Method and processor for analyzing the vasculature of a subject - Patents.com

Abstract

A method is provided for analyzing the vasculature of a subject. Within an image of a region of interest, a vascular tree is identified. Major vessels that form part of a standard tree of major vessels are identified, and then the major vessels of the standard tree are excluded to identify remaining vessels of the identified vascular tree. This allows collateral vessels within the region of interest to be isolated and analyzed.

Description

[Technical field]

[0001] The present invention relates to the analysis of images displaying the blood vessels of a subject. [Background technology]

[0002] Vascular disease in the coronary or peripheral system often involves stenotic lesions or complete blockages in the blood supply of body tissues.

[0003] The presence and extent of collateral vessel formation is recognized as an important marker in two different situations. First, collateral vessels are found as an indicator of the action of the body's compensatory mechanisms; that is, stenotic lesions are compensated for by the development of collateral circulation, maintaining perfusion to areas that are otherwise highly ischemic and prone to necrosis. Thus, collateral formation reveals a negative correlation with patient outcome; that is, patients with adequate, rather than poor, collateral formation have been found to have a good prognosis. Second, it is also known that these collaterals do not fully solve the blood supply problem, since failures are still indicated, especially under stress.

[0004] Both of these effects emphasize the relevance of collateral circulation and the need to quantify the vascular status. Parameters of interest are, in particular, the absolute number of collaterals, their diameter (as it is an indicator of the amount of blood passing through these collaterals instead of the actual vessels), the length of the collaterals, and which parts of the vascular tree are bypassed.

[0005] Algorithms are known for analyzing the vasculature, however, most algorithmic solutions currently focus on detecting main vessels, as these are reliably detected in most patients and are typically the targets of interventions.

[0006] However, there are some small vessels of great anatomical variation. The properties of this remaining part of the vascular tree provide markers of systemic disease status, but are not currently quantified in state-of-the-art analytical applications. However, this neglected part of the vascular system may contain many collateral vessels that interconnect different parts of the vascular tree, acting as natural collaterals for occluded or stenosed sites. Summary of the Invention [Problem to be solved by the invention]

[0007] Therefore, there is a need to quantify the often neglected vascular status of collateral formation, especially in patients with vascular disease in the major vascular trees.

[0008] Van Horssen Pepijn et al., "Innate collateral segments are predominantly present in the subendocardium without preferential connectivity within the left ventricular wall: Distribution and morphology of innate collateral connections," Journal of Physiology, Vol. 592, No. 5, 23 January 2014, pp. 1047-1060 (XP055920694), presents an analysis of arteries based on the understanding that collateral arteries grow as chronic arterial disease develops, providing a pathway for oxygen-rich blood to perfused sites of occluded coronary arteries. The morphology and distribution of the innate collateral network in healthy hearts has been quantified. [Means for solving the problem]

[0009] The invention is defined by the claims.

[0010] According to an embodiment of the present invention, there is provided a method for analysing the vasculature of a subject, the method comprising: receiving an image of a region of interest of a subject; Identifying a vascular tree present in a region of interest; - identifying major vessels within the identified vascular tree, the major vessels forming part of a standard tree of major vessels; - identifying remaining vessels of the identified vascular tree, thus excluding the main vessels of the standard tree, thereby identifying collateral vessels within the region of interest; and performing a collateral vessel analysis.

[0011] This analysis can provide measurements that can be reported to clinicians for use in quantifying vascular status, particularly in relation to collateral vessels, which indicates the presence of vascular disease in the primary vascular tree.

[0012] The method first detects a standard vascular tree as a part of the entire (i.e. net) detected vascular tree. The standard tree is extracted by a hierarchical rule-based classification, for example based on the major large vascular trunks that are also present in healthy individuals. A statistical atlas of vascular trees may be used, in which probabilities are assigned to the structural and positional parameters of the tree.

[0013] The remaining vasculature includes collateral vessels, which are analyzed to specifically quantify anatomical characteristics of collateral vessel formation. The remaining vessels are found by subtracting the main vessels from the identified vascular tree. For example, the quantified anatomical characteristics include parameters such as the number of collateral vessels, the luminal area or blood volume of the collateral vessels, vessel length, or net vessel density.

[0014] Identifying the vascular tree present in the region of interest includes, for example, image segmentation and vascular centerline extraction. Algorithms are known for extracting vascular geometry from both 2D and 3D image data. The image data can be applied to low-resolution images such as Coronary Computed Tomography Angiography (CCTA) images.

[0015] Identifying major vessels may involve, for example, using hierarchical rule-based classification, again, algorithms for this are known.

[0016] Collateral analysis includes determining one or more parameters indicative of collateral formation.

[0017] In a first embodiment, the parameters include the number of collateral vessels.

[0018] In a second embodiment, the parameters include a cumulative length of collateral vessels.

[0019] In a third embodiment, the parameters include collateral lumen volume.

[0020] In a fourth embodiment, the parameter is related to the intervascular area. This parameter comprises, for example, the net vessel density calculated by projection onto a reference surface. This projection allows the net density to be obtained by analysis of a 2D image representing the 3D volume of the region of interest. For example, the reference surface comprises the epicardial wall aligned with the segmented cardiac image.

[0021] The step of performing the collateral analysis may for example comprise a step of normalizing the parameters with respect to the values ​​of the parameters of a standard tree.

[0022] Alternatively, performing the collateral analysis may include normalizing the parameter to a reference value of the parameter corresponding to healthy subjects or to a reference value of a particular patient group.

[0023] In either case, this provides a measure that is readily understandable with reference to a standard vascular tree or a healthy population.

[0024] The received images may be 3D images, such as 3D computed tomography (CT) angiography images or 3D magnetic resonance angiography (MRA) images.

[0025] However, the received image may also be a 2D image, such as a 2D X-ray angiogram image.

[0026] The present invention is therefore applicable to both diagnostic 3D imaging and interventional 2D angiography.

[0027] The present invention also provides a computer program comprising computer program code means for carrying out the above-mentioned method when the computer program is run on a computer.

[0028] The invention also provides a processor programmed with the above computer program.

[0029] The present invention also provides an imaging system comprising: an imager for acquiring an image of a region of interest of the subject; and a processor as described above for analyzing the image to perform an analysis of collateral vessels within the region of interest.

[0030] These and other aspects of the invention will be apparent from and elucidated with reference to the embodiments described hereinafter. [Brief description of the drawings]

[0031] For a better understanding of the present invention and to show more clearly how it may be carried into effect, reference will now be made, by way of example only, to the accompanying drawings in which:

[0032] [Figure 1] FIG. 1 shows a 3D image of the heart showing the major vessels and the complete network of blood vessels. [Diagram 2] FIG. 2 shows the peripheral vasculature of the foot of a patient without foot disease. [Diagram 3] Figure 3 shows the peripheral vasculature of the leg of a patient with severe obstruction in the lower calf and foot. [Figure 4] FIG. 4 illustrates a method for analyzing the vasculature of a subject. [Diagram 5]FIG. 5 illustrates generally how vasculature is identified from a 3D image, such as 3D CT data. [Figure 6] FIG. 6 shows the ellipses A to C of FIG. 5, the determined orientation of the blood vessel, and the blood vessel shape D in a cross section perpendicular to the blood vessel axis. [Figure 7] FIG. 7 shows how the vasculature is segmented. [Figure 8] FIG. 8 illustrates a procedure for extracting a centerline from 3D image data. [Figure 9] FIG. 9 shows an enlarged longitudinal view of the LCX vessel. [Figure 10] FIG. 10 shows an example of an interventional 2D angiogram after segmentation has identified a complete and standard tree of the vasculature of the leg of a patient with little to no collateral formation. [Figure 11] FIG. 11 shows an example of an interventional 2D angiogram after the complete and standard trees of the leg vasculature of a patient with moderate collateral formation have been identified by the segmentation module. [Figure 12] FIG. 12 shows an example of an imaging system that can be used to provide images for analysis using the methods of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0033] The present invention will now be described with reference to the drawings.

[0034] It should be understood that the detailed description and specific examples, while indicating exemplary embodiments of the devices, systems, and methods, are for purposes of illustration only and are not intended to limit the scope of the invention. These and other features, aspects, and advantages of the devices, systems, and methods of the present invention will become better understood from the following description, the appended claims, and the accompanying drawings. It should be understood that the figures are schematic representations only and are not drawn to scale. It should also be understood that the same reference numerals are used throughout the figures to indicate the same or similar parts.

[0035] The present invention provides a method for analyzing the vasculature of a subject, where in an image of a region of interest a vascular tree is identified, where major vessels forming part of a standard tree of major vessels are identified, and then the remaining vessels of the identified vascular tree are identified, excluding the major vessels of the standard tree, whereby collateral vessels in the region of interest are isolated and an analysis of the collateral vessels can be performed.

[0036] In FIG. 1, a 3D image of the heart is shown at the top, depicting the major blood vessels 10. The major blood vessels 10 are present in most patients, but at finer scales the vessels may differ more strongly. Of these, there may be numerous collateral vessels that can establish collateral pathways for blood flow. The number and extent of these collaterals have been found to be descriptive markers indicative of coronary artery disease.

[0037] Figure 1 shows the entire network of blood vessels in the lower diagram, showing small collateral vessels. The coronary vascular tree ensures myocardial perfusion. The anatomical variations are greater the more detailed the tree.

[0038] Figure 2 shows the peripheral vasculature of the foot. In the top image, the patient has no disease in the foot (or less disease than in Figure 3), and shows the three major vessels that come primarily from the calf and supply the foot. The bottom image is from a patient with severe obstruction in the lower calf and foot. As a compensatory mechanism, several collaterals have developed.

[0039] The present invention is based on isolating collateral vessels for analysis.

[0040] FIG. 4 illustrates a method for analyzing the vasculature of a subject.

[0041] In step 40, an image of a region of interest of the subject is acquired, which may be the chest region for analysis of the vascular tree near the heart, but may be any region of interest.

[0042] In step 42, the vascular tree present in the region of interest is identified.

[0043] In step 44, the major vessels are identified, ie the major vessels that form part of the standard tree of major vessels.

[0044] Next, in step 46, the remaining vessels in the identified vessel tree are identified, i.e. all identified vessels present in the region of interest, excluding the main vessels of the standard tree, and therefore these are collateral vessels of the region of interest.

[0045] At step 48, a collateral analysis is performed in which one or more anatomical parameters are described based on one or more measures applied to the image data.

[0046] These parameters are used to quantify vascular status, especially in the presence of vascular disease in the primary vascular tree. The number and characteristics of blood-supplied collaterals indicate the extent to which the body can no longer rely on its standard pathways, and therefore how important a role the collaterals play in maintaining the overall blood supply.

[0047] Figures 5-7 show a schematic of the process described in "Robust 3-D Airway Tree Segmentation for Image-Guided Peripheral Broncoscopy" by Graham et al. (TMI, 29:4, 2010).

[0048] FIG. 5 illustrates generally how vasculature is identified from a 3D image, such as 3D CT data.

[0049] A set of 2D ellipses A, B, C are obtained along all three axes (x, y, z) by a set of simple thresholding operations followed by connected component analysis and local ellipse fitting.

[0050] The ellipses are projected onto a plane perpendicular to the vascular centerline 60 that connects the three consecutive ellipses, resulting in a final ellipse D that is no longer aligned with the axis.

[0051] 6 shows ellipses A to C, the determined orientation of the blood vessel, and the blood vessel shape D in a cross section perpendicular to the blood vessel axis. From this diagram, the diameter and area of ​​the blood vessel can be determined.

[0052] Figure 7 shows how the vasculature is segmented. A set of ellipses is obtained, as shown in panel A. These are assembled into a set of vessel segments, as shown in panel B. The gaps between the vessel segments are filled by an interpolation step to obtain the vessel branches, as shown in panel C. The vessel branches are voxelized and the union of all branches results in a binary vessel mask, as shown in panel D.

[0053] For more information about this procedure, see Graham, Robust Methods for Human Airway-Tree Segmentation and Anatomical-Tree Matching, PhD dissertation, 2008, https: / / etda.libraries.psu.edu / files / final_submissions / 4430.

[0054] In summary, the segmentation step uses axis-aligned ellipses to collect local information and generate a binary voxel mask. Optionally, the segmentation module may use an external probabilistic atlas as a reference.

[0055] Figure 8 shows the procedure for extracting centerlines from 3D image data, such as 3D CT data. Starting with a binary mask, shown in panel A, a distance transform is used, as shown in panel B. Then, a distance-to-peak mask is applied as input to the centerline tracker, as shown in panel C. Also, a calcium penalty of 80 can be used along with a threshold based on the mean aortic intensity plus 3 standard deviations to ensure that the centerline does not pass through calcified tissue.

[0056] Centerline extraction is therefore the process of converting a binary mask containing the image volume occupied by the vessel into a tree structure by tracing its branches one after the other.

[0057] Post-processing can then be used to remove venous branches and eliminate double detections. Smoothing can also be applied. A set of heuristics such as maximum length, branching depth, vessel internal diameter, matching with coronary vessel atlas, vessel curvature, branching angle, and location are used during post-processing, for example.

[0058] Given a parent vessel (e.g., the left circumflex artery, LCX), detection of child vessels (e.g., peripheral and diagonal branches) is aided by computing a stretched lumen view of the vessel, which is a sequence of equal-sized cross-sectional images perpendicular to the centerline of the parent vessel. A neural encoder model is then applied (with single or multiple cross-sections as input) to detect side branches.

[0059] 9 shows an enlarged longitudinal view of the LCX vessel with a first marginal artery branch M1 and a second marginal artery branch M2 to the left of the marker 90. Arrows 92 on the right further indicate smaller branches that are candidates for collateral blood flow.

[0060] For separation of standard trees and collateral trees, standard trees are separated from the set of candidate centerlines by applying rule-based classification, where the classification and application of rules is performed hierarchically, for example starting from the descendants of the left main (LM) or right coronary artery (RCA) at the ostium and proceeding down the vessel tree from parent to child.

[0061] A number of rules include, for example: Requiring a minimum lumen area / vessel diameter (i.e., a threshold vessel diameter); Branch only from a priori known locations from the main vessel. For coronary arteries, the probability distribution of typical LCX branching points from the left anterior descending artery (LAD) is modeled from a set of prior patient data and encoded onto the LAD centerline as a function of parent descent from the LM artery. Truncate to a certain maximum length from the origin (e.g., coronary ostium) or from the descendants of the parents.

[0062] The output from the segmentation and separation process is finally given by a labeled standard vessel tree and a residual tree containing small vessels and collateral circulation.

[0063] Next, anatomical parameters that can be used for collateral vessel analysis are described.

[0064] The output from the segmentation and separation function is put into a form such that A denotes the index set containing all detected / segmented vessels and S denotes the index set running over all detected vessels that belong to the standard vascular tree.

[0065] According to the above specified criteria, (S ⊆ A). Using this notation, a concrete realization of collateral vessel quantification requires: The number of detected collaterals is N C = |A|-|S|, where |A| denotes the cardinality of a given index set.

[0066] Cumulative collateral vessel length L C can be quantified as follows:

number

number

number

[0067] Cumulative collateral volume V Ccan be quantified as follows:

number

[0068] Instead of fitting a cross-sectional area (e.g., an ellipse), vessel segmentation can be based on a binary (voxelized) bit mask, and the collateral lumen volume is calculated by simply summing all voxels from index set A and subtracting all voxels from index set S, and converting the voxel counts to a physical volume measure using the spatial voxel resolution of the given acquisition.

[0069] The intervascular area (i.e., the space between blood vessels) can be quantified. This measure represents vascular density or coverage. For ease of calculation, we assume a known surface area A surf Projections of (O) onto a 2D surface O (e.g. for coronary vessels onto the myocardium) can be performed.

[0070] The vascular tree projection is calculated for each voxel of the vascular tree lumen bitmask by using the surface normal of the reference surface, resulting in an area: A vessel ≦A surf

[0071] In this embedded 2D space, the intervascular area A void =A surf -A vessel Or, blood vessel density D=A vessel / A surf You can calculate metrics such as:

[0072] The projection is obtained by considering an oriented segmentation mesh capturing, for example, the left ventricular myocardial epicardial surface, where its normal vector serves as the (local) projection direction for the mapping.

[0073] Thus, various possible parameters are set above. One or more of these parameters may be determined. Thus, the parameters may include the number of collateral vessels, the cumulative length of the collateral vessels, the (cumulative) collateral lumen volume, or a parameter related to the intervascular area, such as the net vessel density.

[0074] The parameters output from the above analysis (N C , L C , V C ) can be converted to a normalized scale by using the corresponding values ​​from the standard vascular tree or the entire vascular tree as a reference value. Then, for example,

number

number

[0075] In addition, the reference parameter values ​​(e.g., N C , L C , V C ) can be recorded from a healthy patient cohort and these reference values ​​can be used for normalization, where the normalized values ​​indicate deviation from the healthy patient standard set by the selected cohort.

[0076] Rather than normalizing parameters only to a typical healthy patient, the resulting parameter values ​​may be interpreted within the context of specific population statistics for a particular patient, which may be related to other clinical parameters, such as outcome, disease severity, etc.

[0077] To interpret the current set of parameter values ​​for a particular patient statistic, one approach is to explicitly state the patient's position within the overall distribution of patient properties and use the patient's parameters that are close to the current one. An implicit approach uses data-driven modeling or learning of statistics to classify patients into one of several groups.

[0078] The above analysis is based on 3D image data, such as 3D angiographic CT scan images or 3D magnetic resonance angiography (MRA) images, however the invention is also applicable to 2D X-ray angiographic images.

[0079] FIG. 10 shows an example of an interventional 2D angiogram after the complete and standard trees of the leg vasculature have been identified by the segmentation module.

[0080] The top diagram shows the complete tree A, the middle diagram shows the standard tree S, and the bottom diagram shows the remaining tree R=A\S. Figure 10 is for a patient with little or no collateral formation.

[0081] FIG. 11 shows an example of an interventional 2D angiogram after the complete and standard trees of the leg vasculature of a patient with moderate collateral formation have been identified by the segmentation module.

[0082] Again, the top diagram shows the complete tree A, the middle diagram shows the standard tree S, and the bottom diagram shows the remaining tree R=A\S.

[0083] In this case, the collateral quantification parameters can only be approximate due to the projective nature of the imaging modality which causes a foreshortening effect. The surface for the net density calculation is given directly by the detector plane.

[0084] The segmentation and collateral quantification steps can be enhanced using spectral or dynamic CT or X-ray scans.

[0085] FIG. 12 shows an example of an imaging system that can be used to provide images for analysis using the methods of the present invention.

[0086] The imaging device 100 in this description is an X-ray computed tomography (CT) scanner.

[0087] The imaging device 100 generally includes a stationary gantry 102 and a rotating gantry 104. The rotating gantry 104 is rotatably supported by the stationary gantry 102 and rotates about an examination region about a longitudinal, axial, or z-axis.

[0088] A patient support 120, such as a couch, supports an object or subject, such as a human patient, within the examination region. The support 120 moves the object or subject for loading, scanning, and / or unloading the object or subject. The support 120 is movable axially, i.e., along the z-axis or longitudinal axis. Moving the support changes the axial position of the rotating gantry relative to the support (and thus relative to the subject supported by the support).

[0089] A radiation source 108, such as an x-ray tube, is rotatably supported by the rotating gantry 104. The radiation source 108 rotates with the rotating gantry 104 and emits radiation that traverses an examination region 106.

[0090] A radiation responsive detector array 110 subtends an arc that spans an angle across the examination region 106 from the radiation source 108. The detector array 110 includes one or more rows of detectors extending along a z-axis direction to detect radiation traversing the examination region 106 and generate projection data indicative thereof.

[0091] Rotation of the gantry 104 changes the angular or rotational position of the scanner relative to the subject, and movement of the support along the z-axis changes the axial position of the scanner relative to the subject.

[0092] A typical scan is pre-set using a scan protocol. The scan protocol includes multiple scan parameters. The scan parameters define, among other things, the spatial extent of the scan relative to the axial and rotational axes of the scanner. For example, the scan parameters include the boundaries (i.e., start and end points) of the scan extent along one or more axes (e.g., one or both of the rotational and axial axes) of the imaging device. The scan extent defines the field of view (FOV) within which imaging data is collected during the scan. The scan parameters also typically include a number of other parameters, such as tube current, tube voltage, scan spatial resolution, scan temporal resolution, and / or fan angle. The resolution parameter can be defined by the rotational speed of the gantry 104 and the speed of axial movement of the support 120 through the gantry.

[0093] A general purpose computing system or computer serves as an operator console 112 and includes input devices 114, such as a mouse, keyboard, etc., and output devices 116, such as a display monitor. The console, the input devices, and the output devices form the user interface 30. The console 112 allows an operator to control the operation of the system 100.

[0094] A reconstructor 118 processes the projection data and reconstructs volumetric image data, which can be displayed via one or more display monitors of the output device 116.

[0095] The reconstructor 118 may employ filtered backprojection (FBP) reconstruction, low noise (image and / or projection domain) reconstruction algorithms (such as iterative reconstruction), and / or other algorithms. It is understood that the reconstructor 118 may be implemented via a microprocessor executing computer readable instructions encoded or embedded in a computer readable storage medium, such as a physical memory or other non-transitory medium. Additionally or alternatively, the microprocessor may execute computer readable instructions carried by carrier waves, signals, and other transitory (or non-transitory) media.

[0096] The reconstruction device 118 may incorporate a processor programmed with a computer program to perform the above-described method for analyzing the generated 3D CT scan images and performing an analysis of collateral vessels within the region of interest.

[0097] Vessel identification and collateral analysis may involve the use of trained neural networks or hand-crafted algorithms.

[0098] As mentioned above, the system uses a processor to process data. The processor may be implemented in a variety of ways using software and / or hardware to perform the various functions required. Typically, the processor employs one or more microprocessors that are programmed using software (e.g., microcode) to perform the required functions. The processor may be implemented as a combination of dedicated hardware to perform some functions and one or more programmed microprocessors and associated circuitry to perform other functions.

[0099] Examples of circuitry that may be used in various embodiments of the present disclosure include, but are not limited to, conventional microprocessors, application specific integrated circuits (ASICs), and field programmable gate arrays (FPGAs).

[0100] In various implementations, the processor may be associated with one or more storage media, such as volatile and non-volatile computer memory, such as RAM, PROM, EPROM, and EEPROM. The storage media may be encoded with one or more programs that, when executed on the one or more processors and / or controllers, perform the necessary functions. The various storage media may be fixed within the processor or controller, or may be transportable such that one or more programs stored thereon may be loaded into the processor.

[0101] Variations of the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure, and the appended claims. In the claims, the term "comprising" does not exclude other elements or steps, and singular elements do not exclude a plurality.

[0102] The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.

[0103] The computer program may be stored / distributed on any suitable medium, such as an optical storage medium or a solid-state medium, supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless communication systems.

[0104] It should be noted that when the term "adapted to" is used in the claims or description, it is intended to be equivalent to the term "configured to."

[0105] Any reference signs in the claims should not be construed as limiting the scope.

Claims

1. 1. A method for analyzing the vasculature of a subject, comprising: receiving an image of a region of interest of the subject; identifying a vascular tree present in the region of interest; - identifying major vessels within the identified vascular tree, the major vessels forming part of a standard tree of the major vessels; subtracting the identified major vessels from the identified vascular tree, thereby excluding the major vessels of the standard tree, to identify remaining vessels of the identified vascular tree, thereby identifying collateral vessels within the region of interest; performing an analysis of the collateral vessels; A method comprising:

2. The method of claim 1 , wherein identifying the vascular tree present in the region of interest comprises image segmentation and vascular centerline extraction.

3. The method of claim 1 , wherein identifying the major vessels comprises using a hierarchical rule-based classification.

4. The method of claim 1 , wherein performing the collateral analysis comprises determining parameters including the number of collateral vessels.

5. The method of claim 1 , wherein performing the collateral vessel analysis comprises determining parameters including a cumulative length of the collateral vessels.

6. The method of claim 1 , wherein performing the collateral analysis comprises determining parameters including collateral lumen volume.

7. The method of claim 4 , wherein performing the collateral analysis comprises normalizing the parameters to a reference value.

8. 8. The method of claim 7, wherein performing the collateral analysis comprises normalizing the parameter to a reference value of the parameter corresponding to a healthy subject or to a reference value of a patient group associated with the particular subject.

9. The method of claim 1 , wherein the received image is a 3D image.

10. The method of claim 9 , wherein performing the collateral vessel analysis comprises determining net vessel density calculated using image projections onto a reference surface.

11. The method of claim 1 , wherein the received image is a 2D image.

12. A computer program comprising computer program code means for carrying out the method of any one of claims 1 to 11 when the computer program is run on a computer.

13. A processor programmed with the computer program of claim 12.

14. an imager for acquiring an image of a region of interest of the subject; 14. The processor of claim 13 for analyzing the image to perform an analysis of the collateral vessels within the region of interest; an imaging system comprising:

Images