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  • 8/9/2019 Carotid Artery Stenting Virtual Stenting 215

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    2014 Wichtig Publishing - ISSN 0391-3988

    Int J Artif Organs ( 2014; :12) 928-93937

    928

    Feasibility ofa priorinumerical assessment ofplaque scaffolding after carotid artery stentingin clinical routine: proof of concept

    Francesco Iannaccone1, Sander De Bock1, Matthieu De Beule1,2, Frank Vermassen3, Isabelle Van Herzeele3,Pascal Verdonck1, Patrick Segers1, Benedict Verhegghe1,2

    1 IBiTech-bioMMeda, Ghent University, Ghent - Belgium2 FEops bvba, Ghent - Belgium3 Department of Thoracic and Vascular Surgery, Ghent University Hospital, Ghent - Belgium

    ORIGINAL ARTICLE -Focus on: Modeling approaches for endovascular devices

    DOI: 10.5301/ijao.5000379

    INTRODUCTION

    Carotid artery stenting (CAS) is an alternative procedure

    for the treatment of severely stenosed symptomatic or

    asymptomatic carotid artery lesions (70%) in high-risk

    patients. The procedure has a similar stroke, death, and

    myocardial infarction rate as carotid endarterectomy (CEA)

    (1, 2), although lower event rates have been reported for

    CAS in younger patients (

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    thereby shifting the major events to post-procedural com-

    plications (4) in well-selected patients.It has been suggested that stroke after CAS may be caused

    by embolization when the stent mesh is unable to ade-

    quately confine the plaque (5). In 75% of all procedures,

    stents are likely to obtain similar results. However, accurate

    screening and carotid stent choice is influenced mainly by

    arterial anatomy and lesion morphology (7-9), suggesting

    that the plaque scaffolding provided by the stent is one of

    the key parameters to procedural success.

    One of the requirements for carotid stent design is good

    apposition to the vessel wall (10, 11) as the protruding

    stent struts may induce (micro) thrombus formation by the

    decreasing blood flow velocity (12, 13). The effect of in-

    complete stent apposition (ISA) has been studied in coro-

    nary arteries and appears to be a potential factor of late

    stent thrombosis (14). Limited data is available about the

    consequences of ISA on the clinical outcome of CAS (12),

    leaving the associated risk of restenosis yet to be clarified.

    This post-procedural risk is, at present, not trivial to assess

    by imaging.

    Mono and biplane angiography, currently the gold stan-

    dard to assess CAS (15), is not an optimal technique for the

    detection of stent apposition. Optical coherence tomog-

    raphy (OCT) (16) or Intra-vascular ultrasound (IVUS) (17)have been proven to be safe and effective in quantifying

    stent malapposition and plaque prolapse (associated with

    major adverse events) but they are not yet routinely used

    in CAS.

    The influence of stent design on the peri- and early post-

    procedural neurological outcome is still an unsolved top-

    ic, mostly due to the lack of data relating stent design to

    the clinical outcome (5, 18-21). A retrospective analysis of

    patients treated with various carotid stents in symptomatic

    and asymptomatic carotid artery disease demonstrated

    increased post-procedural event rates for the four open

    cell stents and for one of the three closed cell stents (5),

    confirming the findings of other studies (18, 22). When the

    stents were sub-grouped according to the free cell area

    (FCA), higher post-procedural event rates were correlated

    with an increase of FCA especially in symptomatic patients,

    who are known to have an increased risk for stroke peri-

    operatively (23). However, criticism has been raised due to

    possible bias and confounders (24-26), specifically because

    no objective measurement of silent brain infarction (such as

    diffusion-weighted magnetic resonance imaging, DW-MRI)

    was performed. At the same time, however, other studies

    have failed to demonstrate statistically significant differenc-

    es in stroke and death risk between patients treated withopen/closed stent designs or grouped by FCA (19, 25, 26).

    Still, questions remain, especially since the occurrence of

    any stroke is uncertain (27, 28) and may have an impact

    on the interpretation of the results (25). Moreover, open

    cell stent design has also been associated with fewer new

    DW-MRI lesions (26), although in this particular study, unlike

    the others (21), no embolic protection was used during the

    procedure.

    More recently a meta-analysis by Tadros et al (29) noted that

    symptomatic patients with favorable anatomy treated with a

    closed cell design have fewer major adverse events, reopen-

    ing the debate. We feel it is safe to state that geometrical

    features of the device may play a role in CAS outcomes and

    newer devices are effectively focusing on improved scaf-

    folding capabilities (28). Scaffolding parameters have been

    previously proposed on the free expanded configuration

    (11), although the same device can have a different behavior

    in situdepending on the anatomical site (7-9).

    Technical tools able to predict stent apposition and its

    mechanical behavior in the treated vessel are appealing,

    and may be beneficial for procedural planning. Numerical

    simulations can be helpful to optimize carotid stent design,

    and have proven to be a valid predictive tool when testedin vitro(30), and useful in studying the behavior of different

    stent designs implanted in a single carotid model (31-34).

    Nevertheless, to the authors knowledge, no in vivoCAS

    validations have been previously reported.

    The present work introduces a novel virtual, patient-specific,

    pre-operative environment to evaluate the feasibility of nu-

    merical prediction for clinical outcomes after CAS, focusing

    on plaque scaffolding. Mechanical simulations have been

    coupled with novel analysis tools to quantify scaffolding

    parametersin situ. Two real patient cases treated with the

    Acculink stent (Abbott Vascular, Santa Clara, CA, USA) were

    studied to proof the concept. Routine pre- and post-stenting

    imaging were compared with the computer simulations to

    validate the virtual operative procedure.

    MATERIALS AND METHODS

    Patient data

    Two patient datasets (obtained after patients provided in-

    formed consent for the processing of their datasets) were

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    available for the study, referred to as patient A (male,

    75 years old, 82% degree of stenosis in the internal carotidartery, ICA) and patient B (female, 61 years, 79% degree

    of stenosis in the ICA). Both asymptomatic patients were

    treated via the transfemoral route using an embolic pro-

    tection device (Emboshield NAV 6; Abbott Vascular, Santa

    Clara, CA, USA). An Acculink stent was implanted by an

    experienced team: tapered 7 10 40 mm for patient A

    and tapered 7 10 30 mm for patient B. Pre-operative

    computer tomography angiography (CTA) images were ac-

    quired with a Siemens Somatom Sensation Cardiac scan-

    ner with resolution of 0.37 0.37 1 mm for patient A and

    Siemens Somatom Definition Flash scanner with resolution

    of 0.54 0.54 3 mm for patient B (Siemens Healthcare,

    Erlangen, Germany). Post-operative monoplane angiogra-

    phy was acquired using a Philips AlluraXPer FD10 (Philips

    Healthcare Imaging Systems, Andover, MA, USA).

    Vessel models

    From the pre-operative CTA, geometries of the vessel lu-

    men and of the calcified plaques were segmented (Fig. 1A)

    using 3D Slicer software (35). Other tissues (such as soft

    plaques and vessel wall) were not visible on CTA. To create

    an approximate model of the vessel wall the following stepswere taken:

    1. the geometry of the calcified plaque and the vessel lu-

    men were combined and manually corrected to create

    an approximated shape of the healthy lumen before le-

    sion development (Fig. 1B);

    2. the actual lumen and calcified plaque were subtracted

    from the geometry of the healthy lumen in order to ob-

    tain the non calcified plaque geometries (Fig. 1B);

    3. subsequently, the 3D triangulated models were gener-

    ated for the diseased lumen, the plaques components,

    and the healthy lumen;

    4. the healthy lumen geometry was expanded to create

    the outer vessel wall geometry; the radius of the ves-

    sel, computed per node as distance of the healthy lu-

    men to the centerline of the vessel (computed in vmtk

    (36)), was increased by 30% in the normal direction to

    the surface, a realistic value of the carotid artery wall

    thickness (37).

    Starting from the geometry of the real lumen and the re-

    constructed outer wall, the 3D hexahedral mesh of the

    vessel (Fig. 2) for the numerical solver was created using

    IA_FEMesh (Finite Element Meshing Module), a meshing

    tool embedded in 3D Slicer (38). The vessel was consid-

    ered to be single-layered. The finite element analysis was

    performed using brick elements with reduced integra-

    tion (C3D8R in the Abaqus nomenclature) for both stent

    devices and vessel geometries. The final meshes con-

    sisted of 79 092 nodes and 35 256 elements for the stent

    and of 12 848 nodes and 9477 elements for the vessel for

    patient A, while the models of patient B counted 57 534

    nodes and 25 680 elements for the stent and 11 000

    nodes and 8038 elements for the vessel. The density of

    the meshes was increased at critical locations (crowns

    and regions with higher curvature of the stent struts and

    the stent landing zone of the vessels). The sections of the

    Fig. 1 -Work-flow of the vessel model reconstruction: segmentation

    of the vessel lumen and calcified plaque (A); manual adjustment ofthe lumen shape to create the assumed healthy lumen and retrieve

    the subtracted non-calcified lesion depicted in blue(B); expansion

    of the healthy lumen to create the outer vessel wall (C).

    Fig. 2 -Reconstructed vessel geometries of the 2 patients. Calcifi-

    cations are depicted in whiteand the assumed hypo cellular plaque

    in yellow.

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    stents counted 2 2 elements and we used 3 elements

    along the thickness of the vessel wall. Displacement val-ues were used to check convergence of the results. Since

    doubling the number of elements along strut thickness

    and width and the vessel wall resulted in similar displace-

    ment values, the coarser meshes were used to reduce

    the computational cost. Note that an accurate stress

    analysis which was not the goal of this work would re-

    quire denser meshes to ensure reliability of the real stress

    state, especially along the width of the device. Similar

    mesh densities for the carotid arteries were previously

    reported to have sufficient detail to describe stresses in

    the wall (33).

    The vessel geometry was used as reconstructed from the

    scans, without including pre-stress and pre-stretch condi-

    tions. No physiological pressure was applied to the model.

    To compensate for these limitations, we created an en-

    gineering equivalent of the vessel material to describe the

    in vivodisplacements which includes the effects of thein vivo

    boundary conditions of the vessel (pre-pressurization, pre-

    stretch, interactions with the surrounding tissues, etc). This

    approach neglects the real stress state of the material but

    simplifies the simulation by allowing the implantation in an

    unloaded vessel configuration.

    We used a stress-strain curve for the arterial wall publishedin a previous study (39). Then we scaled the curve to cali-

    brate the actual deformations induced by the stent, and

    coefficients of several hyperelastic constitutive models

    were fit to the material response curve using the Evaluate

    tool of the Abaqus software (Simulia Corp, Providence, RI,

    USA) material menu.

    Mainly due to stability concerns, we opted for a third

    order Ogden hyperelastic material model formulation,

    whose general strain energy potential Uas a function of

    the deviatoric principal stretches 1, 2 and 3 is given by:

    ( )= 2 + + 3=1

    2 1 2 3

    Ui

    N

    i

    i

    i ii

    where N,iand

    iare material parameters.

    The original stress-strain curve was scaled along the

    stretch axis to obtain a stiffer material at the beginning of

    the curve (to faster reach higher stresses in the model) in

    order to consider the effects of the pressurizedin vivocon-

    dition. The scaling also provides stiffer behavior at higher

    stretches to account for the interaction with the surround-

    ing structures. The scaled curve does not describe the

    real behavior of the vessel but only an equivalent material

    that can mimic the combined effects of the in vivopres-

    surization, pre-stretch, and the stiffening provided by thesurrounding tissue. A scaling factor of 0.40 was found to

    describe the correct displacements.

    In order to account for the different plaque components,

    the elements of the vessel mesh located inside the seg-

    mented plaque surfaces were selected. The material prop-

    erties of the plaques were taken from Loree et al (40) and

    fit using a first-order, hyperelastic Ogden model using the

    previously mentioned Abaqus tool. To roughly emulate the

    rupture of the plaque, we assumed a constant plastic be-

    havior at the reported values of plaque rupture. All material

    parameters are summarized in Table I.

    Stent models

    To retrieve the basic cell geometry, the stent models were

    built starting from a micro-CT scan of a 8 20 mm Acculink

    straight design. After segmentation of the stent geometry

    (in 3D Slicer), a parametric hexahedral stent model was built

    in pyFormex (41) using the approach described in De Bock

    et al (42). The parametric model was adapted to create the

    tapered stents (7 10 30 mm and 7 10 40 mm) shown

    in Figure 3. Stent thickness and width were assumed equal

    to the ones of the available sample, measured in 3D Slicer(0.17 mm and 0.16 mm, respectively).

    The stress-strain relationship of the nitinol alloy (Ti55.8

    wt% Ni) was retrieved from the literature (43) and defined

    using an embedded user subroutine in Abaqus based on

    the model of Auricchio et al (44). Symmetry in compres-

    sion was assumed (Tab. I). All phases of the device ma-

    nipulation were assumed to occur at body temperature

    of 37C.

    Virtual stent deployment procedure

    The numerical simulation involved non-linearity due to

    the material properties, large deformations, and complex

    contact problems of a real stent implantation; a quasi-static

    analysis was performed using the commercial finite element

    solver Abaqus/Explicit. The general contact algorithm was

    used to handle the interactions, assuming a friction value of

    0.05 for all the contact surfaces (34).

    The stent placement was emulated imposing analytical

    displacements to a cylindrical catheter (4-node surface el-

    ements with reduced integration - SFM3D4R) by a VDISP

    subroutine to drive crimping, smooth bending along the

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    centerline of the vessel and deployment of the stent at the

    arterial lesion, as previously described (42). A similar cylin-

    drical geometry was used to enlarge the vessels, to emu-

    late balloon pre-dilation of the vessel to ensure the device

    insertion in the vascular cavity.

    Validation

    To validate the numerical results we compared the post-

    procedural monoplane angiographic images and the virtual

    implantation both qualitatively (visual assessment) and

    quantitatively measuring the relative errors %

    between the

    diameters of the stented location Dsimulationwith respect tothe clinical data D

    in vivomeasured as :

    =

    D D

    D100%

    simulation in vivo

    in vivo

    The monoplane angiograms were previously calibrated

    from the catheter size used during CAS. The numerical

    results were imported in pyFormex to extract the ves-

    sel lumen. The angle of view of the deformed model was

    manually adjusted in the viewer to match the position of

    thein vivodataset. In vivoand simulated implantation im-

    ages were then calibrated to match the diameter of the

    non-stented vessel portions.

    Post-processing for evaluation of scaffolding

    Three different parameters were measured to evaluate the

    scaffolding of the stent, i.e. the support given by the stent

    to the arterial lesion, as schematically depicted in Figure 4:

    1. Incomplete strut apposition (ISA) to the vessel wall,

    computed as the distance from the external stent sur-

    face nodes to the closest vessel surface element. As

    a descriptive parameter of ISA, the percentage area of

    TABLE I -MATERIAL PARAMETERS FOR THE CALIBRATED VESSEL WALL, THE FIT PLAQUE COMPONENTS, AND THENITINOL STENT

    Arter ial material parameters Source

    1

    (kPa)

    1

    2

    (kPa)

    2

    3

    (kPa)

    3

    Calibrated

    healthy wall

    III order Ogden

    (Hyperelastic)

    -14790 -2.14 10122 0.51 507 -7.79

    (kPa) Plasticity

    threshold

    (kPa)

    Hypocellular

    plaque

    I order Ogden

    (Hyperelastic)

    21.8 24.72 400 Loree et al,

    1994 (40)

    Calcifiedplaque

    I order Ogden(Hyperelastic)

    72.34 25.00 400 Loree et al,1994 (40)

    Device material parameters

    Ea(kPa) v

    aE

    m

    (kPa)

    vm

    L

    LS

    (kPa)

    LE

    (kPa)

    TO

    US

    (kPa)

    UE

    (kPa)

    CLS

    (kPa)

    LV

    Nitinol 41000 0.3 23333 0.3 0.0437 450 520 37 210 130 450 0.0437 Gong et al,

    2004 (43)

    Fig. 3 -Implanted tapered Acculink stent model (7 10 40 mm and

    7 10 30 mm).

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    stent struts within a threshold distance of the vessel

    wall relative to the total stent area was computed. The

    threshold of 0.2 mm was chosen to identify most critical

    areas undergoing ISA (16).

    2. Stent cells areas of the implanted stent. The cell area

    was computed from the minimal surfaces that could fill

    the FCAs. The surface was created on the undeformed

    stent, triangulating the cell nodes and keeping trace of

    the connectivity of each triangle. Using the connectivitytable, the surface was then deformed according to the

    new position of the cell nodes after the implant.

    3. Largest fitting sphere (LFS) going through the FCA. The

    surface fitting the FCA was seeded with an adequate

    number of points (determined after convergence es-

    timation). The minimum distance of each point to the

    free edges of the cell was computed. The point (center

    of the sphere) with the largest distance (radius of the

    sphere) was then selected.

    RESULTS AND DISCUSSION

    Validation

    The numerical simulations were able to emulate the over-

    all shape of the implanted stents retrieved from the clini-

    cal monoplane angiographic images (Figs. 5 and 6). Table II

    summarizes the relative error of the lumen diameters for the

    indicated sections. Mean relative errors of the lumen diam-

    eters were 5.31 8.05% for patient A and 4.12 9.84% for

    patient B.

    Both the qualitative and quantitative comparison between

    post-procedural angiography and the projected image of

    the virtually implanted stent showed overall good agree-

    ment, capturing the main features of the deformed stent

    shape. Patient A showed a somewhat better visual match

    Fig. 4 - Schematic description of the evaluated scaffolding pa-

    rameters.

    Fig. 5 -Qualitative comparison of the implanted stent configura-

    tion for patient A, showing the clinical and numerical results. From

    left to right:angiography of stented lumen, numerical results, and

    X-ray of the implanted stent.Red arrowsindicate the location of the

    measured diameters.

    Fig. 6 -Qualitative comparison of the implanted stent configuration

    for patient B, showing the clinical and numerical results. From left

    to right: angiography of stented lumen, numerical results, and X-

    ray of the implanted stent. Red arrows indicate the location of the

    measured diameters.

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    of the stent shape compared to patient B, even though

    average diameter errors were similar in both cases.

    Scaffolding

    The results of the scaffolding evaluation are summarized in

    Table III and Figures 7, 8, and 9.

    As it can be observed in Figure 7, the open-cell design

    showed ISA to the vessel wall in the most tortuous and ana-

    tomically complex regions. The percentage of stent struts

    area with ISA was markedly higher for patient A (8.8% vs.

    2.4%). Note, however, that results can be misleading due to

    the fact that patient B had a completely occluded external

    carotid artery and patient A had a larger part of the stent

    deployed at the bifurcation region where there was a higher

    mismatch between stent and vessel diameter, consequently

    leading to a higher area of ISA. Both cases showed good

    apposition to the ICA. For patient B the sections at the bifur-

    cation and at the ICA were similar, giving better apposition

    to the vessel wall, also due to a smaller curvature of the ves-

    sel. At the inner curvature of the vessel wall, ISA was noted

    TABLE II -ABSOLUTE VALUES OF THE DIAMETERS OF THE STENTED VESSEL AND THE RELATIVE ERROR BETWEEN THENUMERICAL SIMULATION AND THE CLINICAL DATA, COMPUTED AT 9 DIFFERENT SECTIONS

    Diameters Section Average

    S1 S2 S3 S4 S5 S6 S7 S8 S9

    Patient A Numerical (mm) 7.38 8.59 4.82 4.20 4.59 3.71 4.14 4.42 4.30

    Clinical (mm) 7.44 8.12 3.99 3.78 4.17 3.94 3.94 4.30 4.37

    % -0.85 5.82 21.06 11.01 10.15 -5.73 5.20 2.79 -1.61 5.31 8.05

    Patient B Numerical (mm) 6.45 5.71 5.80 5.97 6.37 5.62 5.63 6.29 5.76

    Clinical (mm) 6.45 6.18 6.54 5.89 5.30 4.96 5.21 5.96 5.37

    % 0.00 -7.62 -11.25 1.26 20.28 13.33 8.10 5.55 7.40 4.12 9.27

    TABLE III - AVERAGE RESULTS OF INCOMPLETE STRUTAPPOSITION, FREE CELL AREAS, AND THERADIUS OF THE LARGEST FITTING SPHERE

    Global scaffolding parameters

    Model Stent area

    with ISA (%)

    FCAs (mm2) Radius of

    LFSs (mm)

    Patient A 8.8% 7.69 3.05 0.45 0.11

    Patient B 2.4% 7.59 2.80 0.46 0.11

    Fig. 7 - Incomplete strut apposition (struts colored in red) of the

    implanted stents. Threshold was set at 0.2 mm. The arrowsindicate

    the regions where the fish scaling effect occurs.

    Fig. 8 -Free cell areas of the implanted stents (in mm2).

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    in both cases, also showing the fish scaling effect that is

    typical for stents with an open-cell design. This effect was

    very apparent in patient A at the bifurcation (Fig. 7). Distal

    ISA, which was reported previously (12), occurred in patient

    B, while proximal ISA occurred in both patients.

    The analysis of the deformed free cell area (FCA) showed

    that the target diameter of the vessel highly influences scaf-folding provided by the device. In fact, patient A, with a

    smaller ICA diameter resulted in smaller FCAs compared to

    patient B with a larger ICA diameter (Fig. 8). Also, in patient

    B, the cross-sectional change of the vessel was less pro-

    nounced, leading to a more uniform cell shape deformation.

    The largest fitting spheres (LFSs, Fig. 9) did not pace the

    FCAs distribution. Maximum values of FCAs do not neces-

    sarily correspond to higher values of the LFSs. For patient B,

    the FCAs were similar at the bifurcation and at the mid part

    of the stent, while the LFSs were more inhomogeneous,

    ranging from 0.36 mm to 0.7 mm. High values of LFSs

    were found at the ICA, mainly concerning the cells appos-

    ing at the concave region of the vessel. Similar consider-

    ations can be made for patient A. The chosen stent, which

    corresponds to the diameter requirements of the distal

    ICA, was undersized for the bifurcation diameter result-

    ing in an under-expansion of the stent, leading to a free

    expanded-like configuration with higher FCAs and LFSs.

    It can be noticed that high values of LFCs were also found

    at the distal location of the ICA, again at a concave part of

    the vessel although the FCA is small. This seems reason-

    able when thinking of a bending bar: at the inner curvature,

    stent struts get closer, while at outer curvatures they wid-

    en. The values were also influenced by ISA. In fact, if thecell opens in the radial directions, due to fish scaling,

    this resulted in higher LFSs compared to a more planar

    configuration, as indicated by the green arrow in Figure 9.

    As highlighted in our study, the fish scaling effect is present

    even with moderate tortuosity at the convex lumen regions,

    which will clearly be more accentuated with a more com-

    plex lumen shape. It has been speculated that fish scaling

    may contribute to restenosis and stent fracture (10). The

    price for the absence of shortening, optimal conformability

    and flexibility of the open-cell design leads to plaque scaf-

    folding with lower plaque coverage, possibly promoting

    plaque protrusion through the interstices of the stent struts,

    allowing plaque material to embolize after implantation (45).

    Nevertheless, in the analyzed patients due to a straight,

    post-stented vessel geometry and adequate sizing, the

    stent seems to offer a good scaffolding of the lesion.

    Another variable to take into account may be device over-

    sizing. Even though manufacturers provide guidelines, the

    choice for a certain degree of oversizing still depends on

    operators preference and experience and on the high vari-

    ability of the carotid anatomy. In patient A the stent had a

    larger degree of oversizing, resulting in a higher closure of

    the stent cells at the ICA, offering more lesion protectionand increasing surface covering. Oversizing may, however,

    lead to increased tension on the vessel wall, consequently

    causing neointimal hyperplasia and restenosis (46).

    In a previous finite element study, Conti et al (31) con-

    structed a single atherosclerotic carotid model which was

    virtually treated with four different types of stents (includ-

    ing Acculink); they then analyzed the FCAs. Likewise, the

    authors found a higher reduction of the cell area at the mid-

    dle location as in patient A. Further comparisons are not

    possible due to the different anatomy, the lower degree of

    stenosis, and different stent positioning.

    In the same study, it was highlighted that LFSs in the free

    expanded configuration (as previously described (11)) can-

    not capture differences in stent designs. Nevertheless, it is

    our opinion that LFSs in the deformed configuration can

    be relevant for measuring the maximum plaque particles

    potentially protruding through the stent struts, especially

    when similar devices are compared under different condi-

    tions. LFSs may improve and refine the FCA analysis. In

    fact, cells with similar FCAs may result in various deformed

    shapes that influence the LFSs (see the ICA segment

    in Figs. 8 and 9 for both patients), suggesting that both

    Fig. 9 -Radius of the largest fitting spheres of the implanted stents(in mm).Red arrows show the concave regions of the vessel, which

    are associated with relatively large spheres compared to the convex

    regions. Thegreen arrowshows how the fish scaling can influence

    the dimensions of the spheres.

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    measures are complementary scaffolding parameters.

    LFSs may be regarded as a measure of how opposite strutsof a single cell are tied, i.e. how the cell is assembled.

    An obvious observation from our results is that the speci-

    ficity of the lesion influences stent behavior and its scaf-

    folding capability. CAS success has been often thought to

    be influenced by stent design, i.e. open versus closed stent

    design, but this may be too basic, as previously suggested

    (44). To refine the discrimination, other studies refer to FCA

    in the free expanded configuration (more than open versus

    closed design) to be an important parameter in CAS failure

    (5). However, other stent characteristics, such as conform-

    ability in angulated arteries and low radial force, play a role

    in selecting the appropriate stent (11, 24, 45, 47).

    Measurements of the maximum free cell area and the cell

    pore diameter of the free expanded stent previously pro-

    posed (11) can be misleading, as the deformed configura-

    tion of the deployed stent in a tortuous vessel is neglected.

    Our approach may be more suitable to analyze cell support

    at the lesion. Our results also suggest that global values

    may not be adequate because they can mask zones at

    higher risk for embolization (i.e., at the lipid plaque loca-

    tion). According to Table III, the mean values of FCAs and

    LFSs do not show any difference between the two pa-

    tients, even though the two stents behaved very differently.On the other hand, a visual evaluation of the scaffolding

    parameters of the virtual implantation allows a direct as-

    sessment of the possible CAS outcome scenarios.

    A recent study, using a strategy that is similar to ours, com-

    pared vessel changes after virtual balloon expanded coro-

    nary stenting with clinical images from two patients (48).

    They were able to reproduce the general geometrical fea-

    tures of the stented vessel and performed both a qualitative

    validation (comparing the stented vessel lumen of conven-

    tional angiographic images and the numerical results) and

    a quantitative analysis, measuring the straightening of the

    vessel in one patient for whom CTA data were available. Al-

    though their results suggest the feasibility of their numerical

    approach, the differences in the interventional procedure

    along with the vascular district and parameters used for the

    validation make a direct comparison with the results of our

    work difficult.

    Limitations

    The main limitations of our study are related to the imaging

    techniques used to retrieve pre- and post-operative data.

    The routine pre-operative CTA scans do not provide ac-

    curate information about the vessel wall and, more impor-tantly, about soft plaques. Validation of the results should

    ideally be based on imaging techniques that allow a direct

    comparison with the post-operative 3D stent configura-

    tion and/or are able to quantify the ISA, which cannot be

    measured by conventional angiography. Inadequate CTA

    resolutions can mask abrupt changes of the lesion, thereby

    introducing inaccuracies into the pre-stented vessel geom-

    etry, which may be amplified by the smoothing algorithms

    to clean the surface for model meshing. Also, the artery

    is acquired by CTA at a certain phase of the cardiac cycle,

    which may be different during post-procedural imaging

    due to a different pressure load. The position of the neck

    also changes the carotid configuration (11, 49) potentially

    raising additional mismatches between the numerical and

    the angiographic configurations.

    The difficulties that may be encountered in linking pre- and

    post-treatment imaging data in this hybrid angio suite can

    be shown by some of the pre-operative sequences of pa-

    tient B, who exhibited less accurate validation. Figure 10A

    shows how displacements of the artery (due either to patient

    movements, heart rate or blood pressure changes) during

    the injection of the contrast agent modify the curvature and

    configuration of the pre-stented vessel. Rigorous validationthus requires strict protocols. Ideally, an identical position of

    the patient should be used during the procedure with stable

    hemodynamics. We believe that these factors contribute to

    the initial mismatch between pre- and post-operative data

    as displayed in Figure 10. Moreover, the angiographic pic-

    tures show irregularities and ulceration of the plaque anato-

    my that could not be detected by the CTA scans due to the

    low resolution in the axial direction (arrows in Fig. 10).

    Although the level of complexity in the simulations is high,

    there are still important simplifications in thein vivoloading

    (ignoring blood pressure, pre-stretching and pre-stresses)

    and on the lesion morphology due to routine CTA inability to

    recognize different tissues (lack of ulceration, thrombus or

    fibrous cap, assumptions of vessel thickness, single-layered

    vessel). A multi-detector scanner and specific reconstruc-

    tion algorithms may improve the lesion imaging (50).

    These factors, together with the high variability of the biolog-

    ical materials, complicate a correct per-patient calibration of

    anisotropic material models. An isotropic material model was

    used in this study, which might not adequately describe the

    real biomechanics of the vascular tissue (51). We needed to

    stiffen the artery by scaling the original arterial stress-strain

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    Registry first 2,001 patients. Catheter Cardiovasc Interv.

    2009;73(2):129-136.

    2. Brott TG, Hobson RW II, Howard G, et al; CREST Investiga-

    tors. Stenting versus endarterectomy for treatment of carot-

    id-artery stenosis. N Engl J Med. 2010;363(1):11-23.

    curve in order to achieve better matching with the in vivo

    data. This is in line with the results of Auricchio et al (51) who

    found larger vessel deformation with the mentioned model

    when compared with other literature data. One might specu-

    late that when dealing within vivostructures even accurate

    ex vivoexperimental material description may underestimatethe real stiffness of the treated vessel if the in vivo loading

    conditions and the interaction with surrounding structures

    are neglected. To overcome the latter problem, alternatively

    to our approach, spring elements might be considered to

    describe these interactions as previously suggested (52).

    CONCLUSIONS

    In the present study we used finite element computer mod-

    el simulations to imitate CAS procedures, and applied au-

    tomatic tools to quantify vessel scaffolding in two patients.

    Results showed the feasibility of the proposed method

    with an overestimation of the predicted stented lumen di-

    ameter of 5.31 8.05% and 4.12 9.84% for the two pa-

    tients compared to the clinical outcome. The quantitative

    measurements of the incomplete stent apposition, free cellareas, and largest fitting spheres highlighted the variability

    of device behavior in relation to the target lesion. In gen-

    eral, the free cell area depended on the target diameter and

    oversizing, while the largest fitting spheres and apposition

    values were influenced by the local concavity and convex-

    ity of the vessel region.

    The proposed method, pending a more accurate 3Din vivo

    validation with a larger number of datasets, may be an ad-

    ditional tool for cardiovascular interventionists for a proper

    selection of stent design and more accurate positioning

    in complex anatomies. This would help to reduce post-

    procedural strokes by better assessinga priorithe poten-

    tial risk of embolization.

    ACKNOWLEDGEMENTS

    The authors gratefully acknowledge P. Mortier, PhD, and G.

    De Santis, PhD, for their valuable support in the modeling pro-

    cess and results evaluation, and B. Vandeghinste for sharing

    his expertise in medical imaging.

    Financial Support: This study was financially supported by the

    Research Foundation Flanders (FWO grant 3G06591) and by GhentUniversity (grant BOF10/GOA/005).

    Conflict of Interest:The authors have no financial disclosures. M. De

    Beule and B. Verhegghe are shareholders of FEops, an engineering

    consultancy spin-off from Ghent University, and have served as con-

    sultants for several medical device companies.

    Meeting Presentations:Part of this work was presented at the 11th

    International Symposium on Computer Methods in Biomechanics and

    Biomedical Engineering held in Salt Lake City, Utah, USA, in an oral

    presentation given on April 6, 2013. A poster presentation was given

    on June 12, 2013 at the Multidisciplinary European Endovascular

    Therapy conference in Rome, Italy.

    Address for correspondence:

    Francesco Iannaccone

    Universiteit Gent IbiTech

    De Pintelaan 185, blok B/5

    B-9000 Gent, Belgium

    [email protected]

    Fig. 10 -Mismatch of the pre-operative condition between conven-

    tional angiography and the reconstructed model. Arterial configuration

    changes during contrast agent injection (A). The border (in red)of thevessel from angiography at a certain instant is overlaid on the angiogra-

    phy at a subsequent instant clearly showing the modification of the lu-

    men shape due to loading factors. At the bottom the arterial geometry

    from the CTA is depicted (B). Thered arrowsindicate the irregularities

    not detected in the CTA (B), which are visible in the angiography (A).

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    C o p y r i g h t o f I n t e r n a t i o n a l J o u r n a l o f A r t i f i c i a l O r g a n s i s t h e p r o p e r t y o f W i c h t i g I n t e r n a t i o n a l

    L i m i t e d a n d i t s c o n t e n t m a y n o t b e c o p i e d o r e m a i l e d t o m u l t i p l e s i t e s o r p o s t e d t o a l i s t s e r v

    w i t h o u t t h e c o p y r i g h t h o l d e r ' s e x p r e s s w r i t t e n p e r m i s s i o n . H o w e v e r , u s e r s m a y p r i n t ,

    d o w n l o a d , o r e m a i l a r t i c l e s f o r i n d i v i d u a l u s e .