HeartAssociation.Circulation 121(7):e46e215, 2010. doi:10.1161/circulationaha.109.192667.

39Malek, A. M., S. L. Alper, and S. Izumo. Hemodynamicshear stress and its role in atherosclerosis. JAMA282(21):20352042, 1999.

40Marquering, H., J. Dijkstra, Q. Besnehard, J. Duthe,J. Schuijf, J. Bax et al., editors. Coronary CT angiography:IVUS image fusion for quantitative plaque and stenosisanalyses. Society of Photo-Optical Instrumentation Engi-neers (SPIE) Conference Series, 2008.

41Moses, J. W., M. B. Leon, J. J. Popma, P. J. Fitzgerald, D.R. Holmes, C. OShaughnessy, et al. Sirolimus-elutingstents versus standard stents in patients with stenosis in anative coronary artery. N. Engl. J. Med. 349(14):13151323, 2003.

ELLWEIN et al.226

http://dx.doi.org/10.1007/s10439-009-9656-7http://dx.doi.org/10.1007/s10439-009-9656-7http://dx.doi.org/10.1016/0021-9290(74)90001-3http://dx.doi.org/10.1016/0021-9290(74)90001-3http://dx.doi.org/10.1007/s10439-010-0238-5http://dx.doi.org/10.1016/j.jacc.2010.07.016http://dx.doi.org/10.1016/j.jacc.2010.07.016http://dx.doi.org/EIJV6I5A97[pii]10.4244/EIJV6I5A97http://dx.doi.org/EIJV6I5A97[pii]10.4244/EIJV6I5A97http://dx.doi.org/10.1016/j.jacc.2008.04.030http://dx.doi.org/10.1016/j.jacc.2008.04.030http://dx.doi.org/10.1152/japplphysiol.00544.2002http://dx.doi.org/10.1115/1.2540836http://dx.doi.org/10.1161/circulationaha.109.192667http://dx.doi.org/10.1161/circulationaha.109.192667

42Muller, J., O. Sahni, X. Li, K. E. Jansen, M. S. Shephard,and C. A. Taylor. Anisotropic adaptive finite elementmethod for modelling blood flow. Comput. Meth. Biomech.Biomed. Eng. 8(5):295305, 2005. doi:10.1080/10255840500264742.

43Murata, A., D. Wallace-Bradley, A. Tellez, C. Alviar, M.Aboodi, A. Sheehy, et al. Accuracy of optical coherencetomography in the evaluation of neointimal coverage afterstent implantation. JACC Cardiovasc. Imaging 3(1):7684,2010. doi:S1936-878X(09)00757-8[pii]10.1016/j.jcmg.2009.09.018.

44Myers, J. G., J. A. Moore, M. Ojha, K. W. Johnston, andC. R. Ethier. Factors influencing blood flow patterns in thehuman right coronary artery. Ann. Biomed. Eng. 29:109120, 2001.

45Nissen, S. E., and P. Yock. Intravascular ultrasound: novelpathophysiological insights and current clinical applica-tions. Circulation 103(4):604616, 2001.

46Otake, H., J. Shite, J. Ako, T. Shinke, Y. Tanino,D. Ogasawara, et al. Local determinants of thrombus for-mation following sirolimus-eluting stent implantationassessed by optical coherence tomography. J. Am. Coll.Cardiol. Intv. 2(5):459466, 2009. doi:10.1016/j.jcin.2009.03.003.

47Papafaklis, M. I., C. V. Bourantas, P. E. Theodorakis, C. S.Katsouras, K. K. Naka, D. I. Fotiadis, et al. The effect ofshear stress on neointimal response following sirolimus-and paclitaxel-eluting stent implantation compared withbare-metal stents in humans. J. Am. Coll. Cardiol. Intv.3(11):11811189, 2010. doi:10.1016/j.jcin.2010.08.018.

48Prause, G. P., S. C. DeJong, C. R. McKay, and M. Sonka.Towards a geometrically correct 3-D reconstruction oftortuous coronary arteries based on biplane angiographyand intravascular ultrasound. Int. J. Cardiac Imaging13(6):451462, 1997.

49Prause, G. P. M., S. C. DeJong, C. R. McKay, andM. Sonka, editors. Geometrically correct 3-D reconstruc-tion of coronary wall and plaque: combining biplaneangiography and intravascular ultrasound. Computers inCardiology, 811 Sep. 1996.

50Redwood, S., N. Curzen, and M. Thomas. Oxford Text-book of Interventional Cardiology. Oxford: Oxford Uni-versity Press, 2010.

51Sahni, O., J. Muller, K. E. Jansen, M. S. Shephard, andC. A. Taylor. Efficient anisotropic adaptive discretizationof the cardiovascular system. Comput. Meth. Appl. Mech.Eng. 195(4143):56345655, 2006. doi:10.1016/j.cma.2005.10.018.

52Schafer, S., K. R. Hoffmann, P. B. Noel, C. N. Ionita, andJ. Dmochowski. Evaluation of guidewire path reproduc-ibility. Med. Phys. 35(5):18841892, 2008.

53Schafer, S., V. Singh, P. B. Noel, A. M. Walczak, J. Xu,and K. R. Hoffmann. Real-time endovascular guidewireposition simulation using shortest path algorithms. Int. J.Comput. Assist. Radiol. Surg. 4(6):597608, 2009. doi:10.1007/s11548-009-0385-z.

54Sheth, T., J. D. Dodd, U. Hoffmann, S. Abbara, A. Finn,H. K. Gold, et al. Coronary stent assessability by 64 slicemulti-detector computed tomography. Catheter. Cardio-vasc. Interv. 69(7):933938, 2007. doi:10.1002/ccd.21130.

55Slager, C. J., J. J. Wentzel, J. C. H. Schuurbiers, J. A. F.Oomen, J. Kloet, R. Krams, et al. True 3-dimensionalreconstruction of coronary arteries in patients by fusion ofangiography and IVUS (ANGUS) and its quantitativevalidation. Circulation 102(5):511516, 2000.

56Stergiopulos, N., J. J. Meister, and N. Westerhof. Simpleand accurate way for estimating total and segmentalarterial compliance: the pulse pressure method. Ann. Bio-med. Eng. 22(4):392397, 1994.

57Stergiopulos, N., P. Segers, and N. Westerhof. Use of pulsepressure method for estimating total arterial compliance invivo. Am. J. Physiol. Heart Circ. Physiol. 276(2 Pt 2):H424H428, 1999.

58Subramanian, K. R., M. J. Thubrikar, B. Fowler, M. T.Mostafavi, and M. W. Funk. Accurate 3D reconstructionof complex blood vessel geometries from intravascularultrasound images: in vitro study. J. Med. Eng. Technol.24(4):131140, 2000.

59Takarada, S., T. Imanishi, Y. Liu, H. Ikejima, H. Tsujioka,A. Kuroi, et al. Advantage of next-generation frequency-domain optical coherence tomography compared withconventional time-domain system in the assessment ofcoronary lesion. Catheter. Cardiovasc. Interv. 75(2):202206, 2010. doi:10.1002/ccd.22273.

60Tang, B. T., C. P. Cheng, M. T. Draney, N. M. Wilson, P. S.Tsao, R. J. Herfkens, et al. Abdominal aortic hemodynamicsin young healthy adults at rest and during lower limb exercise:quantification using image-based computer modeling. Am. J.Physiol. Heart Circ. Physiol. 291(2):H668H676, 2006.

61Taylor, C. A., and D. A. Steinman. Image-based modelingof blood flow and vessel wall dynamics: applications,methods and future directions: Sixth International Bio-Fluid Mechanics Symposium and Workshop, March 2830, 2008 Pasadena, California. Ann. Biomed. Eng.38(3):11881203, 2010. doi:10.1007/s10439-010-9901-0..

62Van Huis, G. A., P. Sipkema, and N. Westerhof. Coronaryinput impedance during cardiac cycle as determined byimpulse response method. Am. J. Physiol. 253(2 Pt 2):H317H324, 1987.

63Vignon-Clementel, I. E., C. A. Figueroa, K. E. Jansen, andC. A. Taylor. Outflow boundary conditions for three-dimensional finite element modeling of blood flow andpressure in arteries. Comput. Methods Appl. Mech. Eng.195:37763796, 2006.

64Wahle, A., G. P. M. Prause, C. Von Birgelen, R. Erbel, andM. Sonka. Fusion of angiography and intravascular ultra-sound in vivo: establishing the absolute 3-D frame orienta-tion. IEEE Trans. Biomed. Eng. 46(10):11761180, 1999.

65Wentzel, J. J., F. J. Gijsen, J. C. Schuurbiers, A. F. van derSteen, and P. W. Serruys. The influence of shear stress onin-stent restenosis and thrombosis. EuroIntervention4(Suppl C):C27C32, 2008.

66Wentzel, J. J., F. J. H. Gijsen, N. Stergiopulos, P. W.Serruys, C. J. Slager, and R. Krams. Shear stress, vascularremodeling and neointimal formation. J. Biomech.36(5):681688, 2003. doi:10.1016/s0021-9290(02)00446-3.

67Wentzel, J. J., R. Krams, J. C. H. Schuurbiers, J. A.Oomen, J. Kloet, W. J. van der Giessen, et al. Relationshipbetween neointimal thickness and shear stress after walls-tent implantation in human coronary arteries. Circulation103:17401745, 2001.

68Westerhof, N., N. Stergiopulos, and M. I. M. Noble.Snapshots of Hemodynamics: An Aid for Clinical Researchand Graduate Education. New York: Springer, 2005.

69Williams, A. R., B.-K. Koo, T. J. Gundert, P. J. Fitzgerald,and J. LaDisa, Jr. Local hemodynamic changes caused bymain branch stent implantation and virtual side branchballoon angioplasty in a representative coronary bifurca-tion. J. Appl. Physiol. 109:532540, 2010. doi:10.1152/japplphysiol.00086.2010.


http://dx.doi.org/10.1080/10255840500264742http://dx.doi.org/10.1080/10255840500264742http://dx.doi.org/S1936-878X(09)00757-8[pii]10.1016/j.jcmg.2009.09.018http://dx.doi.org/S1936-878X(09)00757-8[pii]10.1016/j.jcmg.2009.09.018http://dx.doi.org/10.1016/j.jcin.2009.03.003http://dx.doi.org/10.1016/j.jcin.2009.03.003http://dx.doi.org/10.1016/j.jcin.2010.08.018http://dx.doi.org/10.1016/j.cma.2005.10.018http://dx.doi.org/10.1016/j.cma.2005.10.018http://dx.doi.org/10.1007/s11548-009-0385-zhttp://dx.doi.org/10.1002/ccd.21130http://dx.doi.org/10.1002/ccd.22273http://dx.doi.org/10.1007/s10439-010-9901-0.http://dx.doi.org/10.1016/s0021-9290(02)00446-3http://dx.doi.org/10.1152/japplphysiol.00086.2010http://dx.doi.org/10.1152/japplphysiol.00086.2010

Optical Coherence Tomography for Patient-specific 3D Artery Reconstruction and Evaluation of Wall Shear Stress in a Left Circumflex Coronary ArteryAbstractIntroductionMethodsData CollectionModel ConstructionStage A. Image ProcessingStage B. Wire Pathway ReconstructionDemonstration of ConceptImplementation

Stage C. Segment RegistrationStage D. Model AssemblyStage E. Stent Implantation

Simulation: Boundary Conditions and ParametersResults Quantification

ResultsVessel ReconstructionIndices of Wall Shear Stress

DiscussionComparison with Biplane AngiographyWire Conformation Reconstruction ParametersAssessment of WSS Indices and Correlation with NTLimitations

AcknowledgmentsReferences

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optical coherence tomography for patient-specific 3d artery

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