WO2012126070A1 - Analyse volumétrique automatique et enregistrement 3d d'images oct en section transversale d'un stent dans un vaisseau corporel - Google Patents

Analyse volumétrique automatique et enregistrement 3d d'images oct en section transversale d'un stent dans un vaisseau corporel Download PDF

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WO2012126070A1
WO2012126070A1 PCT/BE2012/000017 BE2012000017W WO2012126070A1 WO 2012126070 A1 WO2012126070 A1 WO 2012126070A1 BE 2012000017 W BE2012000017 W BE 2012000017W WO 2012126070 A1 WO2012126070 A1 WO 2012126070A1
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stent
strut
struts
oct
vessel
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PCT/BE2012/000017
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Tom Adriaenssens
Giovanni Jacopo UGHI
Walter Desmet
Jan Dhooge
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Katholieke Universiteit Leuven
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G06T7/0014—Biomedical image inspection using an image reference approach
    • G06T7/0016—Biomedical image inspection using an image reference approach involving temporal comparison
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
    • G06T7/33—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
    • G06T7/38—Registration of image sequences
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2200/00—Indexing scheme for image data processing or generation, in general
    • G06T2200/04—Indexing scheme for image data processing or generation, in general involving 3D image data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10101—Optical tomography; Optical coherence tomography [OCT]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20004—Adaptive image processing
    • G06T2207/20012—Locally adaptive
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30052—Implant; Prosthesis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30101—Blood vessel; Artery; Vein; Vascular

Definitions

  • the present invention relates generally to diagnosing late incomplete stent apposition and coverage via optical coherence tomography at multiple time points and, more particularly to a system and method for diagnosing late incomplete stent apposition and lack of coverage and comparing same vessel slice on an image level or for diagnosing the severity of stent malapposition and uncoverage by computer automated volumetric analysis and three dimensional (3D) registration of optical coherence images of a stent in body vessel.
  • ISA incomplete stent apposition
  • stent malapposition is defined as the absence of contact between stent struts and the vessel wall not overlying a side branch.
  • Stent coverage is defined as the presence of a thin layer of neointimal tissue covering metallic stent surface.
  • Acute ISA (detected immediately after stent implantation) is differentiated from late ISA (LAISA). No adverse events appear to be associated with acute ISA. Patients with LAISA have no ISA at the time of stent implantation, but ISA is detected at follow-up. Its degree can vary from one malapposed strut to true coronary aneurysm formation. IVUS studies have shown that LAISA is more frequent after DES (10%-20%) than BMS (5%-10%) implantation. Compared with these IVUS studies, recent studies with OCT point to a higher incidence of LAISA, especially in patients with acute coronary syndromes. Lack of coverage has been associated with thrombus formation and more in general to BMS and DES failure.
  • the invention is broadly drawn to a method of automatic image analysis to an accurately diagnose late incomplete stent apposition and coverage and registering OCT datasets of a same vessel and device through time.
  • One aspect of the invention concerns a computer based method of automated volumetric stent analysis comprising computer based analysing of multiple images from in-vivo acquired cross sectional OCT images, recorded by an OCT catheter pullback at baseline (freshly implanted device) and at any time point thereafter, of long vessel segments of stent formed by strut from bars in a body vessel (e.g.
  • the method comprising automatic distance identification and measurement of the separate surfaces zones of said implant device to vessel wall (apposition) and/or covering neointima (corresponding to vessel healing after implantation of the implant device) and the method being characterised in that 1 ) further areas and volumes of separate surfaces zones of a) said implant device to vessel wall (apposition) or b) of neointima coverage are volumetric measured by clustering of neointima coverage according to their spatial 3D location through analysis of equally spaced consecutive OCT cross sectional images or by automatically clustering uncovered (malapposition) struts according to their 3D spatial position through consecutive cross sectional images and 2) registration of two clouds from pullback recordings of points of long vessel segments of transluminal implant device but at different time points of the implantation period comprising a) 3D alignment by rotation and b) refinement of the registration by a closes point registration algorithm (e.g.
  • ICP Interactive closest point
  • These malapposed (uncovered) struts can be into different groups according to their 3D position in space and consequently for every cluster, properties like relative long-axis position, length in long-axis direction, angle distribution, number of struts belonging to the group and maximum, minimum and mean strut malapposition values is quantified.
  • properties like relative long-axis position, length in long-axis direction, angle distribution, number of struts belonging to the group and maximum, minimum and mean strut malapposition values is quantified.
  • the number and percentage of apposed and malapposed (covered uncovered) struts, the number of embedded and protruding struts and/or the number of isolated malapposed struts and clusters can be measured.
  • the volume of the whole malapposition i.e.
  • space between stent to vessel wall is preferably quantified by 1 ) stent shape delineation and 2) scan conversion to pixels followed by image quantification involving a) strut by strut individual apposition and/or neointima coverage analysis and b) lumen area and/or stent area analysis and 3) computing the relative difference between lumen area and stent area and 3) volumetric quantification of the apposition area and/or neointimal tissue area through consecutive cross sectional images for volume estimation lumen volume, stent volume and the apposition volume or neointimal tissue volume.
  • the used algorithmic clustering technique is adapted to define groups of variables with homogenous properties within the group they belong to and inhomogeneous properties between other groups.
  • Such algorithmic clustering technique is preferably a non-supervised clustering techniques based on distance measurements ( in order to cluster malapposed struts) whereby clusters are defined through a hierarchical agglomerative clustering algorithm composed of three steps (1 ) definition of a distance measure (for example Euclidean distance), (2) computation of the pair-wise distance matrix D between all the elements and (3) iterative fusion of the 2 most similar elements in a cluster and updating of matrix D (e.g. Single Linkage (SL)).
  • a distance measure for example Euclidean distance
  • Such algorithm produces as output a hierarchical tree which starts from leaves (single strut) and ends into a super-cluster containing all the struts of an analysed long vessel zone of a stent in a body vessel.
  • This hierarchical tree is obtained through subsequent fusion of elements in clusters becoming bigger step by step (agglomerative methods) and whereby the number of the clusters is dependent on the level at which the tree is observed.
  • the final choose of the number of clusters can be carried out manually (pre-setting at which level the tree is observed) by the user.
  • the final choose of the number of clusters can be through a cut-off value.
  • OCT alignment of a dataset of two pullbacks of the same stented vessel at different times can comprise a first step of computing 3D structures aligned according to same centre and same long-axis direction.
  • centre of mass of both OCT pullbacks can be computed using the strut coordinates as a system of particles according to the following function:
  • the 3D datasets can hereby consequently be shifted through the 3D space in order to match the two centres of mass and the moments of inertia of the two objects are then computed and the 3D datasets consecutively aligned in the long-axis direction (z) and the moment of inertia of a rigid object of N point masses rrv, can be computed as in the following matrix:
  • N the number of points belonging to S2 whereby p, represent a point in the Euclidean space and S a surface in the same space, the distance between p, and S is defined as:
  • the cost function gives information about the distance between the two objects for every possible rotation (figure 11 ) and whereby minimization of the cost function f cos t will result in the rotation able to match the two 3D objects.
  • Yet another embodiment of present invention is use of the method according to embodiments of present invention (here above described) to compare 3D intravascular OCT datasets frame-to-frame.
  • This can be used on pullbacks of the same vessel at different intervals (e.g. baseline, 3 months, 6 months and 9 months) to measure or follow the vessel healing process, after stent implantation, comparing corresponding frames at the different time intervals (e.g. baseline, 3 months, 6 months and 9 months; to assessment on how human coronary arteries react at implantation of different kinds of stents or to asses on how human coronary arteries react after stent implantation on drug treatments.
  • Yet another embodiment of present invention is an OCT apparatus with a processor adapted to receive input signals generated by pullbacks of an OCT catheter whereby the processer comprises an algorithm to carry out data processing according to any one of methods described here above. .
  • a stent is a transluminal implant device or a longitudinal tubular implant device formed by strut from bars. It is a vascular stent when the transluminal implant device is for vascular vessels.
  • Stent malapposition is defined as the absence of contact between stent struts and the vessel wall not overlying a side branch
  • a computer based method was developed in our group for analysing the behaviour of a transluminal implant, for instance of an intracoronary implant device, which method comprises computer based analysing of multiple images from in-vivo acquired OCT images, recorded at baseline (freshly implanted device) and at any time point thereafter, of long vessel segments of transluminal implant device formed by strut from bars or of a longitudinal tubular implant device formed by strut from bars in a body vessel, said method comprising: - automatic contouring and identifying structures of the in-vivo acquired images, - segmentation of the implant device and lumen based on A-scan analysis by (1) a bright reflection, (2) a shadow and (3) a rapid rise and fall of energy, - scan conversion of the image to a fast analysis platform, - evaluation of strut apposition or strut coverage from A-line segmentation (A-scan lines) in the scan-converted image, and wherein said method provides automatic identification and measurement of both 1 ) distance of the separate surfaces zones of said implant device to vessel
  • the segmentation comprises or consists of three steps: (1 ) pre-processing of the image, (2) identification of candidate A-scan lines, (3) processing of the candidates.
  • multiple zone or structure of the implant can be individually analysed for providing such stent strut to vessel wall distance (and neointima coverage) for different individual stent struts zones in the image.
  • Such multiple zones or structures are multiple views or segments of a strut or the multiple zones or structures are multiple images of transversal sections of a strut of the longitudinal tubular implant or the multiple surfaces are formed by at least one axial strut.
  • Such stent struts segmentation is based on A-scan lines analysis whereby a strut in OCT images is characterized by: (1 ) a bright reflection (2) a shadow and (3) a rapid rise and fall of energy.
  • Such segmentation can comprise or consist of three steps: (a) preprocessing of the image, (b) identification of candidate A-scan lines, (c) processing of the candidates.
  • the pre-processing of images can involve image calibration, whereby the calibration line coincides with the catheter plastic border, whereby using the calibration line as a reference, the catheter can be optionally automatically recognized and ignored from the algorithm for strut segmentation (and lumen segmentation later).
  • the A-scan line candidates are identified by searching for strut characteristics whereby the maximum intensity value of each line is located and recorded and if this value exceeds a selected threshold the line is labelled as a candidate.
  • Such A-scan line candidates can be processed by removing the non-desired lines by analysing the shadows coming from a strut and taking into account the depth of the shadow excluding of false candidates (i.e.
  • the horizontal coordinates of he located struts are identified by the following steps: 1 ) the algorithm takes as a starting point the coordinate founded in step b, and from this point, a correction based on intensity level is performed in order to avoid errors resulting from reverberations, blood remnants over the stent (in baseline pullbacks) and misalignment of the coordinates so that the end of this last step, almost all stent struts are correctly segmented avoiding false positive results.
  • the lumen segmentation can be based on A- scan line analysis consisting of consists of three steps: (1 ) pre-processing of the image, (2) A-line single analysis and (3) correction considering all A-lines together.
  • Lumen segmentation comprises or consists of three steps: (a) pre-processing of the image, (b) identification of candidate A-scan lines, (c) correction.
  • the pre-processing step for lumen segmentation comprises optionally removal of the strut positive A-scan lines which are removed from the image and comprises the three steps (1 ) based on the histogram of the whole image, turning all the pixels under a selected intensity value are to zero and incrementing all the pixels above another selected fixed value are to a higher intensity.
  • (2) applying a Gaussian symmetric low-pass filter with a 5 pixel square dimension and sigma equal to 2.5 is applied and (3) morphologically opening the image using a disk structuring element.
  • Example 1 ASSESSMENT OF AREAS AND VOLUMES
  • stent struts After lumen borders are fully-automatically traced and stent struts automatically located (previous paragraphs), measurements representing individual stent strut apposition (figure 1A) and neointima coverage (figure 1 B) are automatically obtained. From 2D spatial coordinates of stent struts in a single frame, the stent contour is traced interpolating different curves and splines. In case of curves (i.e. ellipse), the mean least square is used to fit the best curve through the stent coordinates (figure 3). The use of splines needs optimization of multiple parameters in order to obtain the correct approximation of the stent contour.
  • the second step is obtained through least square optimization extracting ellipse parameters from the conic ellipse equation.
  • the image is analysed to asses if the stent shape can reliably be identified.
  • Inclusion criteria are defined as the following: (1 ) the image is divided in four quadrants using the centre of mass of the lumen as reference, (2) each quadrant must contain at least two struts. Frames that do not satisfy these requirements are processed only for individual strut assessment and lumen area.
  • Measurements are performed in the Cartesian domain after scan conversion to images of pixel dimensions 1024 x 1024. Image quantification is made in two steps:
  • the relative strut is labelled as apposed, otherwise it is labelled as malapposed. In the same way it is possible to discriminate between covered/uncovered struts. In this last case the threshold is taken equal to 20 m.
  • Lumen area (LA) is obtained through numerical trapezoidal integration of the lumen- spline.
  • Stent area (SA) is computed with the equation of the area of the ellipse.
  • Apposition area (AA) and neointimal tissue area (NTA) is obtained computing the "positive" difference between L A and S A (figure 2): intersection points between the two curves are identified and areas divided into different sectors. In the case of freshly implanted stents, the sum of sectors where the difference between lumen area and stent area is positive, represents apposition area (AA).
  • neo-intimal tissue area (NTA) is represented by the sum of sectors where the difference between stent area and lumen area (opposite as previous) is positive. Volumetric quantification is then be obtained estimating AA (or NTA) through consecutive slices cross images).
  • the IV-OCT system acquires slices at a predefined speed and frame rate, the distance between adjacent slice is constant (i.e. 0.03 mm).
  • Multiplying AA (NTA, l_Area > SArea) values of consecutive slices by their relative longitudinal slice distance allows for estimation of lumen, stent and apposition/neointimal-tissue volumes.
  • Apposition volume represents the space between vessel wall and stent surface, neo- intimal tissue volume the amount of tissue growth over the stent surface (figures 4 and 5).
  • Validation was performed by evaluating a test set of 108 in-vivo images randomly selected from 9 different in-vivo pullbacks. Areas analysis was performed manually by two trained cardiologists and by the automated procedure. Regression analysis was performed and correlation between the manual and the automatic measurements was expressed using the Pearson's correlation coefficient. Agreement between both measurements was assessed through Bland-Altman analysis.
  • Registration of pullbacks of the OCT catheter which creates cross sectional images of the same vessel (e.g. vascular vessel) at different times will allow the users to directly follow the vessel healing process, after stent implantation, comparing corresponding frames at different time intervals (e.g. baseline, 3 months, 6 months and 9 months).
  • Registration of stented segments is done through automatic identification of stent strut coordinates. As soon as two pullbacks of the same stented vessel at different times are analysed, as previously reported, position of stent struts in 3D space is available. Then a reconstruction of the stent structure is computed and represented (figure 8). Starting from these data, the algorithm for registration of clouds of points (for example ICP, iterative closest points) can compute the transformation able to match the two clouds. Registration results will allow the user to compare corresponding frames at different times. Assessment on how human coronary arteries react at implantation of different kinds of stents and drug treatments is of vital importance for clinicians and stent manufacturers. 2.1.OCT dataset alignment
  • 3D structures must be automatically aligned: same centre and same long-axis direction. Centre of mass of both OCT pullbacks are computed using the strut coordinates as a system of particles:
  • Final output of the registration procedure (figure 14) is the transformation (rotation, translation) able to match the two pullbacks: translation matrix, rotation matrix (rototranslation matrix) are the outputs.
  • Results of the registration is used to compare 3D intravascular OCT datasets frame- to-frame (figure 14).
  • a method of present invention able to appreciate malapposition as a volume, and not every strut individually, is the clustering of malapposed struts according to their spatial 3D location. Malapposed struts are divided into different groups and for every group relevant properties is quantified.
  • the proposed method concerns completely automatic detection (segmentation) of stent struts and lumen for each frame. Consecutively, discrimination of apposed from malapposed struts is obtained with the use of a threshold computed as follows:
  • a T strut thickness + polymer thickness (in case of drug eluting stents) + blooming artefacts.
  • Malapposed and uncovered struts are clustered into different groups according to their 3D position in space (figure 15). Then, for every cluster, properties like relative long-axis position, length in long-axis direction, angle distribution, number of struts belonging to the group and maximum, minimum and mean strut malapposition values is quantified. In addition, general results (of the whole stented sector) about the number and percentage of apposed and malapposed struts, number of embedded and protruding struts, number of isolated malapposed struts and clusters is consecutively obtained.
  • the volume of the whole malapposition i.e. space between stent to vessel wall
  • neointimal coverage is quantified with the methods reported at paragraph 1.2 and 1.3.
  • Clustering is a technique which aims to define groups of variables with homogenous properties within the group they belong to and inhomogeneous properties between other groups. In order to cluster malapposed and uncovered struts, non-supervised clustering techniques based on distance measurements is used. Clusters are defined through a hierarchical agglomerative clustering algorithm composed of three steps (figure 16):
  • Si(x,y,z) and Sj(x,y,z) are two elements in Cartesian space with z representing long-axis position.
  • Other kinds of distance like Mahalanobis or city- block, is used instead of the simplest Euclidean distance.
  • Figure 1 provides examples of individual strut segmentation & quantification, lumen contouring and lumen area quantification.
  • Panel A contain an example of apposition analysis, panel B of coverage analysis.
  • Figure 2 provides an example of lumen and stent-ellipse overlapping. In order to correctly estimate apposition and neo-intimal tissue area, discrimination of sectors where lumen area is bigger than stent area, and opposite case, is needed.
  • Figure 3 provides lumen and stent segmentation (Panels A and B) and examples of tracing of stent contour (Panels C and D)
  • Figure 4 is a graphic display with left panel: malapposition area assessment (in purple or [A] in figure). Right panel: neointimal tissue area assessment (in green or [B] in figure).
  • Figure 6 is a graphic of a stent strut assessment in case of sunflower artefacts
  • Figure 7 provides graphics that demonstrate the area assessment validation
  • Figure 8 provides a figure which contains two unregistered 3D stent
  • Figure 9 is a graphic display of 3D stent after centre of mass alignment
  • Figure 10 is a graphic display of moment of inertia in the long-axis direction z (in green or [C] in figure).
  • Figure 11 is a graphic display of cost function for correct rotation
  • Figure 12 is a graphic display of 3D stent after alignment and rotation
  • Figure 13 is a graphic display of refining of the registration results after clouds matching (algorithm likes icp)
  • Figure 4 is a graphic display of frame-to-frame registration results to compare same vessel after different instants of time.
  • Figure 15 provides an example of malapposition clustering. Colored dots (or different grey tone) correspond to malapposed struts, different colour to different clusters. Blue line ([D]) is a reference starting and ending on the two edges of the stent.
  • Figure 16 is a graphic display of hierarchical agglomerative clustering: five steps iterative procedure for a six elements database. At every step the distance matrix and the hierarchical tree are computed (red number correspond to the length of the branches); figure also illustrates how elements are merged into different clusters.

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Abstract

L'invention concerne un procédé informatisé d'analyse de stent volumétrique automatisée et d'enregistrement de retraits OCT (ensembles de données) dans le temps comprenant l'analyse informatisée de plusieurs images OCT en section transversale acquises in vivo, enregistrées par un retrait de cathéter OCT à la ligne de base (dispositif récemment implanté) et à tout autre moment consécutif, de segments longs du stent formés dans un vaisseau corporel (par ex. vasculaire). Le procédé comporte l'identification de distance automatique et la mesure de zones de surface séparées du dispositif implant sur la paroi du vaisseau (apposition) et/ou couvrant la néointima (correspondant à la guérison du vaisseau après implantation du dispositif implant). Le dispositif est caractérisé en ce que 1) la couverture de supports de stent individuels et l'apposition ainsi que d'autres zones et volumes de zones de surface séparées a) dudit dispositif implant sur la paroi de vaisseau (apposition) ou b) la couverture de la néointima sont mesurées de façon volumétrique par groupement de la couverture de la néointima selon la position 3D spatiale par analyse d'images en section transversale OCT consécutives espacées uniformément ou par groupement automatique de supports non couverts (malapposition) selon la position 3D spatiale par analyse d'images en section transversale, et 2) l'enregistrement de retraits OCT intravasculaires (ensembles de données OCT-IV) de points de segments longs de vaisseaux du dispositif implant transluminal à différents instants de la période d'implantation comporte a) l'alignement 3D par rotation et translation et b) l'affinage de l'enregistrement par un algorithme d'enregistrement (par ex. point interactif le plus proche (ICT)) et multi-résolution itérative.
PCT/BE2012/000017 2011-03-24 2012-03-26 Analyse volumétrique automatique et enregistrement 3d d'images oct en section transversale d'un stent dans un vaisseau corporel WO2012126070A1 (fr)

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WO2014137353A1 (fr) 2013-03-08 2014-09-12 Lightlab Imaging, Inc. Systèmes, dispositifs et méthodes de visualisation d'endoprothèse vasculaire et de détection d'apposition incorrecte
CN104077808A (zh) * 2014-07-20 2014-10-01 詹曙 一种用于计算机图形图像处理的、基于深度信息的实时三维人脸建模方法
CN104688190A (zh) * 2015-03-18 2015-06-10 深圳市中科微光医疗器械技术有限公司 检测冠状动脉内支架贴壁情况的装置
US9299139B2 (en) 2012-03-26 2016-03-29 The Cleveland Clinic Foundation Volumetric analysis of pathologies
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US20160335766A1 (en) * 2015-05-17 2016-11-17 Lightlab Imaging, Inc. Intravascular Imaging System Interfaces and Stent Detection Methods
WO2016187218A1 (fr) * 2015-05-17 2016-11-24 Lightlab Imaging, Inc. Interfaces de système d'imagerie intravasculaire et procédés de détection d'ombre
WO2016187231A1 (fr) * 2015-05-17 2016-11-24 Lightlab Imaging, Inc. Interfaces de système d'imagerie intravasculaire et procédés de détection d'endoprothèse
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US11287961B2 (en) 2015-07-25 2022-03-29 Lightlab Imaging, Inc. Intravascular data visualization and interface systems and methods
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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100094127A1 (en) * 2008-10-14 2010-04-15 Lightlab Imaging, Inc. Methods for stent strut detection and related measurement and display using optical coherence tomography
WO2011135103A1 (fr) 2010-04-30 2011-11-03 Katholieke Universiteit Leuven, K.U. Leuven R&D Analyse d'images intracoronaires obtenues par tomographie par cohérence optique

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100094127A1 (en) * 2008-10-14 2010-04-15 Lightlab Imaging, Inc. Methods for stent strut detection and related measurement and display using optical coherence tomography
WO2011135103A1 (fr) 2010-04-30 2011-11-03 Katholieke Universiteit Leuven, K.U. Leuven R&D Analyse d'images intracoronaires obtenues par tomographie par cohérence optique

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
BESL P J ET AL: "A METHOD FOR REGISTRATION OF 3-D SHAPES", IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, IEEE SERVICE CENTER, LOS ALAMITOS, CA, US, vol. 14, no. 2, 1 February 1992 (1992-02-01), pages 239 - 256, XP000248481, ISSN: 0162-8828, DOI: 10.1109/34.121791 *
GARRET T BONNEMA ET AL: "An automatic algorithm for detecting stent endothelialization fAn automatic algorithm for detecting stent endothelialization from volumetric OCT datasets", PHYSICS IN MEDICINE AND BIOLOGY, TAYLOR AND FRANCIS LTD. LONDON, GB, vol. 53, no. 12, 21 June 2008 (2008-06-21), pages 3083 - 3098, XP020133865, ISSN: 0031-9155 *
N. ROSENTHAL ET AL: "Unravelling the endovascular microenvironment by optical coherence tomography", EUROPEAN HEART JOURNAL, vol. 31, no. 2, 19 November 2009 (2009-11-19), pages 139 - 142, XP055034955, ISSN: 0195-668X, DOI: 10.1093/eurheartj/ehp481 *
SERHAN GURMERIC ET AL: "A New 3-D Automated Computational Method to Evaluate In-Stent Neointimal Hyperplasia in In-Vivo Intravascular Optical Coherence Tomography Pullbacks", 20 September 2009, MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION Â MICCAI 2009, SPRINGER BERLIN HEIDELBERG, BERLIN, HEIDELBERG, PAGE(S) 776 - 785, ISBN: 978-3-642-04270-6, XP019130527 *
UGHI G J ET AL: "Automatic segmentation of in-vivo intra-coronary optical coherence tomography images to assess stent strut apposition and coverage", THE INTERNATIONAL JOURNAL OF CARDIAC IMAGING, KLUWER ACADEMIC PUBLISHERS, DO, vol. 28, no. 2, 24 February 2011 (2011-02-24), pages 229 - 241, XP035021771, ISSN: 1573-0743, DOI: 10.1007/S10554-011-9824-3 *

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