Method and device for supporting the determination of vascular morphology based on spectral CT information

Abstract

Methods to support the determination of vascular morphology (G) based on spectral CT information, comprising the steps: - Providing at least two 3D CT images (B1, B2) of a person, which were acquired with different acquisition energies, whereby at least one low-energy image (B1) and one high-energy image (B2) are provided, - Selecting a vessel (G) of the person and an examination position at which the vessel (G) is to be examined, - Creating an examination image (U) by superimposing image information from at least the low-energy image (B1) and the high-energy image (B2), wherein the examination image (U) shows a cross-section through the selected vessel (G) at the examination position in top view, - Determining a verification area (P) in the cross-section, and forming a work set (A) from image values ​​of this verification area (P), - Calculate at least the first derivative (F1) of the work quantity (A) with respect to position, - Determining a number of change ranges (V, V1) in the course of the image values ​​of the work set (A) based on the first derivative (F1), and - Outputting information about the positions of the number of change areas (V, V1).

Description

[0001] The invention relates to a method and a device for supporting the determination of vascular morphology based on spectral CT information, a control device for a medical technology system and a medical technology system.

[0002] The prior art documents US 2019 / 0 029 625 A1, DE 10 2006 015 451 A1 and US 2011 / 0 222 748 A1 should be mentioned.

[0003] CT images of vessels are typically acquired using iodine contrast, followed by reconstruction of the images as "standard HU" images (HU: Hounsfield Units). Image interpretation can often be problematic, for example, when calcifications are present in the vessel. The high intrinsic contrast of the calcifications can magnify them, obscuring the visible lumen.

[0004] These partial volume effects are gaining increasing importance in light of computer-aided analysis, because this requires a precise quantitative evaluation of the morphology of the individual areas and components of a vessel for further processing (contours or surface networks). In addition to the active vessel diameter (lumen in percent or millimeters), this also involves the quantitative determination of plaque volumes, e.g., in cubic millimeters, which are generally further subdivided into the categories of soft, fatty, and hard plaques.

[0005] According to current technology, several methods exist for determining relevant vessel diameters, plaque sizes, and stenosis grades. These methods typically require the following steps: determining the vessel's midpoint and subsequently calculating orthogonal line profiles from different directions. Besides the simplest evaluation of the profiles using fixed HU thresholds, the first and second derivatives of the HU profiles are often employed. By evaluating the local maxima, minima, or zero crossings, these derivatives provide a more robust measure of the geometric object sizes. This can be achieved, for example, by controlling for the influence of the point image function of the reconstruction kernel used or by implicitly deriving the full-width-half maximum from the zero crossings of the second derivative.

[0006] However, according to the current state of technology, the methods in practical application are limited to "standard HU" images, the evaluation of which can lead to serious errors.

[0007] It is an object of the present invention to provide a method and a device for supporting the determination of vascular morphology based on spectral CT information, a control device for a medical technology system and a medical technology system with which the disadvantages described above are avoided.

[0008] This problem is solved by a method according to claim 1, a device according to claim 10, a control device according to claim 12 and a medical technology system according to claim 13.

[0009] A method according to the invention serves to support the determination of vascular morphology based on spectral CT information. It comprises the following steps: - Providing at least two 3D CT images of a person, acquired with different acquisition energies, including at least one low-energy image and one high-energy image. - Selecting a vessel of the person and an examination position in which the vessel is to be examined, - Creating an examination image by superimposing image information from at least the low-energy image and the high-energy image, wherein the examination image shows a cross-section through the selected vessel at the examination position in a top view, - Determining a verification area in the cross-section, and forming a work set from image values ​​of this verification area, - Calculating at least the first derivative of the quantity of work with respect to position and - Determining a number of change ranges in the course of the image values ​​of the work set based on the first derivative, - Outputting information about the positions of the number of change areas.

[0010] The two 3D CT images can be provided in the form of corresponding image data sets.

[0011] The procedure requires two or more CT scans of a person. These CT scans must depict the same area of ​​the person. Often, spectral CT scans are acquired simultaneously and each shows the same subject. However, this is not strictly necessary for the procedure. The CT scans can be acquired sequentially. They can also show different sections of the image, as long as both depict the region of interest (ROI).

[0012] Although CT images must be acquired, it is not necessary for them to be used by the procedure immediately after acquisition. They can also be stored in memory after acquisition and later retrieved from this memory, meaning that some time may pass between their acquisition and processing by the procedure.

[0013] As is standard practice when imaging blood vessels, CT images should be acquired after administration of a contrast agent. Acquiring spectral images after contrast agent administration is a known technique. Typically, images are acquired where the acquisition energy of one image is in the region of high absorption of the contrast agent, particularly in the region of its K-edge, and the acquisition energy of the other image is in the region where the substance under investigation has higher absorption than the contrast agent. Ideally, acquisition energies should be chosen at which the contrast agent and the substance under investigation can be clearly distinguished. Although iodine is often preferred, other contrast agents, or two or more contrast agents (with different K-edges), can also be used.More than two absorption energies can be used to differentiate between contrast agents.

[0014] At least two CT images are acquired using different acquisition energies. The one with the lowest acquisition energy is called the "low-energy image," and the one with the highest acquisition energy is called the "high-energy image." Additional images can also be used, such as DER images (DER: "Dual Energy Ratio"), PureLumen© (an image in which the calcium content has been subtracted by utilizing spectral information), or several intermediate energies.

[0015] It should be noted that the method according to the invention relates to the automated processing of images. It can thereby improve the presentation of these images for a subsequent diagnosis carried out by a human, but does not itself perform a diagnosis.

[0016] For this procedure, a vessel in the patient (within the region of interest, ROI) must be selected, along with an examination position (also within the ROI) at which the vessel will be examined. Multiple vessels can be selected, but this essentially amounts to performing the procedure multiple times. The vessels can be selected after, during, or before the CT scan.

[0017] The vessel can be selected automatically, preferably by segmenting the image, identifying the vessels, and then applying the procedure to a number of the segmented vessels, e.g., all of them. Alternatively, a vessel can be selected manually by a user. It is also conceivable that an automated selection of all vessels in a region of interest (ROI) is performed, and a user then manually selects at least one of the automatically selected vessels for the procedure to be applied. The selection of a vessel can also be made dependent on a preset or preselection of a vessel of interest and be automated based on this preset or preselection.

[0018] Once the CT images are available and the examination position is known, the examination image is created. It is generated by superimposing image information from at least the low-energy and high-energy images, preferably by subtracting or dividing the image values ​​at the same position. However, other methods of creating the examination image are also conceivable, such as weighted or unweighted addition, subtraction, multiplication, or division of image values, or more complex superimpositions, for example, to generate a virtually monoenergetic image or to generate a material-specific image using material decomposition.

[0019] The term "same position" refers to the absolute position of the region of interest (ROI). Essentially, the identical ROI is located in at least the CT images, these areas of the CT images are aligned, and overlapping image values ​​are superimposed to create a superimposed image of the ROI. The examination image is thus formed from pixels whose values ​​represent a superimposition of corresponding pixels from the CT images.

[0020] The examination image shows a cross-section through the selected vessel at the examination position, viewed from above. This means it shows the view into the vessel. Although it is preferred that the cross-section be taken perpendicular to the vessel's course, oblique cross-sections are also possible. For ease of use, a two-dimensional image is sufficient. However, the image can also be three-dimensional, for example, if a spatially extended area is to be examined.

[0021] Within the cross-section, i.e., the image under investigation, a review area is then defined. This review area specifies which image values ​​of the image under investigation are to be processed further. The review area can be a line, a plurality of lines (e.g., a cross), or an area (e.g., the entire cross-section).

[0022] The inspection area can be determined automatically, preferably by selecting a predefined type (e.g., line or area) for an inspection area and applying it to the cross-section. Alternatively, the inspection area can also be determined by a user, preferably by manually marking an area.

[0023] Once the inspection area is defined, a work set is created from the image values ​​within that area. If the inspection area is a line, the work set is created from the image values ​​of pixels / voxels along that line; if it is an area, the work set is created from the image values ​​of pixels / voxels on that area. Since derivations will be performed subsequently, the elements of the work set should contain information about their positions in addition to the image values. Because pixels and voxels fulfill this requirement, it is preferred that the work set be composed of pixels and voxels. Therefore, the pixels and voxels of the image under investigation can simply be copied within the inspection area and pasted into the work set.

[0024] Once the work set has been defined, its first derivative with respect to position can be calculated. If the inspection area was a line, then the work set essentially forms a graph of image values ​​(Y-coordinate) along this line (X-coordinate). This graph can then be differentiated with respect to X, the position. It can be exploited that the elements of the work set (pixels / voxels) have fixed distances from each other. For the first derivative, it is then essentially only necessary to calculate the difference Ej - Ei between each adjacent element Ei and Ej.

[0025] If the area to be examined was two-dimensional, meaning the work set represents a surface in an XY plane with image values ​​at a Z-position, then this can be considered a scalar field, and partial derivatives in the X and Y directions can be calculated as the first derivative. The result would be a vector field in which two values ​​per pixel indicate the derivative with respect to X and Y, respectively.

[0026] If the area of ​​investigation was three-dimensional, the work set can represent a volume in an XYZ space with image values ​​in another dimension. Then, as the first derivative, partial derivatives in the X, Y, and Z directions can be calculated. The result would be a multidimensional vector field.

[0027] After calculating the first derivative, the second derivative can also be calculated. This can assist in the subsequent calculation.

[0028] After derivation, the course of the image values ​​is examined for changes. Particular attention is paid to extrema (local maxima and minima) of the first derivative, which would be zeros of a second derivative. A region of change always lies between two extrema, as these indicate that the image values ​​there are systematically different from those beyond these extrema. For an optimal vessel, one can expect, for example, two extrema (if the examination area was a line), namely at the vessel walls. At least with a previously administered contrast agent, the image values ​​inside a vessel should differ significantly from the area outside the vessel. If the vessel contains plaque, there could be, for example, three extrema: one at the vessel wall and one inside the vessel at the edge of the plaque. Thus, two regions of change would exist inside the vessel.Due to the special procedure, namely the shaping of the examination image by superimposing image values, the derivations are much more meaningful, as the transitions in the examination image are clearer than in normal HU images.

[0029] The information, e.g., positions and, if applicable, mean image values, about this number of areas of change can then be output, e.g., as an image or numerical values, and support a person in their assessment.

[0030] The invention thus enables the optimized determination of morphological parameters of a vascular segment, such as lengths, diameters, relative lengths, volumes, or degrees of stenosis, by utilizing spectral CT images from appropriate scans, e.g., dual-energy or photon-counting CT (PCCT). In particular, the invention allows for a more accurate and robust determination of the diameter and degree of stenosis of vessels containing plaque (soft, fatty, calcified, etc.). The vascular profiles derived from this information, as well as their derivatives, reveal structures either more clearly or for the first time compared to classical profile-based methods that use standard hyperradiography images. This has the advantage that, in particular, diameters and volumes can be determined with greater accuracy and less error.

[0031] A device according to the invention serves to support the determination of vascular morphology based on spectral CT information. It comprises the following components: - a data interface designed to receive image data from 3D CT images of a person, which were acquired with different acquisition energies, at least from a low-energy image and a high-energy image, - a selection unit designed for selecting a vessel of the person and an examination position at which the vessel is to be examined, - a superimposition unit designed to create an examination image by superimposing image information of at least the low-energy image and the high-energy image, wherein the examination image shows a cross-section through the selected vessel at the examination position in top view, - a work quantity unit designed to determine a verification area in the cross-section, and to generate a work quantity from image values ​​of this verification area, - a calculation unit designed to calculate at least the first derivative of the quantity of work with respect to location, - a unit of change designed to determine a number of change ranges in the course of the image values ​​of the work set based on this derivation, - a data interface designed to output positions of the number of change areas.

[0032] The function of the device's components has already been described. The device is preferably designed for carrying out a method according to the invention.

[0033] Preferably, the selection unit is designed to automatically select a container, preferably by performing image segmentation. However, it can also alternatively or additionally provide a user interface for manual container selection by a user.

[0034] Preferably, the work quantity unit is designed to automatically determine an inspection area, preferably by selecting a predetermined type (e.g., line or area) for an inspection area and applying it to the cross-section. However, it can also alternatively or additionally provide a user interface for manually defining an inspection area, in particular by which a user can manually mark an inspection area.

[0035] A control device according to the invention for a medical technology system, preferably a CT system or a diagnostic system, comprises a device according to the invention and / or is designed to carry out a method according to the invention.

[0036] A medical technology system according to the invention, preferably a diagnostic system or a CT system, in particular a photon counting CT system, comprises a control device according to the invention.

[0037] In particular, the features and advantages described in connection with the methods according to the invention can also be designed as corresponding subunits of the device or medical device system according to the invention. Conversely, the features and advantages described in connection with the device or medical device system according to the invention can also be designed as corresponding process steps of the methods according to the invention.

[0038] The invention can be implemented, in particular, in the form of a computer unit with suitable software. The computer unit can, for example, comprise one or more cooperating microprocessors or the like. In particular, it can be implemented in the form of suitable software program components within the computer unit. A largely software-based implementation has the advantage that even previously used computer units can be easily retrofitted by a software or firmware update to operate according to the invention. In this respect, the problem is also solved by a corresponding computer program product with a computer program that can be directly loaded into a memory device of a computer unit, containing program sections to execute all steps of the method according to the invention when the program is run in the computer unit.In addition to the computer program itself, such a computer program product may include additional components such as documentation and / or additional components, including hardware components such as hardware keys (dongles, etc.) for using the software.

[0039] For transport to the computer unit and / or for storage on or in the computer unit, a computer-readable medium, such as a memory stick, a hard drive or other portable or permanently installed data carrier, can be used, on which the program sections of the computer program that can be read and executed by a computer unit are stored.

[0040] Further, particularly advantageous embodiments and developments of the invention result from the dependent claims and the following description, wherein the claims of one claim category may also be further developed analogously to the claims and description parts of another claim category and, in particular, individual features of different embodiments or variants may be combined to form new embodiments or variants.

[0041] Typically, CT images are constructed from image elements such as pixels or voxels. The images are then derived from the image values ​​of these elements at discrete positions. This also applies to the examination image. When constructing the examination image, a common region of interest (ROI) of all images is always considered, and this ROI should be identical in all images except for its specific image values ​​(position, size, and, in the 2D case, viewing angle) to minimize errors. Otherwise, image registration could be performed for alignment. Ideally, all CT images should depict the same subject but with different spectral image information.

[0042] Preferably, the examination image is calculated from a quotient and / or a difference of image values ​​of corresponding image elements (pixels / voxels) of the low-energy image and the high-energy image (and possibly other image elements).

[0043] Depending on the desired type of examination, the inspection area can be customized. Particularly informative results are obtained with a linear, cross-shaped, or area-based inspection area. The workload is directly determined by the inspection area.

[0044] Preferably, the work quantity is formed from image values ​​on a line across the vessel cross-section, wherein the first derivative of the work quantity is a derivative over the course of image values ​​along the line.

[0045] Alternatively, preferably, the work set comprises the pixels of the area of ​​the cross-section, wherein the first derivative of the work set is the partial derivative of the work set along two linearly independent spatial directions perpendicular to the cross-section.

[0046] It is also preferred that the work quantity includes the image points of the volume of the vessel from the cross-section and a predetermined distance along the vessel's course, wherein the first derivative of the work quantity are the partial derivatives of the work quantity along three linearly independent spatial directions.

[0047] Preferably, a range of change is determined from the extrema of the image values ​​of the work set, i.e., maxima and minima of the first derivative or zeros of the second derivative. Alternatively, a range of change can also be determined from the amplitude of the first derivative (square root of the sum of the squared partial derivatives). Since the fundamental task is the detection of edges or transitions in space, in the three-dimensional case a spatial derivative ∂ / ∂x, ∂ / ∂y, ∂ / ∂z could be calculated over all three spatial dimensions, followed by the determination of the amplitude. (∂ / ∂x)2+(∂ / ∂y)2+(∂ / ∂z)2.

[0048] Preferably, a second derivative is derived from the first derivative, and ranges of change are preferably determined from this second derivative or from the first and second derivatives. Preferably, zeros of the second derivative are found for this purpose.

[0049] When creating the examination image, normalization and / or calibration and / or adjustment and / or mapping of values ​​is preferably also performed. In particular, multiplication of all image information by a predefined factor or a predefined function that is location-dependent across the examination image is preferred. This is advantageous in order to give the examination image optimal informative value.

[0050] It is preferred that a contrast agent, in particular iodine, be administered to the patient prior to the CT scan, and that the CT images be acquired with this contrast agent in the area to be examined. Such a procedure is known in the prior art. For example, when examining the coronary arteries, it is customary to wait a certain amount of time after administering iodine until the contrast agent has accumulated in these vessels before acquiring the CT scans.

[0051] Preferably, CT images are provided that have been acquired from a person after administration of a contrast agent, particularly an iodine-based contrast agent. The CT images may include image datasets acquired after the contrast agent has been administered to the area of ​​the person being examined.

[0052] According to a preferred embodiment of the method, the CT images are acquired using a photon counting CT technique. This PCCT technique provides energy-resolved measurement. Preferably, the examination image is created by superimposing image information from a number of additional CT images, in addition to the low-energy and high-energy images. Thus, CT images acquired using a photon counting CT technique are preferably provided.

[0053] The photon-counting detector of a PCCT scanner can directly convert incoming X-ray quanta into an electrical signal. Since the signal strength correlates with the energy of the respective detected X-ray quantum, each detected X-ray quantum can be assigned to a specific energy range. The distinguishable energy ranges can be predefined by adjusting the readout electronics of the photon-counting detector. This allows for the selection of energy ranges where the spectral differentiation of different materials, such as calcium, iodine, and water, is optimized.

[0054] It is preferred that several contrast agents with different absorption edges are administered to a person before the CT scan. Very heavy contrast agents with a high K-edge, e.g., gold or tungsten, are preferred. This allows for a more detailed examination. The acquisition energies should be selected so that a different contrast agent exhibits an absorption maximum in each CT image. Due to the high spatial and spectral resolution of PCCT, this acquisition method is very advantageous for this application.

[0055] It is preferred that, in one embodiment of the method, spectral information of image values ​​of the work quantity is additionally determined and a change range based on this spectral information is determined. Preferably, a trend of image values ​​from different CT images is generated as a function of the beam energy, and a derivative of the trend with respect to a beam energy is calculated.

[0056] Preferably, the partial spectral derivative ∂ / ∂s (where s represents the spectral ranges in which the CT images were acquired) is used to calculate a 4D edge amplitude in order to determine a change range. The spectral information is preferably incorporated directly into the amplitude calculation. (∂ / ∂x)2+(∂ / ∂y)2+(∂ / ∂z)2+(∂ / ∂s)2. A corresponding second derivative can be calculated for three or more images, e.g. simply by subtracting adjacent image values ​​and dividing by the difference in the recording energies.

[0057] For this embodiment, the aforementioned PCCT (Photon Counting CT) method would also be very advantageous. This method can provide CT images from several different spectral ranges. This allows for the creation and evaluation of spectral gradients d / ds (or ∂ / ∂s). A 4D gradient could be generated with high-resolution spatial dimensions dx, dy, dz (e.g., from the low-energy image) in combination with the spectral derivative, considering images from other spectral ranges, which may well employ lower spatial resolution. PCCT offers a mixed resolution option for this purpose.

[0058] In a preferred device, the superposition unit is designed to superimpose three or more CT images onto the examination image. The computation unit is preferably also designed to calculate a first derivative of a spectral profile with respect to a beam energy, and the modification unit is preferably designed to determine a modification range based on the derivative of the spectral profile.

[0059] The use of AI-based methods (Cl: "Artificial Intelligence") is preferred for the method according to the invention. Artificial intelligence is based on the principle of machine learning and is generally implemented with a learning algorithm that has been trained accordingly. The English term "machine learning" is frequently used for machine learning, which also includes the principle of "deep learning." Such an application enables optimal evaluation for determining vessel or plaque size based on a plurality of different CT images, using a system trained and implemented in this way.

[0060] Preferably, components of the invention are provided as a "cloud service." Such a cloud service serves to process data, particularly using artificial intelligence, but can also be a service based on conventional algorithms or a service where human evaluation takes place in the background. Generally, a cloud service (hereinafter also referred to simply as "cloud") is an IT infrastructure in which, for example, storage space or computing power and / or application software is provided via a network. Communication between the user and the cloud takes place via data interfaces and / or data transmission protocols. In the present case, it is particularly preferred that the cloud service provides both computing power and application software.

[0061] In a preferred method, data obtained within the scope of the invention is provided to the cloud service via the network. This cloud service comprises a computing system that typically does not include the user's local computer. The method can be implemented using a command-line configuration within a network. The data processed in the cloud is subsequently sent back to the user's local computer via the network. Preferably, HU images (HU: Hounsfield Units) can be used directly as low-energy images and high-energy images (and as a further image) for the method.

[0062] A dual-energy ratio (or an investigation image) can be generated as the ratio of measured and identically reconstructed QR40 low-energy images and high-energy images. A low-energy image is divided by a corresponding high-energy image. This can be achieved by dividing the image values ​​of identical image elements accordingly.

[0063] For the examination image, it is preferred that a threshold be defined, particularly when calculating a quotient of image values. If HU images are available, a threshold below 50 HU, and especially below 25 HU, is preferred. For example, a threshold of 20 HU may be used. Below this threshold, a predefined value, e.g., 1 HU, is preferably assigned to the image elements.

[0064] The image can be normalized after the fact. For example, if an image is derived from a difference or quotient of image values, these values ​​can be multiplied by a constant value. For instance, the image values ​​of an image that lie between 1 and 2.5 can be multiplied by 100, resulting in image values ​​between 100 and 250 that can be readily represented as grayscale.

[0065] The invention is explained in more detail below with reference to the accompanying figures and exemplary embodiments. The same components are designated with identical reference numerals in the various figures. The figures are generally not to scale. They show: Fig. 1 a rough schematic representation of a CT system with an embodiment of a control device according to the invention for carrying out the method, Fig. 2. A block diagram of the process flow, Fig. 3. Detection of a stenosis, Fig. 4 absorptions of different contrast agents.

[0066] Fig. Figure 1 shows an embodiment of a computed tomography system (CT system) 1 with a radiation detector 4 and a radiation source 5. The radiation source 5 is configured to expose the radiation detector 4 with radiation. The CT system 1 shown comprises a gantry 2 with a rotor 3. The rotor 3 includes an X-ray source 5 as the radiation source 5 and the radiation detector 4, which is configured to detect X-rays.

[0067] The rotor 3 is rotatable about the axis of rotation 8. The patient 6 is positioned on the patient table 7 and can be moved along the axis of rotation 8 by the gantry 2. The patient's head 6 rests on a positioning aid L. The processing unit 9 is provided for controlling the imaging system 1 and / or for generating an image data set based on signals detected by the radiation detector 4.

[0068] Typically, a (raw) X-ray image dataset of the object under investigation 6 is acquired from a variety of angular directions using the radiation detector 4 at different beam energies, resulting in two or more raw datasets. Subsequently, a (final) image dataset can be reconstructed from the (raw) X-ray image dataset using a mathematical procedure, for example, including a filtered backprojection or an iterative reconstruction method.

[0069] The processing unit 9 serves here as a control unit 9 for controlling the CT system 1. An input device 10 and an output device 11 are connected to this processing unit 9. The input device 10 and the output device 11 can, for example, enable interaction by a user or the display of a generated image data set B.

[0070] The control unit 9 comprises a device 12 according to the invention for supporting the determination of a vascular morphology based on spectral CT information according to a method according to the invention (see Fig. 2) The device 12 comprises a data interface 13, a selection unit 14, a superposition unit 15, a work quantity unit 16, a calculation unit 17 and a modification unit 18.

[0071] Data interface 13 is used to receive image data from 3D CT scans of a person, acquired with different imaging energies. At a minimum, one low-energy image B1 and one high-energy image B2 should be received. In this example, data interface 13 is also used to output the positions of the number of change areas V, i.e., the result. These can be displayed, for example, on output device 11.

[0072] Selection unit 14 is used to select a vessel G of the person and an examination position at which the vessel G is to be examined. It can automatically select a vessel G, but also provides a user interface via input device 10 and output device 11, allowing a user to select a vessel.

[0073] The superposition unit 15 serves to create an examination image U by superimposing image information of at least the low-energy image B1 and the high-energy image B2, wherein the examination image U shows a cross-section through the selected vessel G at the examination position in top view.

[0074] The work unit 16 is used to determine a verification area P in the cross-section and to generate a work quantity A from image values ​​of this verification area P. It can automatically determine the verification area P, but also provides a user interface via the input device 10 and the output device 11, by means of which a user can determine a verification area P.

[0075] The calculation unit 17 is used to calculate at least the first derivative of the quantity of work A with respect to location.

[0076] The change unit 18 serves to determine a number of change ranges V in the course of the image values ​​of the work quantity A based on this derivation.

[0077] Fig. Figure 2 shows a block diagram of the process of a procedure to support the determination of vascular morphology based on spectral CT information.

[0078] First, two 3D CT images B1, B2 of a person are provided, which were taken with different acquisition energies, with at least one low energy image B1 and one high energy image B2 being provided.

[0079] In step I, a vessel G belonging to the person and an examination position at which the vessel G is to be examined are selected. An examination image U is then created by superimposing image information from at least the low-energy image B1 and the high-energy image B2. The examination image U shows a cross-section through the selected vessel G at the examination position in a top view. In this example, the examination image U is a two-dimensional image.

[0080] In step II, a verification area P is defined as a line in the cross-section, and a work set A is created from the image values ​​of this verification area P. This is done simply by defining the pixels of the inspection image U along this line as the work set A. The image values ​​of the work set A along the line (verification area P) are shown here.

[0081] In step III, the first derivative F1 and the second derivative F2 of the work set A are calculated with respect to location, and the range of change V in the course of the image values ​​of the work set A is determined based on the first derivative F1 and the second derivative F2.

[0082] The information determined in step III about the positions of the change areas V is then output, e.g. as a graphic representation that can be evaluated by a person.

[0083] Fig. Figure 3 outlines a stenosis detection method as described in Fig. Figure 2 is shown. In the case presented here, the transition zone P in the examination image U on the left is again a line, as this is a very simple and easily visualized case. The cross-section through a vessel G shown in the examination image exhibits a stenosis S, which restricts the vessel G at its edge.

[0084] If the first lead F1 and the second lead F2 are now derived from the work volume A, the transition from the edge of the vessel G to the stenosis S and also the transition to the free area of ​​the vessel, as well as from the free area to the edge, can be clearly seen in leads F1 and F2. Two areas of change V and V1 can then be identified.

[0085] Vascular simulation can be performed, for example, with a photon counting CT scan using 140 kV beam energy. This allows for the reliable detection of stenoses, which often consist of calcium, even at varying iodine concentrations.

[0086] Fig. Figure 4 shows the absorptions of different contrast agents. The absorptions on the y-axis with respect to the beam energy on the x-axis for iodine (I), gadolinium (GD), gold (Au), and bismuth (Bi) are shown here.

[0087] Finally, it should be noted once again that the invention described in detail above merely represents exemplary embodiments, which can be modified in various ways by a person skilled in the art without departing from the scope of the invention. Furthermore, the use of the indefinite articles "a" or "an" does not preclude the possibility that the features in question may be present multiple times. Likewise, terms such as "unit" do not preclude the possibility that the components in question consist of several interacting sub-components, which may also be spatially distributed. The term "a number" should be read as "at least one." Regardless of the grammatical gender of a particular term, persons of male, female, or other gender identities are included.

Claims

[1] Method to support the determination of vascular morphology (G) based on spectral CT information, comprising the steps: - Providing at least two 3D CT images (B1, B2) of a person, which were acquired with different acquisition energies, whereby at least one low-energy image (B1) and one high-energy image (B2) are provided, - Selecting a vessel (G) of the person and an examination position at which the vessel (G) is to be examined, - Creating an examination image (U) by superimposing image information from at least the low-energy image (B1) and the high-energy image (B2), wherein the examination image (U) shows a cross-section through the selected vessel (G) at the examination position in top view, - Determining a verification area (P) in the cross-section, and forming a work set (A) from image values ​​of this verification area (P), - Calculate at least the first derivative (F1) of the work quantity (A) with respect to position, - Determining a number of change ranges (V, V1) in the course of the image values ​​of the work set (A) based on the first derivative (F1), and - Outputting information about the positions of the number of change areas (V, V1). [2] Method according to claim 1, wherein image values ​​of image elements of the investigation image (U) are calculated from a quotient and / or a difference of values ​​of corresponding image elements of the low-energy image (B1) and the high-energy image (B2). [3] Method according to any of the preceding claims, wherein in the context of determining a verification area (P) - the work quantity (A) is formed from image values ​​on a line across the vessel cross-section (G), where the first (F1) derivative of the work quantity (A) is a derivative over the course of image values ​​along the line, and / or - the work set (A) comprises the image points of the area of ​​the cross-section, where the first (F1) derivative of the work set (A) are the partial derivatives of the work set (A) along two linearly independent spatial directions perpendicular to the cross-section, and / or - the work set (A) comprises the image points of the volume of the vessel (G) of the cross-section and a predetermined distance along the vessel course (G), wherein the investigation image can also be three-dimensional and wherein the first derivative (F1) of the work set (A) are the partial derivatives of the work set (A) along three linearly independent spatial directions. [4] Method according to one of claims 1 or 2, wherein change ranges (V, V1) of the image values ​​of the work quantity (A) are determined from extrema and / or the amplitude of the first derivative (F1). [5] Method according to one of the preceding claims, wherein a second derivative (F2) is formed from the first derivative (F1) and change ranges (V, V1) are determined from this second derivative (F2) or from the first and the second derivative (F1, F2), preferably wherein zeros of the second derivative (F2) are determined. [6] Method according to one of the preceding claims, wherein, in the creation of the examination image (U), additional normalization and / or calibration and / or adjustment and / or mapping of values ​​is carried out, in particular by multiplying all image information by a predetermined factor or a predetermined function that is location-dependent over the examination image (U). [7] Method according to any of the preceding claims, wherein CT images (B1, B2) are provided which have been taken by a person after administration of a contrast agent, in particular an iodine-containing contrast agent. [8] Method according to one of the preceding claims, wherein CT images (B1, B2) are provided which have been acquired as part of a photon counting CT procedure, preferably wherein the examination image (U) is created by superimposing image information from a number of additional CT images (B1, B2) in addition to the low-energy image (B1) and the high-energy image (B2), preferably wherein, prior to acquiring the CT images (B1, B2), several contrast agents with different absorption edges were administered to the person. [9] Method according to one of the preceding claims, in particular according to claim 8, wherein additionally spectral information of image values ​​of the work quantity (A) is determined and change ranges (V, V1) are determined based on this spectral information, preferably wherein a course of image values ​​of different CT images (B1, B2) is created as a function of the beam energy (E) and a derivative (F1) of the course with respect to a beam energy (E) is calculated. [10] Device (12) for supporting the determination of vascular morphology (G) based on spectral CT information, comprising: - a data interface (13) designed to receive image data from 3D CT images (B1, B2) of a person which have been acquired with different acquisition energies, at least from a low-energy image (B1) and a high-energy image (B2), - a selection unit (14) designed for selecting a vessel (G) of the person and an examination position at which the vessel (G) is to be examined, - a superimposition unit (15) designed to create an examination image (U) by superimposing image information of at least the low-energy image (B1) and the high-energy image (B2), wherein the examination image (U) shows a cross-section through the selected vessel (G) at the examination position in top view, - a work unit (16) designed to determine a verification area (P) in the cross-section, and to form a work quantity (A) from image values ​​of this verification area (P), - a calculation unit (17) designed to calculate at least the first derivative (F1) of the work quantity (A) with respect to location, - a change unit (18) designed to determine a number of change ranges (V, V1) in the course of the image values ​​of the work set (A) based on this derivative (F1), - a data interface (13) designed to output positions of the number of change areas (V, V1). [11] Device (12) according to claim 10, wherein the superposition unit (15) is designed to superimpose three or more CT images (B1, B2) to the examination image (U) and the calculation unit (17) is preferably additionally designed to calculate a first derivative (F1) of a spectral profile with respect to a beam energy (E) and the modification unit (18) is preferably designed to determine a modification range (V, V1) based on the derivative of the spectral profile. [12] Control device (9) for a medical technology system, preferably a CT system (1) or a diagnostic system, wherein the control device (9) comprises a device (12) according to claim 10 or 11 and / or is designed to carry out a method according to one of claims 1 to 9. [13] Medical technology system, preferably a CT system (1), in particular a photon counting CT system, or a diagnostic system, comprising a control unit (9) according to claim 12. [14] Computer program product comprising instructions which, when the program is executed by a computer, cause it to perform the steps of the method according to any one of claims 1 to 9, wherein acquiring CT images (B1, B2) includes the output of corresponding control instructions. [15] Computer-readable storage medium comprising instructions which, when executed by a computer, cause it to perform the steps of the method according to any one of claims 1 to 9, wherein acquiring CT images (B1, B2) comprises the output of corresponding control instructions.

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