Method and device for correlating sets of cardiovascular data - Patents.com

Abstract

There is no one-size-fits-all data collection technique for body vessels, vessel wall anomalies, and materials surrounding the vessels. Thus, data acquired using various data collection methods will be combined so that all collectable data can be collected in one model or in one visualization. This is difficult, particularly when some data is collected from outside the body, such as CT or X-ray, and other data is collected inside the vessel, such as IVUS or OCT. In IVUS and OCT, geometric data such as bends in the vessel are not visible. By identifying points of interest that relate to specific features of the vessel, such as side branches, data acquired using different collection methods can be aligned by associating points of interest in two or more datasets that identify the same feature across two or more datasets, and using that as a basis for merging the data to provide image data of the vessel and its surrounding materials.

Description

[Technical field]

[0001] Various aspects and variations thereof relate to associating externally collected data in electronic format regarding a geometric representation of the vascular structure with intravascularly collected data of the vascular structure, including data regarding material outside and near the blood vessel, including within the vessel wall. [Background technology]

[0002] Different methods of imaging allow different ways of identifying vascular anatomical structures, such as cardiovascular or cerebral vasculature. The vascular lumen can be imaged, for example, using X-rays and contrast agents injected into the blood vessel, using computed tomography (CT) scans, using intravascular ultrasound (IVUS), using optical coherence tomography (OCT) and other methods. Some of these methods are suitable for collecting data that allows a three-dimensional model of the vascular structure to be reconstructed, while others are suitable for collecting data that allows the identification of specific materials that surround the lumen of the blood vessel, either within the vessel wall or just outside the vessel wall. With certain data collection methods, types of materials can also be identified, such as calcified material, lipid material, necrotic material, or other types of material. Summary of the Invention [Problem to be solved by the invention]

[0003] Although detailed data on different characteristics of the vascular structure can be obtained using different data collection methods, a problem is identified that there is no one-size-fits-all data collection technique. Thus, data acquired using various data collection methods will be combined so that all collectable data can be collected in one model or in one visualization. This can be difficult, particularly when some data is collected from outside the body, such as CT or X-ray, and other data is collected inside the vessel, such as IVUS or OCT. A problem with IVUS or OCT is that it does not show geometric data such as vessel curvature, which makes it difficult to visualize the identified material in the vessel wall in combination with the X-ray data. [Means for solving the problem]

[0004] In response, a first aspect provides a method in an electronic computing device for associating a first set of data including a geometric representation of a blood vessel in a mammalian and particularly human body with a second set of data related to the structure of the blood vessel, the second set of data being collected within the blood vessel. The method includes identifying a first point of interest of the blood vessel at a first location along a length of the blood vessel based on the first set of data, and identifying a second point of interest of the blood vessel at a second location along a length of the blood vessel based on the second set of data. The method further includes matching the first set of data to the second set of data based on the first and second locations on the one hand and the first and second points of interest on the other hand, identifying a material surrounding the lumen based on the second set of data and obtaining at least one first material location of the identified material, and associating the first material location of the identified material with a second material location in the second set of data, relative to the length of the blood vessel, based on the matching. A third set of data is then provided, the third set of data including a combination of a geometric representation of the vessel and a location of the identified material relative to the geometric representation of the vessel.

[0005] The first set of data may be collected using a CT scan, an X-ray scan, etc., or a combination thereof. When using X-ray, two X-ray images may be collected under an angle relative to each other, for example using a contrast agent, and from these two images a three-dimensional model of the blood vessel under investigation may be constructed. In another example, only one X-ray image is provided as a two-dimensional image. The second set of data may be collected using intravenous ultrasound (IVUS), optical coherence tomography (OCT), other similar techniques, or a combination thereof. Such data does not include data regarding the shape of the blood vessel in the sense that the data is essentially collected relative to the centerline of the blood vessel, which is followed by a probe moving through the blood vessel. This means, for example, that bends or curvatures of the blood vessel cannot be detected from the collected data.

[0006] The points of interest may vary based on the method for data collection used. Deviations in the lumen with respect to size, shape, area, etc., or combinations thereof, may be used as points of interest or vascular landmarks that characterize a particular location of the vessel. Additionally or alternatively, the location of the vessel's branches, material detected within the vessel's wall or just outside the vessel may also be used to identify points of interest. The actual points of interest used to match the two sets of data may depend on the data collection method used. The points of interest must be detectable using both data collection methods. For example, IVUS may be used to detect calcified material within the vessel wall, just like a CT scan, which is not possible with X-ray. X-ray shows side branches well, as do OCT and IVUS. X-ray also provides better resolution of the lumen than with CT, making X-ray data suitable for matching with OCT or IVUS based on lumen shape deviations. CT data may also be combined with X-ray data. In another implementation, three or more sets of data may also be combined using this embodiment.

[0007] The two sets of data are matched by identifying one or more points of interest in each of the sets that relate to the same physical or vascular feature, such as a side branch or calcified material in the vessel wall. This allows one or more first locations in the first set of data to be matched to second locations in the second set of data. Locations between the points of interest may then be registered in both data sets.

[0008] Finally, this combined data of the third set of data may be presented in a two- or three-dimensional model, depending on whether the first set of data provides two- or three-dimensional data, along with the blood vessel itself, as well as along with any material outside the lumen, either in the vessel wall or as perivascular material.

[0009] In one example, a first point of interest is associated with a first geometric feature of a first lumen of a blood vessel at a first location, and a second point of interest is associated with a second geometric feature of a second lumen of the blood vessel at a second location. Such geometric features may be shape, area, size, symmetry, etc., as well as their deviations from one or more reference values, side branches, bends or curvatures, etc., or combinations thereof. Depending on the data collection method used, different types of geometric features may be identified and employed.

[0010] In another example, identifying the first point of interest in the first lumen includes identifying a first deviation location of a first deviation along the first length of the vessel where the lumen deviates from the predetermined shape by more than a first predetermined threshold based on the first set of data, and identifying the second point of interest in the second lumen includes identifying a second deviation location of a second deviation along the second length of the vessel where the lumen deviates from the predetermined shape by more than a second predetermined threshold based on the second set of data, and matching further includes matching the first set of data to the second set of data based on the first deviation location and the second deviation location on the one hand and the first deviation and the second deviation on the other hand. Based on the data collection method used, the specific deviations of the geometric parameters may be conveniently calculated and processed for use in matching.

[0011] A further example further includes obtaining data regarding a first angular position of the first point of interest at a first location relative to the lumen and a second angular position of the second point of interest at a second location relative to the lumen, in this example, the matching is further based on the first angular position and the second angular position. The angular position relative to the lumen may be determined from a centerline of the lumen or another reference point within the lumen, and the actual angle is the angle in a plane perpendicular to the centerline, relative to the reference. Such references may be predetermined or arbitrary, so long as they are set and stored for later use.

[0012] In yet another example, the first deviation is determined based on a first difference between the first distance criterion and a first distance between the centerline of the vessel and the inner wall of the vessel at the first location, and the second deviation is determined based on a second difference between the second distance criterion and a second distance between the centerline of the vessel and the inner wall of the vessel at the second location. Thus, the deviation is based on the actual distance between the centerline and the vessel wall distance and the reference distance. The actual distance may be smaller, for example, in the case of a stenosis, or may be larger at the location of a side branch of the vessel.

[0013] In yet a further example, a first point of interest is determined at a first location at a first angular position relative to the centerline, and a second point of interest is determined at a second location at a second angular position relative to the centerline. Because the angular positions of the points of interest are known in both sets of data, the data can be matched to achieve improved matching, particularly of three-dimensional views. In particular, this can provide specific angular positions related to vessels such as bends, curves, and side branches, as well as appropriate angular positions of identified material related to other data.

[0014] In yet another example, the first point of interest is determined by determining a deviation at a first location where a first value of a first parameter in the first set of data differs from a first predefined value by more than a first threshold, and the second point of interest is determined by determining a deviation at a second location where a second value of a second parameter in the second set of data differs from a second predefined value by more than a second threshold. This allows a certain margin to be set for the deviation. Predefined may be understood as determined at the time the method is designed. Alternatively or additionally, predefined may be understood as determined before determining the difference value. Thus, the predefined threshold may be determined during the execution of the method, but before the difference is calculated.

[0015] In another example, the first threshold is at least one of a fixed value and a relative value depending on at least one of the first value, the first predetermined value, and the first threshold, and the second threshold is at least one of a fixed value and a relative value depending on at least one of the second value, the second predetermined value, and the second threshold, thereby allowing for dedicated evaluation of the data for each subject.

[0016] In a further example, determining the first point of interest includes identifying a first branch of a blood vessel, and determining the second point of interest includes identifying the first branch of a blood vessel. A branch of a blood vessel may be relatively easily detected regardless of the data collection method used. Thus, a branch is a suitable example for use as a vascular point of interest or landmark. Alternatively or additionally, a stent may be used as a landmark.

[0017] In another example, identifying the material includes identifying material within a wall of a blood vessel. With IVUS and CT, calcified material can be detected within the blood vessel wall. With IVUS, the calcified material can be seen by the shadow it casts, and with typical CT visualization, the calcified material within the blood vessel wall appears white.

[0018] In yet another example, the second set of data is acquired using at least one of intravascular ultrasound and optical coherence tomography. Such data acquisition methods are known and may even be used together using a combined probe. Data from both data acquisition methods may then be further combined with the geometric data.

[0019] Yet further examples further include displaying at least a portion of the third set of data by displaying at least a portion of a geometric, preferably three-dimensional, representation of the blood vessels and the identified material associated with the blood vessels, although this may be two-dimensional. Such visualization allows a physician to assess the condition of the blood vessels and any abnormalities that may give rise to medical problems. Thus, such representations may provide further assistance to the physician in setting a diagnosis.

[0020] Yet another example includes obtaining a number of first distances at a first location at a number of angular locations relative to the centerline, obtaining a number of second distances at a second location at a number of angular locations relative to the centerline, determining a first deviation at the first location if a statistical parameter of the number of first distance values ​​matches a first predetermined statistical condition, and determining a second deviation at the second location if a statistical parameter of the number of second distance values ​​matches a second predetermined statistical condition. This example provides a practical implementation for determining deviation of a lumen structure.

[0021] In yet another example, the first statistical parameter is based on at least one of a mean, a median, a standard deviation, a maximum, and a minimum of the number of first distances, and the second statistical parameter is based on at least one of a mean, a median, a standard deviation, a maximum, and a minimum of the number of second distances. This example provides a practical implementation for determining deviations in the lumen structure.

[0022] In a further example, the first statistical criterion is met if a predetermined amount of the first distance differs from the first statistical parameter by more than a first statistical threshold, and the second statistical criterion is met if a predetermined amount of the second distance differs from the second statistical parameter by more than a second statistical threshold. This example provides a practical implementation for determining deviations in lumen structure.

[0023] In yet another example, the first statistical criterion is met if at least one value of the first distance differs from the first statistical parameter by more than a third statistical parameter threshold, and the second statistical criterion is met if at least one value of the second distance differs from the second statistical parameter by more than a fourth statistical parameter threshold. This example provides a practical implementation for determining deviations in lumen structure. [A repeat of claim 1 follows below, along with explanations and advantages]

[0024] The various embodiments and variations thereof will now be further described in conjunction with the drawings. [Brief description of the drawings]

[0025] [Figure 1] FIG. 2 illustrates an example of a device according to a second embodiment. [Diagram 2] FIG. [Figure 3A] FIG. 1 illustrates a three-dimensional geometric representation of the cardiovascular structure. [Figure 3B] FIG. 1 illustrates a visualization of data acquired by an intravascular data collection method. [Figure 4A] FIG. 1 is a first cross-sectional view of a coronary artery. [Figure 4B] FIG. 2 is a second cross-sectional view of a coronary artery. [Diagram 5] FIG. 1 illustrates a visualization of a three-dimensional geometric representation of a cardiovascular structure with lipid material. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0026] FIG. 1 illustrates an electronic medical data acquisition and processing system 100 as an example of the second embodiment. The system 100 or a portion thereof may be found in a cardiac catheterization laboratory in a clinic or hospital. The system 100 includes an X-ray image acquisition module including a first X-ray source 126 and a second X-ray source 128, a first X-ray detector 122 configured to receive X-ray data from the first X-ray source 126, and a second X-ray sensor 124 configured to receive X-ray data from the second X-ray source 128. The first X-ray source 126, the second X-ray source 128, the first X-ray detector 122, and the second X-ray sensor 124 are positioned to acquire images of a cardiovascular structure 180 under an angle with respect to each other. The angle is preferably between 25° and 45°, more preferably between 30° and 40°.

[0027] The first X-ray detector 122 and the second X-ray detector 124 are connected to a data acquisition module 116 of the electronic computing device 110. The electronic computing device further comprises a processing unit 112, a storage module 114, and a peripheral I / O controller 118. The processing unit 112, which may be implemented as a microprocessor, microcontroller, or other electronic data processing device, is configured to control various parts of the electronic computing device 110 and the system 100, and is configured to perform methods according to the first aspect and implementations thereof.

[0028] The storage module 114 is configured to store therein, for example, data collected by the computing device 110 from various other parts of the system 100, either directly or after processing by the processing unit 112. The storage unit 114 is at least partially implemented as a non-transitory storage medium and is further configured to store computer-executable code that enables the processing unit 112 to perform the method according to the first aspect and its implementations.

[0029] The system 100 includes a data collection probe 148 for acquiring data regarding a wall 184 of a coronary artery 182 of a cardiovascular structure 180 as an example of a tubular vessel or blood vessel in general. The data collection probe 148 may be inserted into the coronary artery 182 via a catheter 146 that is inserted into the body of a mammal, such as a human. Furthermore, the tip of the coronary artery catheter 146 is placed at the ostium of the coronary artery 182 under examination. The data collection probe 148 is an ultrasound probe in this variant and is configured to acquire data regarding the internal structure of the coronary artery 182 as well as the wall 184.

[0030] FIG. 1 shows a wall 184 containing lipid material 186 at multiple locations of a coronary artery 182 detected using intravascular ultrasound (IVUS) imaging. In another instance, or in another location of the same instance, lipid material 186 is present on the outside of the coronary artery 182 adjacent to the outside of the vessel wall 184 as perivascular adipose tissue (PVAT). Thus, lipid material 186 is present either within the arterial wall 184 or just outside the wall 184, around the lumen of the coronary artery. Alternatively or additionally, the data collection probe 148 may be configured to acquire data using optical coherence tomography. Furthermore, the catheter 146 may be used to insert a contrast agent 150, or another dye such as saline, into the coronary artery 182 or another blood vessel of the body, which allows the coronary artery 182 to be visible using an x-ray detector.

[0031] The peripheral I / O controller 118 is configured to connect the computing device 110 and its various components to input devices, such as a keyboard 142 or a touch screen, for receiving data, such as user input. The peripheral I / O controller 118 is configured to connect the computing device 110 and its various components to output devices, such as an electronic display 144 and other output devices, configured to provide data to a user regarding processed or unprocessed data received by the computing device 110.

[0032] As shown in FIG. 1, the catheter 146 and the data collection probe 148 are inserted into a coronary artery 182. The coronary artery 182 may have a stenosis 190 present. The stenosis 190 may be caused by plaque, which may be composed of various materials such as calcium, lipid material, or other plaque components 186. This stenotic area results in a narrowing of the coronary artery 182, which leads to a pressure drop in the stenotic area. The stenosis 190 may result in a cross-section of the coronary artery 182 that is asymmetric, or in any case not circular or elliptical. The cardiovascular structure 180 shown in FIG. 1 may be a hypothetical structure and does not necessarily represent an actual anatomical structure.

[0033] Further functionality of the system 100 and parts thereof described above will be further described in conjunction with the flowchart 200 shown in FIG. 2. The procedures shown in the flowchart 200 are executed by the system 100 and in particular the electronic computing device 110 controlled by the processing unit 112. To provide this functionality, the processing unit 112 may be programmed with a computer program product including computer executable code. The computer program product may be stored in the storage unit 114 as an electronic memory, which may be a non-transitory memory. The various parts of the flowchart 200 are briefly summarized below. The various steps may be interchanged in order of execution or performed in parallel, unless expressly specified otherwise. 202 Start of procedure 204 Acquire 3D vascular structure data 206 Determine the lumen radius as an angular position at a location along the centerline 208 Have all positions been performed? 210 Go to next angle position 212 Determine Lumen Area 214 Processing Radius and Area Data 216 What is the deviation of the processed data? 218 Registering deviation data and location 220 Have all locations been done? 222 Go to next location 224 Acquire intravascular data 226 Determine the lumen radius as an angular position at a location along the centerline Determine the material in the vessel wall at 228 angular positions and store the data 230 Have all positions been performed? 232 Go to next angle position 234 Determine Lumen Area 236 Processing Radius and Area Data 238 What is the deviation of the processed data? 240 Register deviation data and location 242 Have all locations been done? 244 Go to next location Matching deviation data for 246 data sets 248 Locations Correlate 3D Vascular Structure Data and IV Data 250 Linking the determined material data to 3D vascular structure data 252 Combining material data and 3D vascular structure data 254 Displaying Combined Data 256 End procedure

[0034] The procedure starts at terminator 202 and proceeds to step 204, where data regarding the three-dimensional vascular structure of the cardiovascular structure 180 is acquired. This data may be acquired using the system 100 (FIG. 1) or otherwise, for example, using a computed tomography (CT) scan. FIG. 3A shows a three-dimensional representation of the cardiovascular structure 180, where a coronary artery 182 has a vessel wall 184 and a branch vessel 188. In step 206, at a first location in the coronary artery 182, a radius of the lumen of the first location is determined. Prior to this determination, a centerline may be determined for the coronary artery 182 described by the three-dimensional model in the acquired data set. The centerline thus determined may be used to determine a radius starting from the centerline and leading to the inner wall 184 of the coronary artery 182.

[0035] FIG. 4A shows a cross-section of coronary artery 182 at a location designated "4A" in FIG. 3A and FIG. 3B, and FIG. 4B shows a cross-section of coronary artery 184 at a location designated "4B" in FIG. 3A and FIG. 3B. Thus, FIG. 4A shows a cross-section of coronary artery 184 at a different location than FIG. 4B. As can be seen in both FIG. 4A and FIG. 4B, the radius of coronary artery 182 may be different at different angular positions. FIG. 4A shows a cross-section at a stenosis location of coronary artery 182. FIG. 4B shows a cross-section at a location where a branch vessel 188 branches off from coronary artery 182.

[0036] Typically, the cross-section of a coronary artery has a substantially circular cross-section. In such cases, the coronary artery has a roughly rotationally symmetric shape. The radius of the cross-section may be different at different angular positions about the centerline, but the curvature of the vessel inner wall 184 or lumen is generally concave when viewed from the centerline. Furthermore, the radius of different angular positions varies, but gradually and within certain boundaries.

[0037] On the other hand, in some cases, the curvature of the lumen may be convex from the centerline, particularly when large abnormalities are present in the vessel wall, and in such cases, but also in other cases, the lumen radius may be significantly different at different angular positions and may not necessarily change gradually but rather abruptly.

[0038] As can be seen in Figures 4A and 4B, for abnormalities such as stenosis and bifurcation, the cross sections may have different shapes that are not rotationally symmetric. Furthermore, there may be significant changes in radius at different angular positions starting from the centerline.

[0039] As explained above, in step 206, the radius of the lumen of the coronary artery 182 is determined at a particular location along the length of the coronary artery 182, specifically along the centerline. Once that is done, the data is stored and the procedure continues to step 208 to check whether all angular positions have been checked to determine the radius at that particular angular position. The angular positions may be evenly spaced, for example 36 measurements at 10° intervals, or 10 measurements at 36° intervals, or any other number of measurements. If not all positions have been processed, the procedure branches back to step 206 via step 210 where the next angular position is selected.

[0040] When all angular positions have been processed with respect to the radius values ​​at each position for a location, the lumen area is determined for the active locations of the coronary artery 182 in step 212. The data regarding the determined radius and area at a location is then processed in step 214. Processing may include calculating statistical parameters such as the mean, median, standard deviation, minimum, maximum, etc., or any combination thereof, determining and removing outliers, correcting for measurement errors such as noise or offset, other processing, or any combination thereof.

[0041] In step 216, the cross-section is checked for deviations. More specifically, in step 216, it may be checked whether the radius values ​​are all within certain boundaries, the certain boundaries having a predetermined fixed value based on the radius values, based on values ​​obtained from the processing of step 214, other values, or any combination thereof. For example, if the value of one of the radii varies from the median or average value by more than 10% of the median or median, or by more than a number of standard deviations that are approximately 1, then the cross-section may be determined to be a deviating cross-section.

[0042] The underlying causes of cross-sectional changes and cross-sectional deviations can be numerous. An obvious cause of cross-sectional dimension deviations are side branches. Other causes can be calcified areas with calcified material in the vessel wall. Other materials in the vessel wall, such as lipid material, can also cause cross-sectional changes. Thus, side branches can also be considered as lumen deviations.

[0043] The presence and / or location of side branches need not be determined using the processes described above, but may additionally or alternatively be detected as geometric luminal features, for example using image recognition. Apart from the presence and location of side branches, the presence and location of other landmark features of the coronary vessels or other vessels under examination may also be detected or possibly identified as geometric luminal features, and thus used to co-register the data, as described below.

[0044] If the cross section is determined to be deviating, the location of the cross section is registered in step 218. Registration may include registering additional data such as the lumen area, the position along the centerline of the coronary artery 184, the values ​​of the radius at various angular positions, etc., or combinations thereof. The procedure then continues to step 220. If the cross section is not determined to be deviating, the procedure continues from step 216 towards step 220.

[0045] In step 220, it is checked whether all locations along the length of the coronary artery 184 have been processed for radius and area determination, as well as for deviation determination. The number of locations may be determined based on the required accuracy, and may be determined as the amount of locations per millimeter or centimeter or other measure of length. Additionally or alternatively, the number of locations may be set and the distance between locations may be set based on the number of locations and the length of the coronary artery 182. If not all locations have been processed, the procedure branches back to step 206 via step 222, where the next location is selected. If all locations have been processed, the process proceeds to step 224.

[0046] In step 224, intravascularly collected data is obtained regarding the coronary artery 182, as described above. A data set including intravascularly collected data does not include the three-dimensional vessel structure illustrated by FIG. 3A, i.e., how the vessel curves along its length. Rather, by the nature of the data collection process, the data collected at the vessel is provided along a line where the data collection probe 148 is pulled or pushed through the coronary artery 182. This is illustrated in FIG. 3B, which shows a centerline 190 indicating the center of the coronary artery 182.

[0047] The centerline 190 may be determined in a variety of ways, for example, the center may be determined in multiple subsequent cross-sectional views of the coronary artery 182 and the centerline determined as a line following these centers. Alternatively, a moving average of the centers may be determined based on 2, 3, 5, 10, 20 or another large number of cross-sectional views. This is also true for the data shown by FIG. 3A. Various methods for determining the centerline of a vessel are known in the art, and all of these may be employed.

[0048] In step 226, the radius of the lumen of the coronary artery 182 is determined based on the intravascular collected data at a particular angular position. This may be done as described above in step 206. Then, in step 228, the intravascular collected data is processed to determine whether a particular material is present in the wall of the coronary artery 182. Such material may be lipid material, calcified material, necrotic tissue, etc., or a combination thereof.

[0049] For such material, the distance from the centerline is determined for a minimum distance to the centerline 190, as well as a maximum distance to the centerline 190. Because such data is available for multiple angular positions along the length of the coronary artery 182, and specifically at multiple locations along the centerline 190, the size and shape of the lipid material 186 can be reconstructed relative to the centerline, relative to the inner wall 184, relative to another feature of the coronary artery 182, or a combination thereof.

[0050] In step 230, it is determined whether all angular positions along the centerline have been covered to determine the radius and any material in or outside the vessel lumen, within the vessel wall. If all positions have not been covered, the process branches back to step 226 via step 232 where the next angular position is selected. If all angular positions have been covered to determine the radius, the procedure continues to step 234.

[0051] In step 234, the lumen area is determined based on the intravascular acquisition data, similar to the operations of step 214. Thereafter, in step 236, the radius data, area data, and detected material data are processed, which may be as described in connection with step 214. Thereafter, in step 238, it is determined whether the applicable cross-section is a deviating cross-section based on the raw radius and area data, based on the results of the processing, and based on other data, such as a predetermined threshold.

[0052] For example, the cross section shown by FIG. 4A may be characterized as deviating if there is a large change in radius. In this example, the deviated shape of the lumen may be caused by material in the wall of the coronary vessel 182. Note that the cross section shown by FIG. 4A is not rotationally symmetric. Such features may also be used to determine whether the cross section is deviating for one or more of these reasons. The cross section shown by FIG. 4B at the location of the branch vessel 118 may also be considered deviating. Different criteria may be employed, such as a sudden increase in volume due to bifurcation, or a large change in the value of the radius. If the cross section at a location is considered deviating, the data is stored in step 240, similar to the operation of step 218. The process continues to step 242.

[0053] If no deviations are determined, the procedure continues directly to step 242. In step 242, it is checked whether all locations along the centerline 190 have been covered, similar to step 220. If all locations have not been evaluated, the procedure branches back to step 226 to select the next location along the centerline, similar to step 222. If all locations have been processed, the procedure continues to step 246.

[0054] In step 246, the deviation data collected as described above for the three-dimensional shape data and for the intravascular acquisition data are matched. In this variant, the matching is performed in particular by evaluating the geometric characteristics of the cross-sections found to be deviating. In one variant, the radii at multiple positions of a specific location of the deviating cross-sections acquired at the deviating location of the first data set, i.e. the geometric three-dimensional representation, are compared with the radii at multiple positions of the deviating location of the second data set, comprising the intravascular acquisition data.

[0055] If the patterns of radii at the locations in the two sets are found to differ by less than a certain threshold, then both sets of locations may be considered to match. This step may be performed on the data at all locations as well, but it will be apparent that by matching only the locations that deviate, the processing effort is reduced. Second, matching a substantially rotationally symmetric elliptical or circular cross-section for a particular location is generally more complicated than for cross-sections with anomalies.

[0056] However, if sufficient processing power and appropriate pattern recognition and data comparison algorithms are available, it is possible to match a set of data having data relating to a three-dimensional representation of the cardiovascular structure 180 with a set of data having data acquired intravascularly. With recent developments in artificial intelligence, techniques are available for matching sets using all or at least most of the data from both sets.

[0057] Once matching slices are identified in both data sets, the first and second sets of data may be correlated at step 248. In particular, because location-by-location angular data is available for both sets, not only may the locations of both sets be correlated, but locations of particular data in one set may be correlated with data in another set. For example, as described above, the angular position of lipid material 186 relative to centerline 190 may be determined based on intravascular collected data.

[0058] By relating the shape or radius of the cross-section with respect to the angular position relative to the centerline, both sets of data can also be related with respect to the angular position. In this way, it is possible to determine the location of the lipid material 186 in the three-dimensional geometric representation of the cardiovascular structure 180, particularly with respect to the coronary artery 182 and particularly with respect to the length of its centerline, but also to reconstruct the angular position of the lipid material 186 with respect to the coronary artery 182.

[0059] The reconstruction may be performed in step 250. Based on the association in step 252, the data of both data sets may be combined in step 254. In particular, data relating to lipid material 186 of the walls of coronary vessels 182 available in the second data set may thus be combined with data of the first data set providing a three-dimensional representation of the cardiovascular structure 180.

[0060] The result is shown in FIG. 5, which shows a three-dimensional representation of the cardiovascular structure 180 with the lipid material 186 in its actual longitudinal and radial position relative to the coronary artery 182. This structure may be displayed in various ways in step 254. FIG. 5 shows a three-dimensional display of the merged data. Alternatively or additionally, a two-dimensional view may be provided, for example a two-dimensional geometrical view of the cardiovascular structure 180 with the lipid material 186 or other material, such as calcified material, in its actual longitudinal and / or radial position relative to the coronary artery 182. Alternatively or additionally, a cross-sectional view of the vessel may be provided with the entire vessel wall with any material present therein, and selected perivascular material, if present. After display, the procedure ends at terminator 256.

[0061] In summary, there is no one-size-fits-all data collection technique for body vessels, vessel wall anomalies, and per-vascular material. Thus, data acquired using various data collection methods will be combined so that all collectable data can be collected in one model or in one visualization. This is difficult, particularly when some data is collected from outside the body, such as CT or X-ray, and other data is collected inside the vessel, such as IVUS or OCT. In IVUS and OCT, geometric data such as bends inside the vessel are not visible. By identifying points of interest that relate to specific features of the vessel, such as side branches, data acquired using different collection methods can be aligned by associating points of interest in two or more datasets that identify the same feature across datasets, and using that as a basis for merging the data to provide the vessel and its surrounding material in the image data. [Explanation of symbols]

[0062] 100 Systems 110 Electronic Computing Devices 112 Processing Unit 114 Storage Module 116 Data Collection Module 118 Peripheral I / O Controller 122 First X-ray detector 124 Second X-ray sensor 126 The first X-ray source 128 Second X-ray source 142 Keyboard 144 Electronic Display 146 Catheter 148 Data Collection Probes 150 Contrast Agent 180 Cardiovascular Structure 182 Coronary Arteries 184 Blood vessel wall 186 Lipid substances 188 branch blood vessels 190 Stenosis

Claims

1. 1. A method in an electronic computing device for execution by or performed by said electronic computing device of associating a first set of data comprising a geometric representation of a blood vessel in a mammalian, and in particular a human, body with a second set of data relating to a structure of said blood vessel, said second set of data acquired within said blood vessel, comprising: identifying a first point of interest in the vessel at a first location along a length of the vessel based on the first set of data; identifying a second point of interest in the vessel at a second location along a length of the vessel based on the second set of data; matching said first set of data to said second set of data based on said first location and said second location on the one hand and said first point of interest and said second point of interest on the other hand; identifying a material surrounding the lumen of the blood vessel based on the second set of data and obtaining at least a first material location of the identified material in the first set of data; associating the first material location of the identified material in the first set of data with a second material location in the second set of data relative to the length of the blood vessel based on the matching; providing a third set of data including a combination of the geometric representation of the blood vessel and a location of the identified material relative to the geometric representation of the blood vessel; A method comprising:

2. 2. The method of claim 1, wherein the first point of interest is associated with a first geometric feature of one or more first lumens of the blood vessel at the first location and the second point of interest is associated with a second geometric feature of one or more second lumens of the blood vessel at the second location.

3. identifying a first point of interest in the first lumen includes identifying a first deviation location along a first length of the blood vessel based on the first set of data, where the lumen deviates from a predetermined shape by more than a first predetermined threshold; identifying a second point of interest in the second lumen includes identifying, based on the second set of data, a second deviation location along a second length of the blood vessel where the lumen deviates from a predetermined shape by more than a second predetermined threshold; said matching further comprising matching said first set of data to said second set of data based on said first deviation location and said second deviation location on the one hand and said first deviation and said second deviation on the other hand; 3. The method according to claim 1 or 2.

4. obtaining data relating to a first angular position of the first point of interest at the first location relative to the lumen and a second angular position of the second point of interest at the second location relative to the lumen; Further comprising: The method of claim 1 , wherein the matching is further based on the first angular position and the second angular position.

5. the first deviation is determined based on a first difference between a first distance between a centerline of the vessel and an inner wall of the vessel at the first location; the second deviation is determined based on a second difference between a second distance between a centerline of the vessel and an inner wall of the vessel at the second location. A method according to claim 3 or claim 4 depending thereon.

6. the first point of interest is determined at the first location at a first angular position relative to the centerline; the second point of interest is determined at the second location at a second angular position relative to the centerline; 6. The method according to any one of claims 1 to 5.

7. the first point of interest is determined by determining a deviation at the first location where a first value of a first parameter included in the first set of data differs from a first predetermined value by more than a first threshold; the second point of interest is determined by determining a deviation at the second location where a second value of a second parameter included in the second set of data differs from a second predetermined value by more than a second threshold value.

7. The method according to any one of claims 1 to 6.

8. the first threshold value is at least one of a fixed value and a relative value depending on at least one of the first value, the first predetermined value, and the first threshold value; the second threshold value is at least one of a fixed value and a relative value depending on at least one of the second value, the second predetermined value, and the second threshold value; The method of claim 7.

9. determining the first point of interest includes identifying a first branch of the blood vessel; determining the second point of interest includes identifying the first branch of the blood vessel; 9. The method according to any one of claims 1 to 8.

10. 10. The method of claim 1, wherein the step of identifying a substance comprises identifying a substance within a wall of the blood vessel.

11. 11. The method of claim 1, wherein the second set of data is acquired using at least one of intravascular ultrasound and optical coherence tomography.

12. 12. The method of claim 1, further comprising displaying at least a portion of the third set of data by displaying at least a portion of a three-dimensional representation of the blood vessel and the identified material associated with the blood vessel.

13. obtaining, at the first location, a number of first distances at a number of angular locations relative to the centerline; obtaining, at the second location, a number of second distances at a number of angular locations relative to the centerline; determining the first deviation at the first location if a statistical parameter of the multiple first distance values ​​matches a first predetermined statistical condition; determining the second deviation at the second location if a statistical parameter of the multiple second distance values ​​matches a second predetermined statistical condition; 12. The method according to any one of claims 6 to 11 depending on claim 5, further comprising:

14. the first statistical parameter is based on at least one of a mean, a median, a standard deviation, a maximum, and a minimum of the plurality of first distances; the second statistical parameter is based on at least one of a mean, a median, a standard deviation, a maximum, and a minimum of the multiple second distances; The method of claim 13.

15. a first statistical criterion is met if a predetermined amount of the first distance differs from the first statistical parameter by more than a first statistical threshold; a second statistical criterion is met if a second distance differs from the second statistical parameter by more than a second statistical threshold. The method of claim 14.

16. the first statistical criterion is met if at least one value of the first distance differs from the first statistical parameter by more than a third statistical parameter threshold; the second statistical criterion is met if at least one value of the second distance differs from the second statistical parameter by more than a fourth statistical parameter threshold. The method of claim 15.

17. 1. A device configured to associate a first set of data comprising a geometric representation of a blood vessel in a mammalian, and particularly a human, body with a second set of data relating to a structure of said blood vessel, said second set of data being collected within said blood vessel, said device comprising: identifying a first point of interest in the vessel at a first location at a first position along a length of the vessel based on the first set of data; identifying a second point of interest in the vessel at a second location at a second position along a length of the vessel based on the second set of data; matching the first set of data to the second set of data based on the first location and the second location on the one hand and the first point of interest and the second point of interest on the other hand; identifying a material surrounding the lumen based on the second set of data and obtaining at least a first material location of the identified material; associating the first material location of the identified material with a second material location in the second set of data relative to the length of the blood vessel based on the matching; providing a third set of data including a combination of the geometric representation, e.g., a three-dimensional representation, of the blood vessel and a location of the identified material relative to the geometric representation of the blood vessel; A device comprising a processing unit configured to perform the steps of:

18. 1. A computer program product comprising computer executable code configured to cause a processing unit of a processing device, when programmed according to said computer executable code, to execute a method of associating a first set of data comprising a geometric representation of a blood vessel in a mammalian, and in particular a human, body with a second set of data relating to a structure of the blood vessel, the second set of data being collected within the blood vessel, the method comprising: identifying a first point of interest in the vessel at a first location at a first position along a length of the vessel based on the first set of data; identifying a second point of interest in the vessel at a second location at a second position along a length of the vessel based on the second set of data; matching said first set of data to said second set of data based on said first location and said second location on the one hand and said first point of interest and said second point of interest on the other hand; identifying material surrounding the lumen based on the second set of data and obtaining at least a first material location of the identified material; associating the first material location of the identified material with a second material location in the second set of data relative to the length of the blood vessel based on the matching; providing a third set of data including a combination of the geometric representation of the blood vessel and a location of the identified material relative to the geometric representation of the blood vessel; 2. A computer program product comprising:

19. 1. A non-transitory medium carrying a computer program product comprising computer executable code configured to cause a processing unit of a processing device, when programmed according to said computer executable code, to execute a method of associating a first set of data comprising a geometric representation of a blood vessel in a mammalian, and in particular a human, body with a second set of data relating to a structure of the blood vessel, the second set of data being collected within the blood vessel, the method comprising: identifying a first point of interest in the vessel at a first location at a first position along a length of the vessel based on the first set of data; identifying a second point of interest in the vessel at a second location at a second position along a length of the vessel based on the second set of data; matching said first set of data to said second set of data based on said first location and said second location on the one hand and said first point of interest and said second point of interest on the other hand; identifying material surrounding the lumen based on the second set of data and obtaining at least a first material location of the identified material; associating the first material location of the identified material with a second material location in the second set of data relative to the length of the blood vessel based on the matching; providing a third set of data including a combination of the geometric representation of the blood vessel and a location of the identified material relative to the geometric representation of the blood vessel; Non-transitory media, including

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