WO2011135103A1

WO2011135103A1 - Intracoronary optical coherence tomography images analysis

Info

Publication number: WO2011135103A1
Application number: PCT/EP2011/056982
Authority: WO
Inventors: Giovanni Jacopo Ughi; Tom Adriaenssens; Walter Desmet; Jan Dhooge
Original assignee: Katholieke Universiteit Leuven, K.U. Leuven R&D
Priority date: 2010-04-30
Filing date: 2011-05-02
Publication date: 2011-11-03

Abstract

Present invention concerns a new image processing technology and apparatus for the automatic quantification and analysis of optical coherence tomography images of the coronary arteries.

Description

[DESCRIPTION]

INTRACORONARY OPTICAL COHERENCE TOMOGRAPHY IMAGES

ANALYSIS

The present invention relates generally to the field of medical images processing and, more specifically to a system and method to analyse automatically intracoronary optical coherence tomography (OCT) images. In particular the present invention relates to image processing and apparatus for the automatic quantification and analysis of optical coherence tomography images, e.g. of coronary arteries, the urinary tract or the bile duct as well as software for carrying out such processes.

BACKGROUND OF THE INVENTION At present, the analysis of OCT images is performed by having the operator identifying and measuring the relevant structures manually. In this way, luminal area, stent strut to vessel wall distance (apposition) and neointima coverage (corresponding to vessel healing after stent implantation) can be measured. OCT allows for imaging of long vessel segments: generally, around 300 images (frames) per patient need to be analysed. In addition, manual analysis is based on personal interpretation of the colormap representing the image. Comparing results of two different manual assessments coming from same test set of images (60 images containing 496 struts), a relatively high intra- and inter- observer variability has been observed: measurements show different values up to 20%. As manual contouring and identifying structures is very time consuming and labor intensive, this makes the technique impractical and less suitable for clinical routine. Three minutes is the average time computed for manual analysis of a single image. Approximately one second is the real time required from the algorithm to analyze an image. Analysis of for example 100 images manually requires up to 5 hours, the algorithm requires less than two minutes.

There is a need for tools based on algorithms that would allow for an objective and fast automated analysis of in-vivo acquired OCT images that provides an accurate analysis (in terms of miss, false positive and accuracy in segmentation of the borders of stent and vessel) for both stent coverage and apposition and preferably provides such stent strut to vessel wall distance (and neointima coverage) for different individual stent strut zones or different zones in a set of interconnected struts (typically the longitudinal struts) in the image. Such would be extremely valuable for the follow-up of stent behavior after stent implantation, for instance intracoronary implantation and for the prediction of the risk of stent thrombosis or restenosis after such implantation. For the physician, it is very important to obtain accurate information on the success of implantation (e.g. assessment of malapposition, thrombus or neointimal coverage) to guide the interventionalist in further decision making. Bonnema G. et al disclose "An automatic algorithm for detecting stent endothelialization from volumetric optical coherence tomography", in IOP Physics in medicine and biology, 53, p. 3083-3098 (2008). However, the method based on this algorithm has two limitations: (1) it is limited to in-vitro images and (2) it is limited to coverage analysis and not to stent apposition.

Unal G. et al disclose "Stent implant follow-up in intravascular optical coherence tomography images", Int J cardiovascular imaging 24 September 2009 [Epub ahead of print]. This method is limited to assessment of the area of lumen and stent. SUMMARY OF THE INVENTION

An object of the present invention is to provide alternative methods of medical image processing and, more specifically, to provide an alternative system or method to analyse optical coherence tomography (OCT) images. In particular an object of some embodiments of the present invention is to provide image processing and apparatus for the automatic quantification and analysis of optical coherence tomography images, e.g. of coronary arteries, the urinary tract or the bile duct as well as software for carrying out such processes. More in particular the present invention provides alternative image processing and apparatus for automatic stent and lumen analysis. The images may be 2 dimensional (pixels) or 3 dimensional (voxels).

Embodiments of the present invention relating to a method, an algorithm or a system can have the advantages that: (1) it works for in-vivo acquired images and (2) it provides an accurate analysis for both stent coverage and apposition. Embodiments of the present invention provide a method, an algorithm, or a system that is able to provide an individual per strut analysis, as created by a transversal image present (cfr. infra).

In some embodiments, the present invention can provide methods or apparatus performing a completely automated and very fast analysis of lumen and stent images in intra-coronary optical coherence tomography images or for images of the urinary tract or the bile duct or in other images where stents are used.

In one aspect of the invention, the analysis is very robust because the algorithm is able to analyse images corresponding to a large number of different situations (malapposition, restenosis, neointima coverage ...) Another aspect of the invention is that every single structure is individually analysed, providing accurate measurements at a stent strut level.

In still another aspect of the invention, the analysis is very easy to perform via a user friendly graphical interface.

In an embodiment of the invention, an optical signal acquisition and processing apparatus comprises a light source to generate a light that can penetrate into a scattering medium for generating micrometer-resolution three-dimensional images of long vessel segments of a transluminal implant device with multiple separate surface zones or of a longitudinal tubular implant device with multiple surfaces zones within a body vessel, the apparatus further comprising a processor for receiving and processing signals from said its detector for automatic contouring and identifying structures of the in-vivo acquired images, characterized in that the processor is programmed for automated identifying and measuring both 1) the distance of the separate surfaces zones of said implant device to vessel wall (apposition) and 2) neointima coverage (corresponding to vessel healing after implantation of the implant device).

In a preferred embodiment of the invention an optical signal acquisition and processing apparatus comprises a light source to generate a light that can penetrate into a scattering medium for generating micrometer-resolution three-dimensional images of long vessel segments of a transluminal implant device with multiple separate surface zones or of a longitudinal tubular implant device with multiple surfaces zones within a body vessel, the apparatus further comprising a processor for receiving and processing signals from said its detector and for:

- automatic contouring and identifying structures of the in-vivo acquired images,

- segmentating the implant device and lumen based on A-scan analysis characterized by (1) a bright reflection, (2) a shadow and (3) a rapid rise and fall of energy,

- converting the scan of the image to a fast analysis platform (e.g. C++),

- evaluating the strut apposition or strut coverage from A-line segmentation (A-scan lines) in the scan-converted image, and, characterized in that the processor is programmed for automated identifying and measuring both 1) the distance of the separate surfaces zones of said implant device to vessel wall (apposition) and 2) neointima coverage (corresponding to vessel healing after implantation of the implant device).

In another embodiment of the invention, the optical signal acquisition and processing apparatus comprises a processor which is programmed for automated identifying and measuring beside both implant coverage and apposition is also programmed for automated identifying and measuring of the luminal area.

In yet another embodiment of the invention the multiple separate surfaces zones are different strut sections, in particular of axially aligned stent struts or of a set of interconnected struts (typically the longitudinal struts).

In other embodiments of the invention the distance of the separate surfaces zones of said implant device to vessel wall concerns the stent strut to vessel wall distance of said implant device. In another embodiment of the invention the apparatus comprises a processor whereby the processor is programmed is to provide stent strut to vessel wall distance for different individual stent struts zones in the image.

In one embodiment of the invention the processor is programmed to provide neointima coverage for different individual stent struts zones in the image. In some embodiments of the invention the optical signal acquisition and processing apparatus is an optical coherence tomography (OCT) apparatus. In other embodiments of the invention the optical signal acquisition and processing apparatus is a spectral domain or Fourier domain OCT (SEFD-OCT) apparatus. In yet another embodiment of the invention the apparatus comprises a storage means to store the processes signal electronically.

In one embodiment of the invention the apparatus comprisesa display means, which can display the coverage, apposition and or luminal area for the stent or for individual stent struts zones.

In another embodiment of the invention apparatus comprises a near-infrared light source. In yet another embodiment of the invention the apparatus comprises a long wavelength light source.

In one embodiment of the invention the apparatus is used to analyse images corresponding to a multiple parameters of implant disorder or of implant failure parameters such as malapposition, thrombus, restenosis. In other embodiments of the invention the apparatus is used to follow up of stent behaviour after stent implant, for instance intracoronay implant. In another embodiment the apparatus is used for estimating the risk of for instance thrombosis or restenosis after such implantation. In yet another embodiment of the invention the apparatus is used to provide the physician with information to evaluate the success of implantation of a transluminal implant for instance to asses malapposition, thrombus or restenosis.

In some embodiments of the invention the apparatus is used to assess reocclusion or obstructions after angioplasty for instance to treat a problem of neointimal hyperplasia. In other embodiments of the invention the apparatus is used to estimate the need to redilate or to evaluate the incidence and/or severity of post-operative elastic recoil restenosis after percutaneous endovascular therapy by stent placement. In yet another embodiment of the invention the apparatus is used for in-vivo images analysis to measure coverage and stent apposition individual analysis of multiple stent strut visualization in an image. In addition, in other embodiments of the invention the apparatus is used in such way that the images to be analysed are recorded at baseline (freshly implanted device) and at any time point thereafter.

In one embodiment of the invention the apparatus is used for discovering a medicament to prevent reocclusions or obstructions after angioplasty. In another embodiment of the invention the apparatus is used for diagnostic medical treatments of a subject to diagnose for a transluminal implant body reaction, in particular stent body reaction. The invention provides in one embodiment a method for automatically analysing the behaviour of a transluminal implant, for instance of an intracoronary implant device, which method comprises analysing 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, characterised in that said method comprises automatic contouring and identifying structures of the in- vivo acquired images, characterized in that the processor is programmed for automated operator identifying and measuring both 1) distance of the separate surfaces zones of said implant device to vessel wall (apposition) and 2) neointima coverage (corresponding to vessel healing after implantation of the implant device). In another embodiment of the invention the method comprises three processing steps of (1) segmentation of the implant device and lumen based on A-scan analysis, (2) scan conversion of the image to a fast analysis platform (e.g. C++), (3) evaluation of strut apposition or strut coverage from A-line segmentation (A-scan lines) in the scan- converted image. In yet another embodiment of the invention the method comprises that the stent struts segmentation is based on A-scan lines analysis and a strut in OCT images is characterized by: (1) a bright reflection, (2) a shadow and (3) a rapid rise and fall of energy.

In a preferred embodiment of the invention, the invention provides a method for automatically analysing the behaviour of a transluminal implant, for instance of an intracoronary implant device, which method comprises analysing 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, characterised in that said method comprises:

- segmentation of the implant device and lumen based on A-scan analysis characterized 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 (e.g. C++),

- evaluation of strut apposition or strut coverage from A-line segmentation (A-scan lines) in the scan-converted image, and

wherein said method is characterized in that the processor is programmed for automated operator identifying and measuring both 1) distance of the separate surfaces zones of said implant device to vessel wall (apposition) and 2) neointima coverage (corresponding to vessel healing after implantation of the implant device).

In some embodiments of the invention the segmentation used in the method consists of three steps: (1) preprocessing of the image, (2) identification of candidate A-scan lines, (3) processing of the candidates. For example, in the method multiple zones or structures of the implant are individually analysed for providing such stent strut to vessel wall distance (and neointima coverage) for different individual stent struts zones in the image. The multiple zones or structures can be multiple views or segments of a strut. Or the multiple zones or structures can be multiple images of transversal sections of a strut of the longitudinal tubular implant. The multiple surfaces are formed optionally by at least one axial strut.

Stent struts segmentation can be based on A-scan lines analysis and a strut in OCT images can be characterized by: (1) a bright reflection, (2) a shadow and (3) a rapid rise and fall of energy. Preferably, the segmentation comprises or consists of three steps: (a) preprocessing of the image, (b) identification of candidate A-scan lines, (c) processing of the candidates. The preprocessing of image can involve image calibration, whereby the calibration line coincides with the catheter plastic border. Using the calibration line as a reference, the catheter can be automatically recognized and ignored from the algorithm for strut segmentation (and lumen segmentation later).

The A-scan line candidates can be 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 labeled as a candidate. In another example, the A-scan line candidates are processed by removing the non-desired lines by analyzing the shadows coming from a strut and taking into account the depth of the shadow excluding false candidates (i.e. false candidates because of thrombus, plaque protrusion, blood remnants or other "object" inside the lumen), whereby the intensity level of all the pixels or voxels beyond the strut is analyzed and the pixels or voxels divided into three groups: (1) close to the struts, (2) medium distance and (3) far away whereby the intensity of each group is analyzed and if it exceeds a certain fixed value, the candidate is discarded. The horizontal coordinates of the located struts can be 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. It comprises or consists of three steps: (1) preprocessing of the image, (2) A-line single analysis and (3) correction considering all A-lines together. Alternatively, the lumen segmentation comprises or consists of three steps: (a) preprocessing of the image, (b) identification of candidate A-scan lines, (c) correction.

The preprocessing step for lumen segmentation can comprise optionally removal of the strut positive A-scan lines. Removal of these from the image can comprise the three steps (1) based on the histogram of the whole image, turning all the pixels or voxels under a selected intensity value to zero and incrementing all the pixels or voxels above another selected fixed value to a higher intensity, (2) applying a Gaussian symmetric low-pass filter, e.g. with a 5 pixel square dimension or a 5 voxel cube dimension and e.g. a sigma equal to 2.5 and (3) morphologically opening the image using a disk structuring element.

The candidate A-scan lines identification can comprise analysing every line locating the shallowest pixel or voxel with an intensity greater than a certain fraction of the maximum intensity value, e.g. 2/3 of the maximum intensity value of the line and from the located points, moving the code s back in the direction of the lumen for a predefined maximum number of steps stopping when finding the first pixel or voxel with an intensity value inferior to a second lower fraction such as 2/7 of the maximum to generate a rough lumen profile. Optionally the correction of the rough lumen profile to the definitive lumen segmentation can be carried out 1) by considering all the A-scan lines together, 2) consequently generating a ID profile from all the points, 3) consequently filtering this profile using a ID median filter with a very small kernel and consequently 4) fitting a smoothing B-spline through all the points. The stent strut apposition and coverage is generated by calculation of the distance from the external surface of the strut to the vessel wall, more particular the perpendicular distance from the center of the stent to the lumen by computing said distances to obtain strut apposition and coverage, whereby the stent strut apposition represents how far a stent strut is removed from the inner layer of the vessel wall and stent strut coverage represents the thickness of the tissue overlaying the strut surface.

A threshold can be based on strut real thickness or a strut real thickness and its coating thickness to discriminate between apposed and malapposed struts. Or for example, a threshold is based on strut real thickness or a strut real thickness and its coating thickness to discriminate between uncovered, covered struts and hyperplasia. Generally in any of the methods of the present invention the body vessel can be a blood vessel, or the body vessel can be any one of an urinary tract or a bile duct.

The multiple surfaces can be different external or exposed strut zones.

The implant device can be a stent, for example the stent can be an intracoronary stenting Methods of the present invention can be used for follow up of stent behavior after stent implant, for instance intracoronay implant, and/or for estimating the risk of for instance thrombosis or restenosis after such implantation.

An advantage of the present invention is that it can provide the physician with information to evaluate the success of implantation of a transluminal implant for instance to asses malapposition, thrombus or restenosis and/or to assess reocclusions or obstructions after angioplasty for instance to treat a problem of neointimal hyperplasia, and/or to estimate the need to redilate or to evaluate the incidence and/or severity of post-operative elastic recoil restenosis after percutaneous endovascular therapy by stent placement.

The present invention also provides software such as an operating system for operating any of the methods of the embodiments described above. The software or operating system may comprise an automated operator for automatic stent and lumen analysis system identifying and measuring luminal area, both stent apposition and neointima stent coverage on in-vivo acquired OCT images. The software or operating system can be adapted for automatic contouring and identifying of the structures of stent or tissue environment.

The software or operating system can be adpted to measure neointima coverage and stent apposition.

The software or operating system can be adapted to provide such stent strut to vessel wall distance.

The software or operating system can be adapted to provide neointima coverage for different individual stent struts zones in the image.

The software or operating system can be adapted to measure coverage and stent apposition individual analysis of multiple stent strut visualization in an image. The software or operating system can be adapted for determining the presence or absence of disorder, the seriousness of disorder or the progress of disorder in the patient of any of the previous embodiments.

The software or operating system can be adapted so that it also controls usage of the apparatus.

The software or operating system can be adapted so that the operating system includes a user interface to enable the user to interact with the functionality of the computer. The software or operating system can be adapted so that the operating system includes a graphical user interface whereby the software or operating system can be adapted to control the ability to generate graphics on the computer's display device that can be displayed in a variety of manners representative for or associated with the condition of a stent placement malfunction or disorder in a selected patient or a group of patients to allow a user to distinguish between the absence of malfunction or disorder, the seriousness of malfunction or disorder or the progress of malfunction or disorder in identified patients or patient groups. Such software can be provided as a computer-executable code, e.g. stored on a computer-readable medium, the computer executable code being adapted, so that it can run on a computer system, e.g. to run the operating system of any of the above embodiments or to execute the method described in any of the embodiments above and to direct a processing means to produce output signals that are representative for a condition of disorder or a modifying condition of disorder.

Further scope of applicability of the present invention will become apparent from the detailed description given hereafter. However, it should be understood that the detailed description and specific examples, while indicating preferred embodiments of the invention, are given by way of illustration only, since various changes and modifications within the spirit and scope of the invention will become apparent to those skilled in the art from this detailed description. It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will become more fully understood from the detailed description given below and the accompanying drawings which are given by way of illustration only, and thus are not limitative of the present invention:

FIG. la provides a graphic display that demonstrates segmented A-scan lines containing just lumen. FIG. lb provides a graphic display that demonstrates segmented A-scan lines of a uncovered strut.

FIG. lc provides a graphic display that demonstrates segmented A-scan lines of a covered stent (the tissue over the stent is clearly visible from the profile).

FIG. 2a provides an image that shows candidate A-lines.

FIG. 2b provides an image that shows candidate A-lines after low intensity and close shadow processing.

FIG. 2c provides an image that shows candidate A-lines after medium and far shadow processing.

FIG. 2d provides an image that shows the final strut positive A-lines after correction.

FIG. 2e provides a graphic display of the derived maximum intensity profile and threshold.

FIG. 2f provides a graphic display of the derived amount of pixels having a value over the maximum profile and threshold.

FIG. 2g provides a graphic display of the derived shadow profile and relative threshold. FIG. 3a provides an image (raw-data) to analyse for the lumen segmentation.

FIG. 3b provides an image after preprocessing.

FIG. 3c provides an image after final lumen segmentation.

FIG. 4a provides an image that illustrates the scan conversion of images (and relative segmentation) for an image from follow-up pullbacks (coverage).

FIG. 4b provides an image that illustrates the scan conversion of images (and relative segmentation) for an image from a baseline pullback (fresh implanted stent).

FIG. 5a: provides an image that demonstrates coverage analysis.

FIG. 5b provides an image that is used for apposition analysis.

FIG. 6a is a graphic display of the correlation between the stent apposition algorithms against MD n. l.

FIG. 6b is a graphic display of a Bland-Altman plot of the stent apposition algorithm against MD n. l.

FIG. 6c is a graphic display of the correlation between the stent coverage algorithms against MD n. l

FIG. 6d is a graphic display of a Bland-Altman plot stent coverage algorithm against MD n. l.

FIG. 7 is an example of an algorithm for segmentation. DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

In the following detailed description of the preferred embodiments, reference is made to the accompanying drawings, which form a part hereof, and within which are shown by way of illustration specific embodiments by which the invention may be practised. The drawings described are only schematic and are non-limiting. In the drawings, the size of some of the elements may be exaggerated and not drawn on scale for illustrative purposes. Those skilled in the art will recognize that other embodiments may be utilized and structural changes may be made without departing from the scope of the invention.

Furthermore, the terms first, second, third and the like in the description and in the claims, are used for distinguishing between similar elements and not necessarily for describing a sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances and that the embodiments of the invention described herein are capable of operation in other sequences than described or illustrated herein.

It is to be noticed that the term "comprising", used in the claims, should not be interpreted as being restricted to the means listed thereafter; it does not exclude other elements or steps. Thus, the scope of the expression "a device comprising means A and B" should not be limited to devices consisting only of components A and B. It means that with respect to the present invention, the only relevant components of the device are A and B. Definitions

Optical coherence tomography (OCT) is an optical signal acquisition and processing method. It captures micrometer-resolution, three-dimensional images from within optical scattering media (e.g., biological tissue). Optical coherence tomography is an interferometric technique, typically employing near-infrared light. The use of relatively long wavelength light allows it to penetrate into the scattering medium. Confocal microscopy, another similar technique, typically penetrates less deeply into the sample.

This reflectivity profile, called an A-scan, contains information about the spatial dimensions and location of structures within the item of interest. A cross-sectional tomography (B-scan) may be achieved by laterally combining a series of these axial depth scans (A-scan). En face imaging (C-scan) at an acquired depth is possible depending on the imaging engine used. "Rapid rise and fall" of energy corresponds for example to an adluminal edge of a stent strut. The rapid rise and fall of energy can be defined as the speed at which the signal consecutively increases and decreases its energy as a function of depth. Spatially encoded frequency domain OCT or spectral domain or Fourier domain OCT, SEFD-OCT, extracts spectral information by distributing different optical frequencies onto a detector stripe (e.g. line-array CCD or CMOS) via a dispersive element. Thereby the information of the full depth scan can be acquired within a single exposure. For example, using SEFD-OCT images, the distance between stent and tissue can be evaluated.

A stent is a generally longitudinal tubular device formed of biocompatible material, preferably a metallic or plastic material. Stents are useful in the treatment of stenosis, strictures or aneurysms in body vessels, such as blood vessels. The device is implanted either as a "permanent stent" within the vessel to reinforce collapsing, partially occluded, weakened or abnormally dilated sections of the vessel or as a "temporary stent" for providing therapeutic treatment to the diseased vessel. Stents are typically employed after angioplasty of a blood vessel to prevent restenosis of the diseased vessel. Stents may be useful in other body vessels, such as the urinary tract and the bile duct.

A typical intracoronary stent includes an open flexible configuration. The stent configuration allows the stent to be configured in a radially compressed state for transluminal catheter insertion into an appropriate site. Once properly positioned within the lumen of a damaged vessel, the stent is radially expanded to support and reinforce the vessel. Radial expansion of the stent may be accomplished by an inflatable balloon attached to the catheter, or the stent may be of the self-expanding type so that it will radially expand once deployed. An example of a suitable stent is disclosed in U.S. Pat. No. 4,733,665, which is incorporated herein by reference in its entirety.

Conventionally, the stent properties of flexibility in the unexpanded state and radial rigidity in the expanded state have been achieved using one set of interconnected struts (typically the longitudinal struts) to confer flexibility to the unexpanded stent and another pair of interconnected struts (typically non-longitudinal circumferential rings of struts) which open up to radially rigid hoop structures (in the ideal case) to confer radial rigidity to the expanded stent. Stents find various uses in medical procedures. For instance, stents are widely used in angioplasty. Angioplasty involves insertion of a balloon-tipped catheter into an artery at the site of a partially obstructive atherosclerotic lesion. Inflation of the balloon can rupture the intima and media, dramatically dilating the vessel and relieving the obstruction. About 20 to 30% of obstructions reocclude in a few days or weeks, but most can be redilated successfully. Use of stents significantly reduces the reocclusion rate. Angioplasty is an alternative to bypass surgery in a patient with suitable anatomic lesions. The risk is comparable with that of surgery. Mortality is 1 to 3%; myocardial infarction rate is 3 to 5%; emergency bypass for intimal dissection with recurrent obstruction is required in <3%; and the initial success rate is 85 to 93% in experienced hands.

Stents are also used in percutaneous endovascular therapy. Many new treatments for vascular disease (occlusions and aneurysms) avoid open surgery. These treatments may be performed by interventional radiologists, vascular surgeons, or cardiologists. The primary approach is percutaneous translumninal angioplasty (PTA), whereby a small high-pressure balloon is used to open an obstructed vessel. However, because of the high recurrence rate of obstruction, alternative methods may be necessary. A stent, such as a metallic mesh-like tube, is generally inserted into a vessel at an obstructed site. As stents can be very strong, they tend to keep vessels open much better than balloons alone. Moreover, the recurrence rate of obstruction is reportedly lower when stents are used. Stents work well in larger arteries with high flow, such as iliac and renal vessels. They work less well in smaller arteries, and in vessels in which the occlusions are long. Stents for carotid disease are being studied. There are at least two known causes of post-operative restenosis— elastic recoil, wherein the vessel contracts due to the natural elasticity of the vessel wall, and neointimal hyperplasia, wherein medial cells proliferate in response to immune system triggers. Stents have proven useful in reducing the incidence and/or severity of post-operative elastic recoil restenosis, as they resist the tendency of blood vessels to restenose after removal of the balloon. Stents have proven less useful for treatment of neointimal hyperplasia, which arises out of a complex immune response to expanding and fracturing the atherosclerotic plaque. In the case of neointimal hyperplasia, the initial expansion and fracture of the atherosclerotic lesion initiates inflammation, which gives rise to a complex cascade of cellular events that activates the immune system, which in turn gives rise to the release of cytokines that stimulate cell multiplication in the smooth muscle layers of the vessel media. This cell stimulation eventually causes the vessel to restenose. Various approaches to the problem of neointimal hyperplasia have been attempted. Among these approaches are: subsequent stent placement, debulking, repeat angioplasty, and laser treatment. Another recent approach has been to coat the stent with an immunosuppressant or a chemotherapeutic drug. Immunosuppressant drugs, such as rapamycin, target cells in the Gl phase, preventing initiation of DNA synthesis. Chemotherapeutic drugs, such as paclitaxel (Taxol— Bristol-Myers Squibb) and other taxane derivatives, act on cells in the M phase, by preventing deconstruction of microtubules, thereby interrupting cell division. While these approaches present some promise, they also suffer certain limitations, such as the tendency for rapamycin and taxanes to quickly disperse from the stent site, thereby both limiting the drugs' effective duration in proximity to the stent and also risking undesirable systemic toxic effects.

There is a need for tools based on algorithms that would allow for an objective and fast automated analysis of in-vivo acquired OCT images that provides an accurate analysis for both stent coverage and apposition and preferably provides such stent strut to vessel wall distance (and neointima coverage) for different individual stent strut zones or different zones in a set of interconnected struts (typically the longitudinal struts) in the image. Such would be extremely valuable for the follow-up of stent behavior after stent implantation, for instance intracoronary implantation and for the prediction of the risk of stent thrombosis or restenosis after such implantation. For the physician, it is very important to obtain accurate information on the success of implantation (e.g. assessment of malapposition, thrombus or neointimal coverage) to guide the interventisionalist in further decision making.

Present invention provides such solution. A software made in Matlab and C++ (Visual), was used to develop the program for automatic assessment of stent strut apposition and coverage in the present invention. In-vivo OCT images raw data were recorded as rectangular images (504 x 976 pixels) containing A-scan lines. Images were recorded at baseline (freshly implanted stent) and at variable time points thereafter (showing a variable degree of tissue growth over the stent surface). The following approach mainly comprised or consisted of three steps: (1) segmentation of stent and lumen based on A- scan analysis (Matlab), (2) scan conversion of the image (C++ for speed), (3) evaluation of strut apposition or strut coverage from A-line segmentation (A-scan lines) in the scan-converted image (Matlab). In an embodiment, the program can be executed using a graphical interface for ease of use.

Segmentation of the A-scan lines is illustrated as examples in Figs, la-lc. Fig. la provides a graphic display that demonstrates A-scan lines containing just lumen. Fig. lb provides a graphic display that demonstrates A-scan lines of an uncovered strut. Finally, Fig. lc provides a graphic display that illustrates A-scan lines of a covered stent (the tissue over the stent is clearly visible from the profile). High intensity value, rapid rise and fall on energy and presence of shadow can be easily seen on the profile on Figs, lb and lc. Please note the different scale y- value on Figs, la-lc.

Stent segmentation of the A-scan lines is illustrated in Figs. 2a-2d. Fig. 2a provides an image that shows candidates A-lines. Fig. 2b illustrates candidates A-lines after low intensity and close shadow processing using the derived maximum intensity profile and threshold as illustrated in Fig. 2e. Fig. 2c provides an image comprising candidate A- lines after medium and far shadow processing using the derived amount of pixels having a value over the maximum profile and threshold as illustrated in Fig. 2f and the derived shadow profile and relative threshold as illustrated in Fig. 2g . Fig. 2d shows the final derived positive A-lines of a strut after correction.

Lumen segmentation of the A-scan lines is illustrated in Figs. 3a-3c. Fig. 3a provides an image comprising the raw-data of an image which has to be analysed. Fig. 3b provides an image after preprocessing. Fig. 3c shows the final lumen segmentation. The points 2 representing the result of single A-scan lines analysis are illustrated in Fig. 3c, as are the final results 1 which are additionally fitted using a smoothing spline filtering profile as illustrated in Fig. 3d. Scan conversion of the image data is illustrated in Figs. 4a and 4b. The latter depict an image that provides an example of scan conversion of images and relative segmentation. The latter is done for and image from a follow-up pullbacks (coverage) in Fig. 4a. And in Fig. 4b an image from a baseline pullback (fresh implanted stent) is used and converted.

Stent strut apposition and coverage analysis is illustrated in Fig. 5a and 5b. Fig. 5a provides an image that demonstrates coverage analysis: for every strut present in the image an estimation of the thickness of coverage is given with a distance. Moreover even a small strut 3 is correctly taken in account by the algorithm. Fig. 5b provides an image which is used for apposition analysis: for every strut present in the image an estimation of the distance from the external surface of the strut to the lumen reconstruction is given. Moreover even the presence of blood over the strut 4 is correctly managed by the algorithm.

Figs. 6a-6d concern the stent strut apposition algorithm validation and the stent strut coverage algorithm validation. Fig. 6a is a graphic display of the correlation between the stent apposition algorithm against MD n.l . The correlation between the stent coverage algorithm against MD n. l is illustrated in Fig. 6c. Fig. 6b illustrates the Bland- Altman plot of the stent apposition algorithm against MD n. l., whereas Fig. 6d illustrates the Bland- Altman plot of the stent coverage algorithm against MD n.l.

EXAMPLES

1, Example 1: Stent strut segmentation

Stent struts segmentation is based on A- scan lines analysis. A strut in OCT images is characterized by: (1) a bright reflection, (2) a shadow and (3) a rapid rise and fall of energy.

The segmentation consists of three steps: (1) preprocessing of the image, (2) identification of candidate A-scan lines, (3) processing of the candidates.

a. Preprocessing of the image

Once the image has been calibrated, the calibration line must coincide with the catheter plastic border. Using the calibration line as a reference, the catheter can be automatically recognized and ignored from the algorithm for strut segmentation (and lumen segmentation later).

As can line candidates

Every A-scan line is analyzed searching for strut characteristics. The maximum intensity value of each line is located and recorded. If this value exceeds a very high threshold the line is labeled as a candidate. The second characteristic (the rapid rise and fall of the energy) is analysed in this way: the number of pixels that exceeds 3/5 of the maximum is taken into account. If this number is lower than a fixed value, the line is labeled as a candidate. In order to look for the last characteristic (shadow), each A-line is first processed identifying the shallowest object or, in the case of images presenting coverage, the most important peak of intensity. Then the average intensity of the pixels after this object, or peak, is computed. Also in the case if the average is inferior to a fixed value, the A-line is labeled as a candidate.

A-scan line strut positive

An A-line is recorded as a candidate if it presents any of the fore- mentioned characteristics (or conditions). In this way, all lines really containing a strut are recorded as candidates, but also many other lines might be classified as candidates. To remove the non-desired lines, the characteristics of the shadow must be analysed. In-vivo images quite often present a shadow that is not very well defined (in 10-20% of the cases, the shadow can contain strong noise), but the shadow is always present. A necessary characteristic of a shadow coming from a strut, is that it is there through the whole depth of the image. In this way, taking into account the depth of the shadow allows for exclusion of false candidates (e.g. because of thrombus, plaque protrusion, blood remnants or other "objects" inside the lumen).

In order to do that, the intensity level of all the pixels beyond the strut is analyzed. Pixels cannot be analyzed singularly because of the presence of noise along the shadow; they are divided into three groups: (1) close to the struts, (2) medium distance and (3) far away. The intensity of each group is analyzed and if it exceeds a fixed value, the candidate is discarded. Finally, horizontal coordinates of the located struts are identified. The algorithm takes as a starting point the coordinate founded in step b. 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. At the end of this last step, almost all stent struts are correctly segmented avoiding false positive results.

The algorithm can be successfully run in both of the possible situations: baseline pullbacks (freshly implanted stents) and follow-up pullbacks (stent presenting neointima coverage). The main difference is how horizontal coordinates of struts are located (see step b). Moreover, depending on the kind of pullback (baseline or follow-up) a different set of optimal parameters must be selected (see validation). The complexity of the algorithm (for the entire stent strut segmentation) is O(n). Real time of this algorithms running under Matlab 7.9.0 is in the order of 10^"1 seconds.

* Example 2: Lumen segmentation

Also lumen segmentation is based on A-scan line analysis. It consists of three steps: (1) preprocessing of the image, (2) A-line single analysis and (3) correction considering all A-lines together. a. Preprocessing

For lumen segmentation, the preprocessing plays a key role. First, all the strut positive A-scan lines are removed from the image (this happens only for the baseline pullback). Then, a three- step iterative strategy is applied.

(1) Based on the histogram of the whole image, all the pixels under a certain intensity value are turned to zero and all the pixels above another fixed value are incremented to a higher intensity. (2) A Gaussian symmetric low-pass filter with a 5 pixel square dimension and sigma equal to 2.5 is applied. (3) The image is morphologically opened using a disk structuring element. In this way, everything that is not vessel wall (i.e. piece of stent struts still present after strut a-scan positive removal, residual of blood, lumen protrusion ...) is correctly removed from the lumen. b. A-line analysis

After the preprocessing, the image is ready to be analysed to obtain the initial lumen segmentation. Every line is analysed locating the shallowest pixel with an intensity major than 2/3 of the maximum intensity value of the line. Now, from the located points, the code moves back in the direction of the lumen (for a predefined maximum number of steps) stopping when finding the first pixel with an intensity value inferior to 2/7 of the maximum. c. Correction

At the end of the previous step, a rough lumen profile is obtained. To find the definitive lumen segmentation and correct some possible errors, all the A-scan lines must be considered together. First, from all the points located in the last step, a ID profile is obtained. Then this profile is filtered using a ID median filter with a very small kernel. Then, a smoothing B-spline is fitted through all the points. In this way, is possible to find an approximation of the vessel hiding under the shadows; this is a crucial point for the later estimation of stent strut apposition. The obtained spline represents the correct lumen segmentation.

This embodiment has revealed itself particularly efficient not only in standard cases but also in difficult situations when a lot of noise (blood) and non desired objects (protrusion, thrombus ...) are present. For baseline and follow-up pullbacks, the strategy is the same with the following two exceptions: (1) optimal parameters values, (2) the step of strut positive A-lines removal is skipped for images coming from follow-up pullbacks (the tissue covering the stent strut must not be removed). As soon the segmentation is obtained also the lumen area is easily available. The complexity of the algorithm (entire lumen segmentation from step a to c) is O(n). Real time running under Matlab 7.9.0 is in the order of

10^"1 seconds.

3. Example 3: Scan Conversion

Intracoronary OCT images are recorded as raw data. Raw data correspond to a rectangular image with dimensions of 504 x 976 pixels (FD-OCT). In order to display a round image with segmentation and to proceed with the automatic assessment of strut apposition and coverage, a scan conversion is needed. The scan conversion proposed is based on bilinear interpolation. For reason of speed (the goal is to create images with dimensions of at least 1000 x 1000 pixels), the algorithm for scan-conversion has been implemented using language C++ and IT++ libraries. Through MEX function was made available for the Matlab user interface. The complexity of the following algorithm is 0(n ) and real time running it through Matlab 7.9.0 (using MEX Libraries) is 1.7 seconds (average).

4. Example 4: Stent strut apposition and coverage

Assessment of stent strut apposition and coverage means calculation of the distance from the external surface of the strut to the vessel wall. Stent strut apposition represents how far a stent strut is removed from the inner layer of the vessel wall. Stent strut coverage represents the thickness of the tissue overlaying the strut surface. When the catheter is not positioned in the center of the vessel

{eccentricity), the OCT image suffers from a very common problem: sunflower artifact (Bezerra et al, "Intracoronary Optical Coherence Tomography: A Comprehensive Review', in JACC: Cardiovascular Intervention (2009)). For this reason, the correct distance to evaluate is the distance perpendicular to the lumen. In order to assess strut apposition and coverage, an algorithm evaluating the perpendicular distance from the center of the stent to the lumen has been developed. If more than one distance matching these criteria exists, the minimum distance is taken into account.

As soon these distances are computed, strut apposition and coverage are obtained. Putting a threshold based on strut real thickness and polymer thickness (for drug eluting stent) (Bezerra et al 2009, Terashima et al "Accuracy and Reproducibility of Stent-Strut Thickness Determined by Optical Coherence Spectroscopy" in The Journal of Invasive Cardiology (2009)) it is possible to discriminate between apposed and malapposed struts. In the same way is possible to discriminate between uncovered, covered strut and hyperplasia (Garcia-Garcia et al "Virtual

Histology and optical coherence tomography: from research to a broad clinical application" in Heart (2010), Tearney et al "Three dimensional coronary artery microscopy by intra coronary optical coherence tomography" in European hearth journal (2008), Bezerra et al 2009). 5, Example 5: Validation

To validate the software proposed, the results of the algorithm are compared with the golden standard available. The golden standard at this moment is manual assessment performed by trained expert medical doctors (Bezerra et al 2009). Distances can be measured manually by MDs using an Offline Review Workstation (LightLab Imaging). Validation consists in comparison of individual distances manual against automatic. In our study, 108 images randomly taken from 9 different pullback were used for validation.

Correlation and Bland-Altman are the methods used for validation. Every single measure obtained from automatic analysis has been compared to both manual assessments. For correlation Pearson's correlation index has been used. 6, Example 6: Validations

• Stent strut apposition algorithm validation

According to the medical doctor who performs the manual analysis, 492 stent struts are present. Algorithm presents a correlation of 0.96. The algorithm is able to locate all the struts with these exceptions: 6 struts are missed (1.2 %) and 3 false positive (0.6%), see Fig 6A.

Bland Altman plot does not present significant bias and very good interval of confidence (Fig. 6B)

• Stent strut coverage algorithm validation

According to the medical doctor who performs the manual analysis, 490 stent strut are present. Algorithm presents a correlation of 0.97. The algorithm is able to locate all the struts with these exceptions: 14 struts are missed (2.8 %) and 3 false positive (0.6%), see Fig. 6C.

Bland Altman plot does not present significant bias and very good interval of confidence (Fig. 6D)

The methods and systems described above according to embodiments of the present invention may be implemented in a processing that may include at least one customisable or programmable processor coupled to a memory subsystem that includes at least one form of memory, e.g., RAM, ROM, and so forth. It is to be noted that the processor or processors may be a general purpose, or a special purpose processor, and may be for inclusion in a device, e.g., a chip that has other components that perform other functions. Thus, one or more aspects of the method according to embodiments of the present invention can be implemented in digital electronic circuitry, or in computer hardware, firmware, software, or in combinations of them. The processing system may include a storage subsystem that has at least one disk drive and/or CD-ROM drive and/or DVD drive. In some implementations a user interface subsystem is preferably provided for a user to manually input information or adjust the operation. More elements such as network connections, interfaces to various devices, and so forth, may be included in some embodiments. In particular an interface is provided to receive the in vivo images captured images. The various elements of the processing system may be coupled in various ways, including via a bus subsystem, e.g. for simplicity a single bus, but will be understood to those in the art to include a system of at least one bus. The memory of the memory subsystem may at some time hold part or all of a set of instructions that when executed on the processing system implement the steps of the method embodiments described herein.

The present invention also includes a computer program product which provides the functionality of any of the methods according to embodiments of the present invention when executed on a computing device. Such computer program product can be tangibly embodied non-transiently in a carrier medium carrying machine -readable code for execution by a programmable processor. The present invention thus relates to a carrier medium carrying a computer program product that, when executed on computing means, provides instructions for executing any of the methods as described above. The term "carrier medium" refers to any medium that participates in providing instructions to a processor for execution. Such a medium may take many forms, including but not limited to, non-volatile media, and transmission media. Non-volatile media includes, for example, optical or magnetic disks, such as a storage device which is part of mass storage. Common forms of computer readable media include, a CD-ROM, a DVD, a flexible disk or floppy disk, a tape, a memory chip or cartridge or any other medium from which a computer can read. Various forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to a processor for execution. The computer program product can also be transmitted via a carrier wave in a network, such as a LAN, a WAN or the Internet. Transmission media can take the form of acoustic or light waves, such as those generated during radio wave and infrared data communications. Transmission media include coaxial cables, copper wire and fibre optics, including the wires that comprise a bus within a computer.

Accordingly, the present invention also includes a software product which when executed on a suitable computing device carries out any of the methods of the present invention. Suitable software can be obtained by programming in a suitable language such as C++ and compiling on a suitable compiler for the target computer processor. Target computer processor can be (for example but not limited to): the general purpose processor (CPU) in a computer system, a graphical processor (such as a GPU) of a computer system, a general purpose processor present in a display system, a graphical processor (such as a GPU) present in a display system, an embedded processor present in a display system, a processor present in a panel such as a LCD panel or OLED panel or plasma panel, a processor present in the driver system of a liquid crystal display panel.

An example of software for segmentation is given in Fig. 7.

Claims

[CLAIMS]

1. A computer based method for analysing the behaviour of a transluminal implant, for instance of an intracoronay 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,

wherein said method provides automatic identificationand measurement of both 1) distance of the separate surfaces zones of said implant device to vessel wall (apposition) and 2) neointima coverage (corresponding to vessel healing after implantation of the implant device).

2. The method of claim 1, whereby the segmentation comprises or consists of three steps: (1) preprocessing of the image, (2) identification of candidate A-scan lines, (3) processing of the candidates.

3. The method of any of the claims 1 or 2, whereby multiple zone or structure of the implant are individually analysed for providing such stent strut to vessel wall distance (and neointima coverage) for different individual stent struts zones in the image.

4. The method of any of the claims 1 to 3, whereby the multiple zones or structures are multiple views or segments of a strut.

5. The method of any of the claims 1 to 4, whereby the multiple zones or structures are multiple images of transversal sections of a strut of the longitudinal tubular implant

6. The method of any of the claims 1 to 5, whereby the multiple surfaces are formed by at least one axial strut.

7. The method of claim 6, whereby the 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.

8. The method of claim 7, whereby the segmentation comprises or consists of three steps: (a) preprocessing of the image, (b) identification of candidate A-scan lines, (c) processing of the candidates.

9. The method of claim 8, whereby the preprocessing of images involves 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).

10. The method of any of claims 7 and 9, whereby 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 labeled as a candidate.

11. The method of claim 10, whereby the A-scan line candidates are processed by removing the non-desired lines by analyzing the shadows coming from a strut and taking into account the depth of the shadow excluding of false candidates (i.e. false candidates because of thrombus, plaque protrusion, blood remnants or other "object" inside the lumen), whereby the intensity level of all the pixels beyond the strut is analyzed and the pixels dividedinto three groups: (1) close to the struts, (2) medium distance and (3) far away whereby the intensity of each group is analyzed and if it exceeds a certain fixed value, the candidate is discarded.

12. The method of any of the claims 8 to 11, whereby 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.

13. The method of any previous claim, whereby the lumen segmentation is based on A- scan line analysis consisting of consists of three steps: (1) preprocessing of the image, (2) A-line single analysis and (3) correction considering all A-lines together.

14. The method of claim 13, whereby the lumen segmentation comprises or consists of three steps: (a) preprocessing of the image, (b) identification of candidate A-scan lines, (c) correction .

15. The method of claim 14 , whereby the preprocessing 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.

16. The method of any of the claims 14 or 15, whereby the candidate A-scan lines identification comprises analysing every line locating the shallowest pixel with an intensity greater than 2/3 of the maximum intensity value of the line and from the located points, moving the code s back in the direction of the lumen for a predefined maximum number of steps stopping when finding the first pixel with an intensity value inferior to 2/7 of the maximum to generate a rough lumen profile.

17. The method of claim 16 , whereby the correction of the rough lumen profile to the definitive lumen segmentation is carried out 1) by considering all the A-scan lines together, 2) consequently generating a ID profile from all the points, 3) consequently filtering this profile using a ID median filter with a very small kernel and consequently 4) fitting a smoothing B-spline through all the points.

18. The method of any of the previous claims 1 to 17, whereby the stent strut apposition and coverage generated by calculation of the distance from the external surface of the strut to the vessel wall, more particular the perpendicular distance from the center of the stent to the lumen comprises computing said distances to obtain strut apposition and coverage, whereby the stent strut apposition represents how far a stent strut is removed from the inner layer of the vessel wall and s tent strut coverage represents the thickness of the tissue overlaying the strut surface.

19. The method of claim 18, whereby a threshold is put based on strut real thickness or a strut real thickness and its coating thickness to discriminate between apposed and malapposed struts.

20. The method of claim 18, whereby a threshold is put based on strut real thickness or a strut real thickness and its coating thickness to discriminate between uncovered, covered strut and hyperplasia.

21. The method of any of the claims 1 to 20, whereby the body vessel is a blood vessel.

22. The method of any of the claims 1 to 20, whereby the body vessel is any one of an urinary tract or a bile duct.

23. The method of any of the claims 1 to 22, whereby the multiple surfaces are different external or exposed strut zones.

24. The method of any of the claims 1 to 23, whereby the implant device is a stent.

25. The method of claim 24, whereby the stent is a intracoronary stenting

26. The method of any of the claims 1 to 25, used for follow up of stent behaviour after stent implant, for instance intracoronay implant.

27. The method of any of the claims 1 to 25, used for estimating the risk of for instance thrombosis or restenosis after such implantation.

28. The method of any of the claims 1 to 25, used to provide the physician with information to evaluate the success of implantation of a transluminal implant for instance to asses malapposition, thrombus or restenosis.

29. The method of any of the claims 1 to 25, used to assess reoccluding of obstructions after angioplasty for instance to treat a problem of neointimal hyperplasia.

30. The method of any of the claims 1 to 25, used to estimate the need to redilate or to evaluate the incidence and/or severity of post-operative elastic recoil restenosis after percutaneous endovascular therapy by stent placement.

31. An operating system for operating the methods of any of the previous claims 1 to 30, which operating system comprises an automated operator for automatic stent and lumen analysis system identifying and measuring luminal area, both stent apposition and neointima stent coverage on in-vivo acquired OCT images

32. The operating system of claim 31, for automatic contouring and identifying of the structures of stent or tissue environment.

33. The operating system of claim 31 to measure neointima coverage and stent apposition

34. The operating system of any of the previous claims 31 to 33, which provides such stent strut to vessel wall distance

35. The operating system of any of the previous claims 31 to 33, which provides neointima coverage for different individual stent struts zones in the image.

36. The operating system of any of the previous claims 31 to 35, to measure coverage and stent apposition individual analysis of multiple stent strut visualization in an image. ,

37 '. The operating system of any of the previous claims 31 to 36, for determining the presence or absence of disorder, the seriousness of disorder or the progress of disorder in the patient of any of the previous claims.

38. The operating system of any of the previous claims 31 to 37, whereby the operating system also controls usage of the apparatus.

39. The operating system of any of the previous claims 31 to 38, whereby the operating system includes a user interface that to enable the user to interact with the functionality of the computer.

40. The operating system of any of the previous claims 31 to 39, whereby the operating system includes a graphical user interface whereby the operating system controls the ability to generate graphics on the computer's display device that can be displayed in a variety of manners representative for or associated with the condition of a stent placement malfunction or disorder in a selected patient or a group of patients to allow a user to distinguish between the absence of malfunction or disorder, the seriousness of malfunction or disorder or the progress of malfunction or disorder in identified patients or patient groups.

41. A computer-executable code, stored in a computer-readable medium, the computer executable code adapted, when running on a computer system to run the operating system of any of the claims 31 to 40 or to execute the method described in any of the claims 1 to 30, and to direct a processing means to produce out put signals that are representative for a condition of disorder or a modifying condition of disorder.

42. An optical signal acquisition and processing apparatus with a light source to generate a light that can penetrate into a scattering medium for generating micrometer- resolution three-dimensional images of long vessel segments of a transluminal implant device with multiple separate surface zones or of a longitudinal tubular implant device with multiple surfaces zones within a body vessel, the apparatus further comprising a processor for receiving and processing signals from said its detector and for:

- converting the scan of the image to a fast analysis platform (e.g. C++),

43. The optical signal acquisition and processing apparatus of claim 42, whereby the processor is programmed for automated identifying and measuring beside both implant coverage and apposition is also programmed for automated identifying and measuring of the luminal area.

44. The optical signal acquisition and processing apparatus of any of the previous claims 42 and 43, whereby the multiple separate surfaces zones are different strut sections, in particular of axially aligned stent struts or of a set of interconnected struts (typically the longitudinal struts).

45. The optical signal acquisition and processing apparatus of any of the previous claims 42 to 44, whereby the distance of the separate surfaces zones of said implant device to vessel wall concerns the stent strut to vessel wall distance of said implant device.

46. The optical signal acquisition and processing apparatus of any of the previous claims 42 to 45, whereby the processor is programmed is to provide stent strut to vessel wall distance for different individual stent struts zones in the image.

47. The optical signal acquisition and processing apparatus apparatus of any of the previous claims 42 to 46, whereby the processor is programmed is to provide neointima coverage for different individual stent struts zones in the image.

48. The optical signal acquisition and processing apparatus of any of the previous claims 42 to 47, whereby said apparatus is an optical coherence tomography (OCT) apparatus.

49. The optical signal acquisition and processing apparatus of any of the previous claims 42 to 47, whereby said apparatus is a spectral domain or Fourier domain OCT

(SEFD-OCT) apparatus.

50. The optical signal acquisition and processing apparatus of any of the previous claims 42 to 49, which comprises a storage means to store the processes signal electronically.

51. The optical signal acquisition and processing apparatus of any of the previous claims 42 to 50, which a display means to display the coverage, apposition and or luminal area for the stent or for individual stent struts zones.

52. The optical signal acquisition and processing apparatus of any of the previous claims 42 to 51, whereby the apparatus comprises a near- infrared light source

53. The optical signal acquisition and processing apparatus of any of the previous claims 42 to 52, whereby the apparatus comprises a long wavelength light source

54. Use of an apparatus of any of the claims 42 to 53, to analyse images corresponding to a multiple parameters of implant disorder or of implant failure parameters such as malapposition, thrombus, restenosis.

55. Use of the apparatus of any of the claims 42 to 53, to follow up of stent behaviour after stent implant, for instance intracoronay implant.

56. Use of the apparatus of any of the claims 42 to 53, for estimating the risk of for instance thrombosis or restenosis after such implantation.

57. Use of the apparatus of any of the claims 42 to 53, to provides the physician with information to evaluate the success of implantation of a transluminal implant for instance to asses malapposition, thrombus or restenosis

58. Use of the apparatus of any of the claims 42 to 53, to assess reoccluding orobstructions after angioplasty for instance to treat a problem of neointimal hyperplasia

59. Use of the apparatus of any of the claims 42 to 53, to estimate the need to redilate or to evaluate the incidence and/or severity of post-operative elastic recoil restenosis after percutaneous endovascular therapy by stent placement

60. Use of the apparatus of any of the claims 42 to 53, for in-vivo images analysis to measure coverage and stent apposition individual analysis of multiple stent strut visualization in an image.

61. The use of the apparatus any of the claims 42 to 53, whereby the images to be analyses are recorded at baseline (freshly implanted device) and at any time point thereafter.

62. The use of the apparatus any of the claims 42 to 53, for discovering a medicament to prevent reoccluding or obstructions after angioplasty

63. The apparatus of any of the previous claims 42 to 53, for use in a diagnostic medical treatment of a subject to diagnose for a transluminal implant body reaction, in particular stent body reaction.