NL2023477B1 - Method of obtaining vessel wall parameters in a 3D model of a cardiovascular system - Google Patents

Method of obtaining vessel wall parameters in a 3D model of a cardiovascular system Download PDF

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NL2023477B1
NL2023477B1 NL2023477A NL2023477A NL2023477B1 NL 2023477 B1 NL2023477 B1 NL 2023477B1 NL 2023477 A NL2023477 A NL 2023477A NL 2023477 A NL2023477 A NL 2023477A NL 2023477 B1 NL2023477 B1 NL 2023477B1
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vessel wall
image data
identifying
curve
points
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NL2023477A
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Dutch (nl)
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Hendrik Kitslaar Pieter
Hendrikus Christiaan Reiber Johan
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Medis Medical Imaging Systems B V
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/149—Segmentation; Edge detection involving deformable models, e.g. active contour models
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
    • 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
    • G06T2207/20112—Image segmentation details
    • G06T2207/20168—Radial search
    • 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
    • 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
    • G06T2207/30172—Centreline of tubular or elongated structure

Abstract

A method is provided of obtaining vessel wall parameters in a 3D model of a cardiovascular system, comprising the steps of obtaining 3D image data of a body volume comprising a coronary vessel structure, identifying a cardiovascular vessel in the image data, identifying, based on the image data, a centre line through the vessel. identifying, based on the image data, perimeter points identifying an outer perimeter of the vessel at a first location along the centre line, identifying, based on the image data, lumen points identifying a lumen of the vessel at a first location along the centre line, fitting a first transversal perimeter curve to the perimeter points, fitting a first transversal lumen curve to the lumen points, and obtaining the vessel wall parameters based on the first transversal perimeter curve and the first transversal lumen curve.

Description

P123762NL00 Title: Method of obtaining vessel wall parameters in a 3D model of a cardiovascular system
FIELD OF THE INVENTION The invention relates to a method for constructing a 3D model of a cardiovascular system.
BACKGROUND Many useful visualizations and measurements, either direct or derived from computer simulations, can be performed on the cardiovascular system when 3D model descriptions are available. These models can be derived from imaging data obtained from a CT system.
SUMMARY It is preferred to provide a 3D model of a cardiovascular system which provides a better visualisation of flow information through a lumen, as well as of the vessel wall and surrounding tissue. A lumen may be defined as the interior of a vessel, such as the central space in an artery or vein, through which blood as an example of a fluid flows. The lumen is surrounded by a vessel wall with a certain thickness, which thickness may be non-constant. Protruding into the lumen, thus restricting the lumen, may be one or more obstructions, for example due to atheroma, or atheromatous plaque.
The plaque first expands in the vessel wall up to a certain degree, followed by intrusion into the lumen. Because the plague expands first in the vessel wall, as explained in the Glagov effect, most of the plaque may not be visible when using X-ray imaging angiography. Outside of the vessel wall is the peri-vascular space, which may provide important information that may also not be visible when using x-ray imaging angiography.
A first aspect provides a method of obtaining vessel wall parameters in a 3D model of a cardiovascular system, comprising the steps of obtaining 3D image data of a body volume comprising a vessel structure, which for example may be a coronary vessel structure and/or a cardiovascular vessel structure, identifying a cardiovascular vessel in the image data, identifying, for example based on the image data, a centre line through the vessel, identifying, for example based on the image data, perimeter points identifying an outer perimeter of the vessel at a first location along the centre line, identifying, based on the image data, lumen points identifying a lumen of the vessel at a first location along the centre line, fitting a first transversal perimeter curve to the perimeter points, fitting a first transversal lumen curve to the lumen points, and obtaining the vessel wall parameters based on the first transversal perimeter curve and the first transversal lumen curve.
The area between the outer perimeter curve and the lumen curve may correspond to the vessel wall, or at least part thereof. The first location along the centre line may be obtained for example from a user input.
The perimeter may be formed by the outside of the vessel wall, or by at least part thereof. Thus, a perimeter point may be an outer vessel wall point, a perimeter contour may be an outer vessel wall contour, and a perimeter curve may be an outer vessel wall curve.
Optionally, multiple perimeters may be identified. For example may intermediate perimeter points be identified corresponding to a perimeter between a muscle as an intermediate vessel wall layer and an outer vessel wall layer.
The vessel wall parameters that may be obtained using an embodiment of the first aspect may comprise at least one of an area surrounded by the transversal perimeter curve, an area surrounded by the transversal lumen curve, a circumference of the transversal perimeter curve, a circumference of the transversal lumen curve, a material comprised by the vessel wall, a set of materials comprised by the vessel wall, a set of surface areas corresponding to the set of materials, a thickness of the vessel wall and/or any combination thereof.
Identifying the perimeter points and identifying the lumen points may comprise obtaining a first longitudinal 2D projection of the centre line, obtaining, from the 3D image data, a first set of 2D image data around the centre line in accordance with the first 2D projection, identifying the perimeter points based on the first set of 2D image data and identifying the lumen points based on the first set of 2D image data.
A longitudinal 2D projection may be a longitudinal section of the 3D image data of the cardiovascular by a plane oriented corresponding to the centre line through the vessel, which may be a planar plane when the centre line is a straight centre line, or may be a curved plane when the centre line is a curved centre line.
In an embodiment, the method further comprises the steps of transforming the 2D projection of the centre line to a substantially straight line according to a first transformation. applying the first transformation to the first set of 2D image data for obtaining a 2D image data plane and identifying the outer perimeter points and the lumen points based on the transformed 2D image data.
Identifying the perimeter points and identifying the lumen points may comprise obtaining a second longitudinal 2D projection of the centre line, which second projection is rotated along the centre line relative to the first projection, obtaining, from the 3D image data, at least a second set of 2D image data around the centre line in accordance with the second 2D projection, identifying the perimeter points based on the second set of 2D image data, and identifying the lumen points based on the second set of 2D image data.
In an embodiment, the method further comprises the steps of identifying, based on the image data, perivascular points identifying a perivascular object adjacent to the outer perimeter of the vessel at a first location along the centre line, and wherein obtaining at least some of the vessel wall parameters 1s based on the perivascular points.
The vessel wall parameters may thus also comprise parameters related to perivascular objects provided adjacent to the perimeter of the vessel. These perivascular objects are provided outside this perimeter.
In an embodiment, the method further comprises the steps of identifying, based on the image data, vessel wall object points identifying a vessel wall object between at least one transversal perimeter curves and at least one lumen curve, and wherein obtaining at least some of the vessel wall parameters is based on the vessel wall object points.
The vessel wall parameters may thus also comprise parameters related to one or more objects present in the vessel wall. An examples of such an object 1s plaque. A vessel wall parameter may relate to the material composition of the one or more identified vessel wall objects, to the size, the volume, and/or the surface area occupied by the vessel wall objects and/or distinct materials comprised by the vessel wall object.
The method may further comprise fitting a vessel wall object curve to the vessel wall object points.
In a further embodiment of the method, the method further comprises identifying, based on intensities of the image data, a vessel wall object curve, identifying a vessel wall object between at least one transversal perimeter curves and at least one lumen curve, and wherein obtaining at least some of the vessel wall parameters is based on the vessel wall object curve.
In an embodiment of the method, the method further comprises obtaining at least one material parameter of the vessel wall object using intensities of image data surrounded by the vessel wall object curve. Such an material parameter may be indicative of a material comprised by the vessel wall object.
Embodiments of the method may further comprise determining, in a transversal section, a surface area ratio between an area surrounded by the perimeter curve and an area surrounded by the vessel wall object curve. If in multiple transversal sections this ratio is determined, an indication 5 may be provided where the ratio deviates from an average ratio, or where the ratio falls below a certain threshold, and/or exceeds a certain threshold. A visual indicator may be provided at such transversal sections.
Embodiments of the first aspect may be applied to a cardiovascular model, and in particular blood vessel, but may also be used for other vessels, such as lymph vessels or other vessels comprising a lumen through which fluid may be transported.
When the image data comprises pixels or voxels, the intensity of the image data may refer to intensity values comprised by said pixels or voxels. A pixel of voxels may comprise a grey scale intensity, for example varying between 0 and 255, wherein the value 0 may refer to no intensity or black, the value 255 may refer to full intensity or white, and values between 0 and 255 may refer to different shades of grey. Furthermore may pixels or voxels comprise intensities in different colours, expressed for example in a RGB space, CMYK space or any other intensity space. Different intensity values of adjacent pixels of voxels may result in an intensity gradient, which may be used for detection of contours and/or edges.
Intensity values may also be expressed using Hounsfield units, which may be used for example when the image data is obtained using a CT imaging device. The Hounsfield unit scale may range from -1000 corresponding to air, up to a value larger than 0, for example 1000, wherein 0 may correspond to water. By using known intensity values for known materials, from the intensity values the material composition of the area depicted by a certain pixel or voxel may be determined.
According to embodiments of the method according to the first aspect, first, geometric parameters such as the first perimeter curve and the first lumen curve are identified. Next, using these geometric parameters, optionally in combination with intensity data in parts of the image data related to the geometric parameters, vessel wall parameters may be determined. The vessel parameters may for example relate to the material composition of the entire vessel wall, or of certain locations within the vessel wall.
A second aspect provides a computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the steps of an embodiment of the method according to the first aspect.
A third aspect provides a computer-readable data carrier having stored thereon the computer program according to the second aspect.
A fourth aspect provides a computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out the steps of the method according to the first aspect.
BRIEF DESCRIPTION OF THE DRAWINGS The various aspects and embodiments thereof will now be elucidated in conjunction with drawings. In the drawings: Figure 1 A: shows 3D image data depicting a vessel; Figure 1 B: shows a straightened model of the vessel; Figure 2 A: shows a longitudinal section through the vessel; Figure 2 B: shows a transversal section with edge points; Figure 3 A: shows a transversal section view with contours; Figure 3 B: shows a 3D vessel wall representation of the vessel; Figure 4 A: shows a transversal section view with an artefact; Figure 4 B: shows a 3D vessel wall representation with the artefact; Figure 5 A: shows a longitudinal section through the vessel with a peri-vascular object; and
Figure 5 B: shows a transversal section view with the peri- vascular object.
DETAILED DESCRIPTION OF THE FIGURES Fig. 1A shows 3D (three-dimensional) image data 110 comprising image data on a vessel 106 of a cardiovascular system. The 3D image data may for example be constructed from a CT scan of a human body, or part of that body as a body volume. The 3D image data 110 is shown in a 3D coordinate system 112. Based on the image data, a centre line 109 through vessel 106 is identified.
Fig. 1B shows a straightened model 113 of vessel 106, which has been straightened using a curved planar reformation, as for example explained in Kanitsar et al., 2002, CPR - Curved Planar Reformation, as an example of a straightening transformation applied to the vessel 106 such that the centre line 109 of the vessel 106 is transformed to a straightened centre line 119, which is a substantially straight line.
The optional straightening process may be preferred to make further steps in the method less computational heavy, more precise, and/or generally less complicated.
Through the straightened model 113, four longitudinal sections are shown in Fig. 1B, which are provided through the centre line 109 and are spaced at a constant angle relative to each other. In this example with the four longitudinal sections, the angular spacing is 45 degrees. Embodiments are envisioned with any other number of longitudinal sections, and the angular spacing may be non-constant. For example, a tighter angular spacing may be used in a certain region for higher precision.
The longitudinal sections in Fig. 1B are shown as planar planes, due to the straightening process. In other embodiments, for example when no straightening process has been applied, one or more of the longitudinal sections may be curved planes.
The four longitudinal sections in Fig. 1B are referred to as a first longitudinal section 161, a second longitudinal section 162, a third longitudinal section 163, and a fourth longitudinal section 164.
The straightened model 113 in Fig. 1B is plotted in a 3D space spanned by a straightened centre line vector 191, and axes 117 and 118. In the embodiment of Fig. 1B, the first longitudinal section plane 161 is spanned by the centre line vector 191 and axis 118.
Although the straightened model 113 of the vessel 106 is shown in Fig. 1B as having a substantially constant diameter, the diameter of the vessel 106 in the straightened model may vary as well, as will become apparent from Fig. 2A.
Fig. 2A shows a longitudinal section through the first longitudinal plane 161 as a first longitudinal 2D projection of the vessel 106. Using for example a contour detection algorithm or an edge detection algorithm, four contours are identified, of which the outer contours correspond to perimeter contours and the inner contours correspond to lumen contours. A first perimeter contour 136 and a second perimeter contour 137 together are indicative of the perimeter of the vessel 106, and a first lumen contour 134 and a second lumen contour 135 are indicative of the lumen of the vessel
106.
At a first location 165 on the straightened centre line 119, a first transversal section 171 is made, substantially perpendicular to centre line
119. At an intersection between the first transversal section 171 and the first perimeter contour 136, a first perimeter point 142 is identified. At an intersection between the first transversal section 171 and the first lumen contour 134, a first lumen point 141 is identified. Similarly, at the intersection between first transversal section 171 and the second perimeter contour 137 and the second lumen contour 135, respectively, a second perimeter point 147 and a second lumen point 145 are identified.
Fig. 2B shows the first transversal section 171, with the first lumen point 141, second lumen point 145, the first perimeter point 142, and the second perimeter point 147 for the first longitudinal section 161. Similarly, lumen points and perimeter points for the second longitudinal section 162, the third longitudinal section 163, and the fourth longitudinal section 164 are plotted in the first transversal section view 171. Per longitudinal section, two lumen points and two perimeter points may be provided.
Fig. 3A again shows the first transversal section view 171, wherein now a first lumen curve 151 has been fitted to the lumen points, and a first perimeter curve 152 has been fitted to the perimeter points. The area between the first lumen curve 151 and the first perimeter curve 152 represent a vessel wall 182 section of the vessel 106 at the first location 165 along the centre line 109. Having determined the location of the vessel wall 182 in the image data, the image data belonging to the vessel wall 182 may be analysed to obtain vessel wall parameters, such as the materials comprised by the vessel wall.
From the first transversal section view 171, vessel wall parameters may be obtained. For example may an area surrounded by the transversal perimeter curve 152 be derived from the transversal perimeter curve 152, which area may be indicative of a total cross section area of the vessel 106 at the particular location along the centre line 109 at which the transversal section view has been obtained. Furthermore, a circumference of the transversal perimeter curve 152 may be derived.
For example may also an area surrounded by the transversal lumen curve 151 be determined from the transversal lumen curve 151, which area may be indicative of a flow through area for fluid through the lumen, which in an example may be blood through a vessel. Furthermore, a circumference of the transversal lumen curve 151 may be derived.
If different materials are found to be comprised by the vessel wall 182, for example due to formation of plaque, the surface area of the different materials as a vessel wall parameter may be analysed, for example using the intensity of the pixels or voxels of the image data.
For constructing a lumen curve or a perimeter curve, a curve fitting algorithm for example based on a travelling salesman problem or Dijkstra’s algorithm may be used. A curve may be represented by a mathematical model like a spline, an ellipse, a polygon, or another mathematical model, or by a numerical model.
Fig. 3B shows a vessel wall representation 144 of the vessel 106, plotted in the same 3D coordinate system 112 as Fig. 1A. By using a plurality of transversal sections, a plurality of perimeter curves and lumen curves are obtained along the centre line 109. The perimeter curves are combined into a perimeter tube 186, and the lumen curves are combined into a lumen tube 188.
A tube fitted to a plurality of curves may be represented as a mesh, as a polygon mesh, as a mathematical model, a numerical model.
Fig. 4A again shows the first transversal section view 171, now also showing a first vessel wall object point 201 and a second vessel wall object point 202, corresponding to the fourth longitudinal section 164. Fig. 4A also shows a third vessel wall object point 203 and a fourth vessel wall object point 204 corresponding to the third longitudinal section 163.
A wall object curve 206 (shown as a dashed line) is fitted to the vessel wall object points to provide an indication of the surface area in the first transversal section view 171 occupied by a vessel wall object 210 as a vessel wall parameter, The vessel wall object may for example comprise plaque.
Alternatively to using wall object points identified in the longitudinal section or additional thereto, the intensity of the image data comprised by the first transversal section view 171, and/or any other transversal section view, may be used to identify one or more objects in the vessel wall.
Using the intensities of the image data, an alternative wall object curve 208 (shown as a dashed-dotted line) is identified, and may provide an indication of the location and/or surface area occupied by the wall object.
The intensities of the image data may be further used to provide an indication of the materials comprised by the wall object, such as cholesterol, fat, calcium, any other material which may be present in the vessel wall, or any combination thereof.
A combination of the wall object points and the intensities of the image data may also be used for obtaining vessel wall parameters such as parameters related to the vessel wall object 210.
A surface area ratio between an area surrounded by the perimeter curve and an area surrounded by the vessel wall object curve may be used to provide an indication of the relative size of the vessel wall object or of the thickness of the vessel wall itself - though the thickness of the vessel wall may also be determined otherwise.
In an embodiment of the method, in one or more particular transversal sections, a ratio between the square root of the area surrounded by a lumen curve and the square root of the area surrounded by the perimeter curve may be calculated. This ratio may then be compared to the ratio between the circumference of the lumen curve and the circumference of the perimeter curve. This comparison may provide an indication that an obstruction is present in the vessel wall which limits the flow through area through the lumen. If the comparison is made for multiple transversal sections, an indicator may be provided corresponding to the outcome of the comparison relative to the outcome of the comparison at adjacent transversal sections.
In embodiments of the method, for obtaining further vessel wall parameters, further mathematical, numerical and/or statistical operations may be performed using one or more of the vessel wall parameters. One or more formulas or equation may be applied to one or more of the obtained vessel wall parameters, using the vessel wall parameters themselves or a numerical derivation thereof, such as a square root, an exponentiation, a multiplication, or any other numerical derivative.
Fig. 4B shows another vessel wall representation 144 of the vessel 106, wherein now the vessel wall object 210 is visualised. For example by combining a plurality wall object curves obtained in a plurality of transversal sections along multiple locations on the centre line 109, the vessel wall object 210 may be visualised.
Fig. 5A depicts a situation wherein the image data on the cardiovascular system comprises, next to the vessel with its lumen and perimeter, image data on a perivascular object 192 provided adjacent to the vessel perimeter. More particular, Fig. 5A shows a section view through the first longitudinal section 161, wherein next to lumen contours 134, 135 and perimeter contours 136, 137 also the perivascular object 192 is visible.
Around the perivascular object 192, a perivascular boundary 185 may be detected, which together with part of the first perimeter contour 136 may form a perivascular object contour. Additionally or alternatively, image intensity information, such as pixel or voxel intensity, may be used to identify the perivascular object 192. If the image data is acquired using a CT scanner device, the image intensity may differ by virtue of different materials comprised by the perimeter, the perivascular object, and the surroundings thereof.
At an intersection between the first transversal section 161 and the perivascular boundary 185, a first perivascular object point 181 is identified.
Fig. 5B shows the first transversal section view 171, with the perivascular object 192 adjacent to the perimeter of the vessel. Plotted in the transversal section view 171 of Fig. 5B is the first perivascular object point 181 which was identified in the first longitudinal section 161, and further a second perivascular point 182 identified in the second longitudinal section 162 and a third perivascular point 184 identified in the fourth longitudinal section 162.
Based on the perivascular points, a perivascular object curve 185 may be fitted. The perivascular object curve 185 may be an open curve, or may be a closed curve together with part of perimeter curve 152.
Next to obtaining the perivascular object curve 185 according to the exemplary method as described above, a perivascular object curve may be obtained by fitting a curve at a certain fixed distance from a vessel wall curve, which may be one or both of a transversal vessel wall curve and a longitudinal vessel wall curve. As such, a perivascular perimeter around the vessel wall may be determined, and for example using intensities in the image data constrained between the vessel wall and the perivascular perimeter, parameters such a material parameters may be obtained for the perivascular perimeter.
At least some of the various aspects and embodiments thereof discussed above may be summarised by means of the following numbered embodiments:
1. In a programmed computer, a method of obtaining vessel wall parameters in a 3D model of a cardiovascular system, comprising the steps of: - obtaining 3D image data of a body volume comprising a vessel structure; - identifying a cardiovascular vessel in the image data; - identifying, based on the image data, a centre line through the vessel; - identifying, based on the image data, perimeter points identifying an outer perimeter of the vessel at at least a first location along the centre line;
- identifying, based on the image data, lumen points identifying a lumen of the vessel at the first location along the centre line; - fitting a first transversal perimeter curve to the perimeter points; LC fitting a first transversal lumen curve to the lumen points; and > - obtaining the vessel wall parameters based on the first transversal perimeter curve and the first transversal lumen curve.
2. Method according to embodiment 1, wherein the vessel wall parameters comprises at least one of: - an area surrounded by the transversal perimeter curve; - an area surrounded by the transversal lumen curve; - a circumference of the transversal perimeter curve; - a circumference of the transversal lumen curve; - a material comprised by the vessel wall; - a set of materials comprised by the vessel wall; - a thickness of the vessel wall; and - a set of surface areas corresponding to the set of materials.
3. Method according to embodiment 1 or 2, wherein identifying the perimeter points and identifying the lumen points comprises: - obtaining a first longitudinal 2D projection of the centre line; - obtaining, from the 3D image data, a first set of 2D image data around the centre line in accordance with the first 2D projection; - identifying the perimeter points based on the first set of 2D image data; and - identifying the lumen points based on the first set of 2D image data.
4. Method according to embodiment 3, further comprising: - transforming the 2D projection of the centre line to a substantially straight line according to a first transformation;
- applying the first transformation to the first set of 2D image data for obtaining a 2D image data plane; and - identifying the outer perimeter points and the lumen points based on the transformed 2D image data.
5. Method according to any of the embodiments 2-4, wherein identifying the perimeter points and identifying the lumen points further comprises: - obtaining a second longitudinal 2D projection of the centre line, which second projection is rotated along the centre line relative to the first projection; - obtaining, from the 3D image data, at least a second set of 2D image data around the centre line in accordance with the second 2D projection; - identifying the perimeter points based on the second set of 2D image data; and - identifying the lumen points based on the second set of 2D image data.
6. Method according to any of the preceding embodiments, further comprising identifying, based on the image data, perivascular points identifying a perivascular object adjacent to the outer perimeter of the vessel at a first location along the centre line, and wherein obtaining at least one of the vessel wall parameters 1s based on the perivascular points.
7. Method according to any of the preceding embodiments, further comprising identifying, based on the image data, vessel wall object points identifying a vessel wall object between at least one transversal perimeter curves and at least one lumen curve, and wherein obtaining at least one of the vessel wall parameters is based on the vessel wall object points.
8. Method according to embodiment 7, further comprising fitting a vessel wall object curve to the vessel wall object points.
9. Method according to any of the preceding embodiments, further comprising identifying, based on intensities of the image data, a vessel wall object curve, identifying a vessel wall object between at least one transversal perimeter curves and at least one lumen curve, and wherein obtaining at least some of the vessel wall parameters is based on the vessel wall object curve.
10. Method according to embodiment 8 or 9, further comprising obtaining at least one material parameter of the vessel wall object using intensities of image data surrounded by the vessel wall object curve.
11. Method according to embodiment 10, further comprising determining, in a transversal section, a surface area ratio between an area surrounded by the perimeter curve and an area surrounded by the vessel wall object curve.
12. Method according to any of the embodiments 2-11, further comprising, in one or more longitudinal 2D projections, identifying one or more contours, wherein identifying the outer perimeter points and the lumen points is based on the one or more contours.
13. A computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the steps of the method according to any of the preceding embodiments.
14. A computer-readable data carrier having stored thereon the computer program of embodiment 13.
15. A computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out the steps of the method according to any of the embodiments 1-12. Expressions such as "comprise", "include", "incorporate", "contain", "is" and "have" are to be construed in a non-exclusive manner when interpreting the description and its associated claims, namely construed to allow for other items or components which are not explicitly defined also to be present.
Reference to the singular is also to be construed in be a reference to the plural and vice versa.
In the description above, it will be understood that when an element such as layer, region or substrate is referred to as being “on” or “onto” another element, the element is either directly on the other element, or mtervening elements may also be present.
Furthermore, the invention may also be embodied with less components than provided in the embodiments described here, wherein one component carries out multiple functions.
Just as well may the invention be embodied using more elements than depicted in the Figures, wherein functions carried out by one component in the embodiment provided are distributed over multiple components.
A person skilled in the art will readily appreciate that various parameters disclosed in the description may be modified and that various embodiments disclosed and/or claimed may be combined without departing from the scope of the invention.

Claims (15)

ConclusiesConclusions 1. In een geprogrammeerde computer, een werkwijze voor verkrijgen van vaatwandparameters uit een 3D model van een cardiovasculair system, omvattende de stappen van: - verkrijgen van 3D beelddata van een lichaamsvolume omvattende een bloedvatstructuur; - identificeren van een bloedvat in de beelddata; - identificeren, gebaseerd op de beelddata, van een hartlijn door het bloedvat; - identificeren, gebaseerd op de beelddata, van omtrekpunten welke een buitenomtrek van het bloedvat identificeren op een eerste locatie langs de hartlijn; - identificeren, gebaseerd op de beelddata, van lumenpunten welke een lumen van het bloedvat identificeren op de eerste locatie langs de hartlijn; - inpassen van een eerste transversale omtrekscurve op de omtrekpunten; - inpassen van een eerste transversale lumencurve op de lumenpunten; en - verkrijgen van de vaatwandparameters gebaseerd op de eerste transversale omtrekscurve en de eerste transversale lumencurve.In a programmed computer, a method for obtaining vessel wall parameters from a 3D model of a cardiovascular system, comprising the steps of: - obtaining 3D image data of a body volume comprising a blood vessel structure; - identifying a blood vessel in the image data; - identifying, based on the image data, a centerline through the blood vessel; - identifying, based on the image data, contour points identifying an outer circumference of the blood vessel at a first location along the axis; - identifying, based on the image data, lumen points that identify a lumen of the blood vessel at the first location along the axis; - fitting a first transverse contour curve onto the contour points; - fitting a first transverse lumen curve at the lumen points; and - obtaining the vessel wall parameters based on the first transverse circumferential curve and the first transverse lumen curve. 2. Werkwijze volgens conclusie 1, waarin de vaatwandparameters ten minste een omvatten van: - een oppervlak omringt door de transversale omtrekscurve; - een oppervlak omringt door de transversale lumencurve; - een omtrek van de transversale omtrekscurve; - een omtrek van de transversale lumencurve; - een materiaal omvat door de vaatwand;Method according to claim 1, wherein the vessel wall parameters comprise at least one of: - a surface surrounded by the transverse circumferential curve; - an area surrounded by the transverse lumen curve; - a contour of the transverse contour curve; - an outline of the transverse lumen curve; - a material encompassed by the vessel wall; - een set materialen omvat door de vaatwand; - een dikte van de vaatwand; en - een set van oppervlaktes corresponderend met de set materialen.a set of materials contained by the vessel wall; - a thickness of the vessel wall; and - a set of surfaces corresponding to the set of materials. 3. Werkwijze volgens conclusie 1 of 2, waarin identificeren van de omtrekpunten en identificeren van de lumenpunten omvat: - verkrijgen van een eerste longitudinale 2D projectie van de hartlijn; - verkrijgen, uit de 3D beelddata, een eerste set van 2D beelddata rond de hartlijn in overeenstemming met de eerste 2D projectie; - identificeren van omtrekpunten gebaseerd op de eerste set van 2D beelddata; en - identificeren van lumenpunten gebaseerd op de eerste set van 2D beelddata.A method according to claim 1 or 2, wherein identifying the circumferential points and identifying the lumen points comprises: - obtaining a first longitudinal 2D projection of the centerline; obtain, from the 3D image data, a first set of 2D image data about the centerline in accordance with the first 2D projection; - identifying contour points based on the first set of 2D image data; and - identifying lumen points based on the first set of 2D image data. 4. Werkwijze volgens conclusie 3, verder omvattende: - transformeren van de 2D projectie van de hartlijn naar een in hoofdzaak rechte lijn volgens een eerste transformatie; - toepassen van de eerste transformatie op de eerste set van 2D beelddata voor verkrijgen van een 2D beelddatavlak; en - identificeren van de omtrekpunten en de lumenpunten gebaseerd op de getransformeerde 2D beelddata.A method according to claim 3, further comprising: - transforming the 2D projection of the centerline into a substantially straight line according to a first transformation; - applying the first transformation to the first set of 2D image data to obtain a 2D image data plane; and - identifying the contour points and the lumen points based on the transformed 2D image data. 5. Werkwijze volgens een van de conclusies 2-4, waarin identificeren van de omtrekpunten en de lumenpunten verder omvat: - verkrijgen van een tweede longitudinale 2D projectie van de hartlijn, welke tweede projectie geroteerd is om de hartlijn ten opzichte van de eerste projectie;The method of any of claims 2-4, wherein identifying the circumferential points and the lumen points further comprises: - obtaining a second longitudinal 2D projection of the centerline, the second projection being rotated about the centerline from the first projection; - verkrijgen, van de 3D beelddata, ten minste een tweede set van 2D beelddata rond de hartlijn in overeenstemming met de tweede 2D projectie; - identificeren van de omtrekpunten gebaseerd op de tweede set van 2D beelddata; en - identificeren van de lumenpunten gebaseerd op de tweede set van 2D beelddata.- obtain, from the 3D image data, at least a second set of 2D image data about the centerline in accordance with the second 2D projection; - identifying the contour points based on the second set of 2D image data; and - identifying the lumen points based on the second set of 2D image data. 6. Werkwijze volgens een van de voorgaande conclusies, verder omvattende identificeren, gebaseerd op de beelddata, perivasculaire punten welke een perivasculair object identificeren aangrenzend aan de buitenomtrek van het bloedvat op een eerste locatie langs de hartlijn, en waarin verkrijgen van ten minste een van de vaatwandparameters gebaseerd is op de perivasculaire punten.The method of any one of the preceding claims, further comprising identifying, based on the image data, perivascular points identifying a perivascular object adjacent to the outer circumference of the blood vessel at a first location along the axis, and wherein obtaining at least one of the vessel wall parameters based on the perivascular points. 7. Werkwijze volgens een van de voorgaande conclusies, verder omvattende identificeren, gebaseerd op de beelddata, vaatwandobjectpunten welke een vaatwandobject identificeren tussen ten minste een transversale omtrekcurve en ten minste een transversale lumencurve, en waarin verkrijgen van ten minste een van de vaatwandparameters gebaseerd is op de vaatwandobjectpunten.The method of any preceding claim, further comprising identifying, based on the image data, vessel wall object points identifying a vessel wall object between at least one transverse perimeter curve and at least one transverse lumen curve, and wherein obtaining at least one of the vessel wall parameters is based on the vessel wall object points. 8. Werkwijze volgens conclusie 7, verder omvattende fitten van een vaatwandobjectcurve op de vaatwandobjectpunten.The method of claim 7, further comprising fitting a vessel wall object curve to the vessel wall object points. 9. Werkwijze volgens een van de voorgaande conclusies, verder omvattende identificeren, gebaseerd op intensiteiten van de beelddata, een vaatwandobjectcurve welke een vaatwandobject identificeert tussen ten minste een transversale omtrekcurve en ten minste een transversale lumencurve, en waarin verkrijgen van ten minste enkele van de vaatwandparameters gebaseerd is op de vaatwandobjectcurve.The method of any preceding claim, further comprising identifying, based on intensities of the image data, a vessel wall object curve identifying a vessel wall object between at least one transverse perimeter curve and at least one transverse lumen curve, and wherein obtaining at least some of the vessel wall parameters is based on the vessel wall object curve. 10. Werkwijze volgens conclusie 8 of 9, verder omvattende verkrijgen van ten minste een materiaalparameter van het vaatwandobject gebruik makend van intensiteiten van beelddata omringt door de vaatwandobjectcurve.The method of claim 8 or 9, further comprising obtaining at least one material parameter of the vessel wall object using intensities of image data surrounded by the vessel wall object curve. 11. Werkwijze volgens conclusie 10, verder omvattende bepalen, in een transversale doorsnede, een oppervlakteratio tussen een oppervlak omringt door de omtrekcurve en een oppervlak omringt door de vaatwandobjectcurve.The method of claim 10, further comprising determining, in a transverse section, an area ratio between an area surrounded by the perimeter curve and an area surrounded by the vessel wall object curve. 12. Werkwijze volgens een van de conclusies 2-11, verder omvattende, in een of meerdere longitudinale 2D projecties, identificeren van een of meerdere contouren, waarin identificeren van de omtrekpunten en de lumenpunten gebaseerd is op de een of meerdere contouren.The method of any of claims 2-11, further comprising, in one or more longitudinal 2D projections, identifying one or more contours, wherein identifying the contour points and the lumen points is based on the one or more contours. 13. Een computerprogramma omvattende instructies welke, wanneer het programma wordt uitgevoerd door een computer, de computer de stappen van de werkwijze volgens een van de voorgaande conclusies doet uitvoeren.A computer program comprising instructions which, when the program is executed by a computer, causes the computer to perform the steps of the method according to any preceding claim. 14. Een door een computer uitleesbare datadrager met daarop opgeslagen het computerprogramma van conclusie 13.A computer readable data carrier with the computer program of claim 13 stored thereon. 15. Een door een computer uitleesbaar opslagmedium omvattende instructies welke, wanneer uitgevoerd door een computer, de computer de stappen van de werkwijze volgens een van de conclusies 1-12 doet uitvoeren.A computer readable storage medium comprising instructions which, when executed by a computer, causes the computer to perform the steps of the method of any of claims 1-12.
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