Providing a comparative dataset

Abstract

This invention relates to a method for providing a comparative dataset, comprising providing a first dataset mapping a first contrast agent flow and a second dataset mapping a second contrast agent flow; spatially registering the first and second datasets; identifying images of at least one vascular segment of a region of interest in the first and second datasets by means of the perfusion direction of the mappings of the first and / or second contrast agent flows; temporally registering the first and second datasets to minimize the time difference in perfusion between the mappings of the first and second contrast agent flows in at least one vascular segment; identifying a deviation between the first and second contrast agent flows by comparing the registered first and second datasets; and providing a comparative dataset based on the registered first and second datasets; wherein the comparative dataset has at least one parameter characterizing the deviation. The invention also relates to a providing unit, a medical imaging device, and a computer program product.

Description

Technical Field

[0001] The present invention relates to a method for providing a comparative dataset, a providing unit, a medical imaging device, and a computer program product. Background Technology

[0002] To detect changes in the region of interest (ROI) of an examined subject, such as a human or / or animal patient, differential imaging methods, particularly X-ray-based methods, such as digital subtraction angiography (DSA), are typically used. Changes to be detected may include, for example, the diffusion and / or flow of contrast agent within a vascular segment of the examined subject, particularly contrast agent flow. In DSA, a mask image is typically subtracted from one or more filled images to provide a differential image, wherein the mask image does not include the ROI with contrast agent disposed therein, while at least one filled image maps the ROI with contrast agent disposed therein. To map the temporal dynamics of contrast agent flow, such as contrast agent perfusion, multiple filled images are typically recorded chronologically within the ROI and combined with the mask image to form a time-resolved differential image.

[0003] Preoperative DSA is typically performed, especially for preoperative planning of cerebrovascular treatment, particularly for vascular malformations and / or stenosis and / or aneurysms. Additional DSA may be performed preoperatively or intraoperatively to also detect the temporal dynamics of contrast agent flow during or after treatment of vascular malformations and / or stenosis and / or aneurysms. Typically, the medical operator visually compares the differential images from the first DSA with the additional DSA. Disadvantageously, differences between preoperative and intraoperative or postoperative contrast agent administration, such as injection time and / or injection rate and / or contrast agent concentration and / or catheter position, can lead to erroneous comparisons of the differential images. Therefore, quantitative analysis of the temporal dynamics of contrast agent flow may be hindered or tampered with. Summary of the Invention

[0004] Therefore, the technical problem to be solved by the present invention is to enable quantitative analysis of the temporal dynamics of contrast agent flow changes in the region of interest of the examined object.

[0005] The technical problem described herein is solved by a method for providing a comparison dataset, the method comprising:

[0006] - Provide a time-resolved first dataset, which maps to a first contrast agent flow in the region of interest of the examined object within a first time period;

[0007] - Provide a time-resolved second dataset, which maps to a second contrast agent flow in the region of interest of the examined object within a second time period following the first time period;

[0008] - Spatially register the first and second datasets;

[0009] - Identify images of at least one particularly incoming vascular segment of the region of interest in the first and second datasets by means of the perfusion direction mapped by the first and / or second contrast agent flows;

[0010] - Register the first and second datasets in time to minimize the time difference of perfusion of the mapping of the first and second contrast agent flows in the at least one vascular segment;

[0011] - Identify the discrepancy between the first and second contrast agent flows by comparing the first and second registered datasets;

[0012] - The comparison dataset is provided based on the first and second registration datasets;

[0013] The comparison dataset has at least one parameter characterizing the deviation.

[0014] The technical problem described herein is also solved by a providing unit designed to implement the method of the aforementioned type.

[0015] The technical problem described above is also solved according to the present invention by a medical imaging device having a providing unit of the aforementioned type, wherein the medical imaging device is designed to record and / or provide a first dataset and a second dataset.

[0016] The technical problem described herein is also solved by a computer program product having a computer program that can be directly loaded into the memory of a providing unit, the computer program having multiple program segments so that when these program segments are executed by the providing unit, all steps of the methods of the aforementioned type are implemented.

[0017] In a first aspect, the present invention relates to a method, particularly computer-implemented, for providing a comparative dataset. In this method, a time-resolved first dataset is provided, which maps to a first contrast agent flow in a region of interest of an examined object within a first time period. Furthermore, a time-resolved second dataset is provided, which maps to a second contrast agent flow in a region of interest of the examined object within a second time period following the first time period. In a further step, the first and second datasets are spatially registered. Furthermore, an image of at least one, particularly incoming, vascular segment of the region of interest in the first and second datasets is identified by means of the perfusion direction of the mappings of the first and / or second contrast agent flows. Furthermore, the first and second datasets are temporally registered to minimize the time difference in perfusion between the mappings of the first and second contrast agent flows in the at least one vascular segment. Furthermore, a deviation between the first and second contrast agent flows is identified by comparing the registered first dataset and the registered second dataset. Thereafter, the comparative dataset is provided based on the registered first dataset and the registered second dataset. The comparative dataset has at least one parameter characterizing the deviation.

[0018] Here, the steps described above in the proposed method for providing comparative datasets can be performed sequentially and / or at least partially simultaneously. Furthermore, the steps of the proposed method can be implemented at least partially, and especially entirely, by a computer.

[0019] The provision of the first dataset may include receiving and / or generating, particularly reconstructing, the first dataset. Furthermore, the provision of the second dataset may include receiving and / or generating, particularly reconstructing, the second dataset.

[0020] Receiving the first and / or second datasets may in particular include acquiring and / or reading from computer-readable data storage and / or receiving from data storage units, such as databases. Furthermore, the first and / or second datasets may be provided by one or more medical imaging devices, particularly those having the same or different imaging modalities. At least one medical imaging device may be designed, for example, as a magnetic resonance imaging (MRT) system and / or a computed tomography (CT) system and / or a medical X-ray device and / or a positron emission tomography (PET) system and / or an ultrasound device.

[0021] Alternatively or additionally, the first dataset can be generated, in particular reconstructed, from pre-acquired first image data. Similarly, the second dataset can be generated, in particular reconstructed, from pre-acquired second image data.

[0022] The time-resolved first dataset can map a region of interest (ROI) in two-dimensional (2D) and / or three-dimensional (3D) spatial resolution of the examined object. The examined object can be, for example, a human and / or animal patient. Furthermore, the ROI can include a spatial region of the examined object containing anatomical objects, such as organs, especially hollow organs, and / or tumors, and / or predefined segments of anatomical objects. Advantageously, the ROI can include at least one vascular segment, such as an artery and / or vein. Furthermore, the first dataset can map the ROI within a first time period, wherein the first time period includes multiple first recording time points. In this case, the first dataset can map the contrast agent, especially the first contrast agent flow, especially diffusion motion, especially perfusion and / or flow motion, of the contrast agent in the ROI during the first time period. Advantageously, the first dataset can have multiple image points, each with a time-intensity curve, which maps the first contrast agent flow in the ROI to temporal intensity variations in at least one vascular segment.

[0023] The time-resolved second dataset can map regions of interest (ROIs) in two-dimensional (2D) and / or three-dimensional (3D) spatial resolution to the examined object. Furthermore, the second dataset can map ROIs within a second time period, which includes multiple second recording time points. In this case, the second dataset can map the contrast agent, particularly the contrast agent blobs, second contrast agent flow, particularly diffusion motion, particularly perfusion and / or flow motion within the ROI during the second time period. Advantageously, the second dataset can have multiple image points, each with a time-intensity curve, which maps the second contrast agent flow within the ROI to temporal intensity variations in at least one vascular segment.

[0024] Spatial registration of the first and second datasets may, for example, include rigid and / or non-rigid transformations of the first and / or second datasets, particularly their respective image points, especially translation and / or rotation and / or scaling. Advantageously, spatial registration may be based on anatomical and / or geometric features mapped in the first and second datasets. Anatomical features may, for example, include organs, particularly hollow organs, and / or anatomical landmarks, particularly the mouth. Furthermore, geometric features may, for example, include contours and / or marked objects. Here, spatial registration may include determining a transformation and / or applying a transformation to the first and / or second datasets such that spatial deviations between images of corresponding anatomical and / or geometric features in the first and second datasets are minimized.

[0025] Identification of an image of at least one vascular segment within a region of interest may include identifying, in particular segmenting, image points of a first dataset and a second dataset that map at least one vascular segment. At least one vascular segment, particularly at least one locally introduced vascular segment relative to and / or for supplying the region of interest, may be identified here by means of the perfusion direction of the mapping of the first and / or second contrast agent flow within the region of interest. The perfusion of the first and / or second contrast agent flow may describe a particularly local increase, particularly a predefined intensity change, in the contrast agent concentration within the image of at least one vascular segment, particularly the image of the at least one vascular segment in the first and / or second datasets. In this case, the direction of perfusion may be determined by means of a time-resolved image of the increase in contrast agent concentration, particularly the predefined intensity change, along the image points mapping at least one vascular segment, particularly along the centerline of at least one vascular segment. If the perfusion direction in at least one vascular segment points towards the region of interest, then the introduced vascular segment, particularly for supplying, may be identified as said at least one vascular segment.

[0026] Advantageously, one or more particularly continuous vascular segments, especially common vessels, such as arteries and / or veins, can be identified by means of the perfusion direction mapped by the first and / or second contrast agent flows.

[0027] According to the first variant scheme, the image of at least one vascular segment of the region of interest in the first and second datasets can be identified by means of the perfusion direction of the mapping of the first and second contrast agent flows.

[0028] According to the second variant, the image of at least one vascular segment in the region of interest in the first and second datasets can be identified by means of the perfusion direction mapped by the first or second contrast agent flow. Here, the image of at least one vascular segment in the first and second datasets can also be identified by spatial registration of the first and second datasets.

[0029] Temporal registration of the first and second datasets can include perfusion synchronization of spatially corresponding image points of the first and second datasets, particularly the temporal intensity curves of corresponding image points in the first and second datasets, based on the mapping of the first and second contrast agent flows in at least one vascular segment. In this case, the spatially corresponding image points of the first and second datasets can map common spatial locations in at least one vascular segment and are also used, in particular, to synchronize the temporal intensity curves of other image points in the first and second datasets. Furthermore, the time difference in perfusion of the mapping of the first and second contrast agent flows in at least one vessel can be determined by comparing time points that reach or exceed a predefined intensity threshold of the temporal intensity curves of the spatially corresponding image points in the first and second datasets. Advantageously, temporal registration of the first and second datasets, particularly synchronizing the temporal intensity curves of spatially corresponding image points in the first and second datasets, can minimize the time difference in perfusion of the mapping of the first and second contrast agents in at least one vascular segment.

[0030] Comparing first and second registered datasets, particularly spatially and temporally, to identify deviations between the first and second contrast agent flows may include comparing the temporal intensity curves of registered image points of the first and second datasets that correspond to each other spatially. This comparison can identify deviations between the first and second contrast agent flows. Specifically, it can identify image points in the registered first and / or second datasets that map deviations between the first and second contrast agent flows. Advantageously, deviations can be qualitatively identified first by comparing the registered first and second datasets.

[0031] Furthermore, the provision of the comparison dataset may include a quantitative comparison of registered first and second datasets, particularly a quantitative comparison of image points mapping the deviation between the first and second contrast agent flows in the registered first and second datasets. The comparison dataset may have at least one parameter characterizing the deviation. Here, the at least one parameter characterizing the deviation may describe, particularly quantify, the deviation, particularly globally, particularly locally on a per-image basis, or particularly regionally, particularly for at least one vascular segment, for the entire region of interest. Furthermore, the comparison dataset may have multiple parameters characterizing the deviation, which describe the deviation locally or regionally. Additionally, the comparison dataset may have 2D and / or 3D spatially resolved comparison images of the region of interest of the examined object, wherein the image points of the comparison images map the deviation, such as difference or ratio, between the first and second contrast agent flows. Furthermore, the comparison images may be time-resolved. In this case, the image values ​​of the image points in the comparison images may at least partially correspond to the multiple parameters characterizing the deviation.

[0032] Providing a comparison dataset may, for example, include storing it on a computer-readable storage medium and / or displaying a graphical representation of the comparison dataset on a display unit and / or transmitting it to a providing unit. In particular, providing a comparison dataset may include displaying a graphical representation of the comparison dataset, especially at least one parameter representing a deviation, via a display device. Furthermore, providing a comparison dataset may include displaying, especially overlaying or side-by-side, graphical representations of registered first and second datasets. Additionally, at least one display parameter of the display device used to display the graphical representation can be adjusted based on the comparison dataset, especially at least one parameter representing a deviation, for example, through color encoding.

[0033] The proposed implementation can advantageously enable quantitative analysis, particularly by providing a comparative dataset with at least one parameter representing a bias, of the temporal dynamics of the second contrast agent flow relative to the first contrast agent flow in the region of interest of the examined object.

[0034] In another advantageous embodiment of the proposed method, providing the first dataset may include receiving pre-acquired first image data. The first dataset can be reconstructed from the first image data. Furthermore, providing the second dataset may include receiving pre-acquired second image data. The second dataset can be reconstructed from the second image data.

[0035] Receiving the first and / or second image data may in particular include acquiring and / or reading from a computer-readable data storage device and / or receiving it from a data storage unit, such as a database. Furthermore, the first and / or second image data may be provided by a providing unit of one or more medical imaging devices, particularly those having the same or different imaging modalities.

[0036] The first image data can be mapped to the region of interest (ROI) of the object examined within a first time period, particularly at multiple first recording time points. The first image data can map the ROI at least partially, and particularly completely, each ROI. Furthermore, the first image data can map the ROI with at least partially different mapping geometries, particularly mapping directions, and / or at different first recording time points. The first image data can, for example, have layered images and / or projected images of the ROI within the first time period.

[0037] The second image data can be mapped to the region of interest (ROI) of the object examined within a second time period, particularly at multiple second recording time points. The second image data can map the ROI at least partially, and particularly completely, each time point. Furthermore, the second image data can map the ROI with at least partially different mapping geometries, particularly mapping directions, and / or at different second recording time points. The second image data can, for example, contain layered images and / or projected images of the ROI within the second time period.

[0038] Advantageously, the first dataset can be reconstructed from the first image data. Furthermore, the second dataset can be reconstructed from the second image data. In this case, the reconstruction of the first dataset may include spatial and / or temporal registration of the first image data relative to each other. Furthermore, the reconstruction of the first dataset may include applying 2D or 3D reconstruction, such as inverse Radon transform and / or filtered back projection and / or inverse Fourier transform, to the first image data. Similarly, the reconstruction of the second dataset may include spatial and / or temporal registration of the second image data relative to each other. Furthermore, the reconstruction of the second dataset may include applying 2D or 3D reconstruction, such as inverse Radon transform and / or filtered back projection and / or inverse Fourier transform, to the second image data. Here, the reconstructions of the first and second datasets may be the same or different. Furthermore, the reconstruction of the first and / or second datasets may include applying image artifact correction, particularly motion correction and / or metal artifact correction, to the corresponding image data.

[0039] The proposed implementation can achieve improved mapping of the first and second contrast agent flows in the region of interest of the examined object.

[0040] In another advantageous embodiment of the proposed method, the first image data may have a plurality of first projected images of the object to be examined from at least partially different, and particularly non-collinear, first projection directions. A time-resolved first dataset can be reconstructed from the first projected images. Furthermore, the second image data may have a plurality of second projected images of the object to be examined from at least partially different, and particularly non-collinear, second projection directions. A time-resolved second dataset can be reconstructed from the second projected images.

[0041] At least one medical imaging device for recording first and second image data may advantageously have a source and a detector, which are positioned in a defined arrangement relative to the object being examined, particularly the region of interest. In designs where the medical imaging device is designed as a medical X-ray device, particularly a medical C-arm X-ray device and / or a computed tomography (CT) system, the source may be an X-ray source and the detector may be an X-ray detector.

[0042] At least partially different first and second projection directions can respectively describe the trajectory of rays, particularly central rays and / or central rays, between the source and detector of at least one medical imaging device, particularly the center point of the detector, at the recording time point of the corresponding image data, especially at the first or second recording time point. In particular, the first and second projection directions can respectively describe the angle of at least one medical imaging device relative to the object being examined, particularly the region of interest, and / or the angle of the isocenter, particularly the rotation center, of the defined arrangement of the source and detector. In this case, the isocenter point can describe a spatial point around which the defined arrangement of the source and detector can move, particularly rotate, particularly during the recording of the first and second image data. Advantageously, the at least partially different first and second projection directions can each extend through a particularly common isocenter. In this case, the isocenter during the recording of the first image data can be located relative to the object being examined, particularly the region of interest, in the same or different manner as the isocenter during the recording of the second image data.

[0043] Advantageously, multiple first and second projection images can map 2D spatially resolved regions of interest (ROIs) of the examined object, respectively. Advantageously, a first dataset can be reconstructed from multiple first projection images such that the first dataset maps 3D spatially and temporally resolved ROIs. Furthermore, a second dataset can be reconstructed from multiple second projection images such that the second dataset maps 3D spatially and temporally resolved ROIs. In particular, the first and second datasets can be reconstructed from their respective projection images according to 4D DSA, respectively.

[0044] The proposed implementation can achieve improved, and particularly less obstructive, mapping of the first and second contrast agent flows in the region of interest of the examined object.

[0045] In another advantageous embodiment of the proposed method, the first image data and / or the second image data may have at least one mask image. Furthermore, the first image data may have multiple first padding images. Reconstruction of the first dataset may include subtracting at least one mask image from the multiple first padding images. Similarly, the second image data may have multiple second padding images. Reconstruction of the second dataset may include subtracting at least one mask image from the multiple second padding images.

[0046] Advantageously, the first and / or second image data may have at least one mask image, particularly at least one mask image each. If the first image data has at least one mask image, then the at least one mask image can map the region of interest temporally prior to the perfusion of the first contrast agent flow, particularly prior to the perfusion of the first contrast agent flow in at least one vascular segment. If the second image data has at least one mask image, then the at least one mask image can map the region of interest temporally prior to the perfusion of the second contrast agent flow, particularly prior to the perfusion of the second contrast agent flow in at least one vascular segment. Furthermore, the at least one mask image may have all the features and characteristics of the first and / or second image data, particularly the first and / or second projected images. Advantageously, the first and / or second image data may have multiple mask images, particularly mask images for each mapped geometry relative to the object being examined.

[0047] The first image data may have multiple first fill images. In this case, the first fill images may map to the first contrast agent flow in the region of interest of the object examined within a first time period, particularly at multiple first recording time points. The multiple first fill images may have all the features and characteristics of the first image data, particularly the first projected image.

[0048] Furthermore, the second image data may have multiple second filler images. In this case, the second filler images may map the second contrast agent flow of the object examined within a second time period, particularly at multiple second recording time points. The multiple second filler images may possess all the features and characteristics of the second image data, particularly the second projection image.

[0049] According to a first variant, a first dataset can be determined by subtracting at least one mask image from a plurality of first padded images. In particular, the first dataset may include a first difference image from which at least one mask image is subtracted from a plurality of first padded images. Furthermore, a second dataset can be determined by subtracting at least one mask image from a plurality of second padded images. In particular, the second dataset may include a second difference image from which at least one mask image is subtracted from a plurality of second padded images. If only the first image data or only the second image data has at least one mask image, then the at least one mask image can advantageously be spatially registered with the first and / or second padded images, particularly, before being subtracted. Alternatively, the first and second image data may each have at least one mask image, particularly at least one first mask image and at least one second mask image. In this case, the first dataset can be determined by subtracting at least one first mask image from a plurality of first padded images. Furthermore, the second dataset can be determined by subtracting at least one second mask image from a plurality of second padded images.

[0050] According to the second variation, the first and / or second image data may have multiple mask images that map the region of interest with at least partially different mapping geometries, particularly projection directions. In this case, at least one mask dataset can be advantageously reconstructed from the multiple mask images. Furthermore, the first dataset can be determined by subtracting at least one mask dataset from multiple first filled images. Similarly, the second dataset can be determined by subtracting at least one mask dataset from multiple second filled images. If the first and second image data each have multiple mask images, the corresponding mask datasets, particularly the first and second mask datasets, can be reconstructed from the mask images. In this case, the first dataset can be determined by subtracting the first mask dataset from multiple first filled images. Furthermore, the second dataset can be determined by subtracting the second mask dataset from multiple second filled images.

[0051] By subtracting at least one mask image, and in particular at least one mask dataset, from the first and second filled images respectively, image regions that are temporally and / or spatially immutable in mapping the region of interest can be advantageously removed, and in particular masked. This advantageously allows the first dataset to map substantially only the first contrast agent flow within a first time period. It also allows the second dataset to map substantially only the second contrast agent flow within a second time period.

[0052] In another advantageous embodiment of the proposed method, the registered first and second datasets may have multiple image points, each with a time-intensity curve. Identification of the deviation between the first and second contrast agent flows may include comparing the time-intensity curves of spatially corresponding image points in the registered first and second datasets, particularly comparing the slope, variance, mean intensity value, maximum intensity value, and / or cumulative intensity value of the time-intensity curves. Furthermore, the at least one parameter characterizing the deviation, describing the fill delay and / or flow rate ratio, can be determined by comparing the time-intensity curves of spatially corresponding image points in the registered first and second datasets.

[0053] The comparison of the temporal intensity curves of spatially corresponding image points in the registered first and second datasets can be performed, for example, at at least one time point after the start of the corresponding perfusion within the first and second time periods, for example, by comparing the intensity values ​​of the temporal intensity curves at at least one time point. Alternatively or additionally, the comparison of the temporal intensity curves of spatially corresponding image points in the registered first and second datasets can be performed over a predefined time period after the start of the corresponding perfusion within the first and second time periods, for example, by comparing the average intensity value and / or cumulative intensity value and / or intensity value change of the temporal intensity curves over the predefined time period. Alternatively or additionally, the comparison of the temporal intensity curves of spatially corresponding image points in the registered first and second datasets can be performed within a predefined range of intensity values, for example, by comparing the duration from the lower limit of the range of intensity values ​​of the temporal intensity curves of spatially corresponding image points in the registered first and second datasets until the upper limit of the range of intensity values ​​is reached or exceeded.

[0054] The slopes of the temporal intensity curves of spatially corresponding image points in the registered first and second datasets can map the perfusion velocities of the corresponding first and second contrast agent streams. Furthermore, the variance of the temporal intensity curves of spatially corresponding image points in the registered first and second datasets can map the perfusion of the region of interest. Additionally, the average intensity value can map the particularly average volumetric flow rate of the first and second contrast agent streams. Furthermore, the maximum intensity value can map the particularly maximum volumetric flow rate of the first and second contrast agent streams. Furthermore, the cumulative intensity value can map the contrast agent volume of the first and second contrast agent streams over a predefined time period. By comparing the slopes, variances, average intensity values, and / or cumulative intensity values ​​of the temporal intensity curves of spatially corresponding image points in the registered first and second datasets, deviations between the first and second contrast agent streams can be advantageously identified, and in particular quantified.

[0055] Advantageously, determining the parameter for at least one characterization bias can include quantifying the bias, particularly locally, regionally, or globally, by comparing the temporal intensity curves, especially the slope, variance, mean intensity value, maximum intensity value, and / or cumulative intensity value, of spatially corresponding image points in the registered first and second datasets. In particular, the parameter for at least one characterization bias can map the differences or ratios of the variance, mean intensity value, maximum intensity value, and / or cumulative intensity value of the temporal intensity curves of spatially corresponding image points in the registered first and second datasets. Furthermore, determining the parameter for at least one characterization bias can include quantifying the changes in hemodynamic parameters, especially hemodynamic parameters, between the first and second time periods based on the deviations in the slope, variance, mean intensity value, and / or cumulative intensity value of the temporal intensity curves of spatially corresponding image points in the registered first and second datasets. In this case, the parameter for at least one characterization bias can, for example, describe the filling delay and / or flow rate ratio. Here, the filling delay can describe the time difference between the perfusion of the first and second contrast agent flows until a predefined, especially normalized, intensity value is reached. Furthermore, the flow rate ratio can describe a particularly normalized ratio of the maximum or average volumetric flow rates of the first and second contrast agent flows. Advantageously, the filling delay can be determined by comparing the slopes and / or cumulative intensity values ​​of the time-intensity curves of spatially corresponding image points in the registered first and second datasets. Furthermore, the flow rate ratio can be determined by the average intensity value, the maximum intensity value, and / or the cumulative intensity value. Additionally, at least one parameter characterizing the deviation can describe the hemodynamic pressure and / or shear stress of the vessel wall and / or the flow velocity in at least one vessel segment. For this purpose, at least one parameter characterizing the deviation can be determined additionally based on a flow model, such as computational fluid dynamics (CFD), and / or by determining, for example, boundary conditions for perfusion velocity using the registered first and second datasets.

[0056] The proposed implementation can advantageously enable the quantification of the different temporal dynamics of the first and second contrast agent flows by biophysical quantities, such as filling delay and / or flow rate ratio.

[0057] In another advantageous embodiment of the proposed method, at least one first image point and at least one second image point can be determined in the registered first and second datasets, respectively, these image points mapping two different spatial locations within the at least one vascular segment. Furthermore, the spatial location mapped by the at least one second image point can be arranged downstream relative to the spatial location mapped by the at least one first image point. The bolus arrival time (BAT) can be determined as the time point at which the first and second contrast agent flows perfuse at the spatial location mapped by the at least one second image point. Furthermore, the deviation can be identified based on a comparison of the time-intensity curves of the at least one second image point and the at least one first image point in the registered first and second datasets, particularly a comparison of the slope, variance, mean intensity value, maximum intensity value, and / or cumulative intensity value of the time-intensity curves.

[0058] Determining at least one first pixel and at least one second pixel in the registered first and second datasets can be done manually, semi-automatically, or fully automatically. For example, at least one first pixel and / or at least one second pixel can be predetermined by a medical operator through input from an input unit, particularly by graphical representations of the registered first and / or second datasets. In particular, at least one first pixel or at least one second pixel can be predetermined by the input of a medical operator. In this case, at least one additional pixel can be determined automatically, for example, by using anatomical atlases and / or segmentation of at least one vascular segment. Alternatively, at least one first pixel and at least one second pixel can be predetermined by the input of a medical operator. Furthermore, at least one first pixel and at least one second pixel can be determined fully automatically, for example, by segmentation of the region of interest and at least one vascular segment in the registered first and second datasets.

[0059] The two distinct spatial locations can be mapped by one or more image points from the registered first and second datasets, respectively, particularly by at least one first image point and at least one second image point. Furthermore, the spatial location mapped by at least one second image point can be positioned downstream of the spatial location mapped by at least one first image point, particularly along the flow direction and / or perfusion direction in at least one vascular segment. Thus, the spatial location mapped by at least one first image point can be perfused by the first and second contrast agent flows prior to the spatial location mapped by at least one second image point in time.

[0060] Advantageously, the arrival time of the contrast agent clump at the spatial location mapped by at least one second image point for the first and second contrast agent streams can be determined by comparing the corresponding time-intensity curves with a predetermined intensity threshold. Reaching or exceeding the predetermined intensity threshold can indicate the start of perfusion of the first or second contrast agent stream at the spatial location mapped by at least one second image point. Advantageously, the arrival time of the contrast agent clump at the spatial location mapped by at least one second image point for the first and second contrast agent streams can identify the point and / or segment with the maximum slope of the time-intensity curve of at least one first image point in the registered first and second datasets. Advantageously, the deviation can be identified based on a comparison of the time-intensity curves of at least one first image point in the registered first and second datasets at the identified point.

[0061] The proposed implementation enables a precise comparison of the time dynamics of the first and second contrast agent flows.

[0062] In another advantageous embodiment of the proposed method, at least one first image point and at least one second image point can be determined in the registered first and second datasets, respectively, these image points mapping two different spatial locations within the at least one vascular segment. In this case, identifying the deviation between the first and second contrast agent flows may include determining a first ratio between the time-intensity curves, particularly the slope, variance, mean intensity value, maximum intensity value, and / or cumulative intensity value of the time-intensity curves, for at least one first image point in the registered first and second datasets. Furthermore, identifying the deviation may include determining a second ratio between the time-intensity curves, particularly the slope, variance, mean intensity value, maximum intensity value, and / or cumulative intensity value, for at least one second image point in the registered first and second datasets. Additionally, identifying the deviation may include determining a third ratio between the first and second ratios, wherein the deviation is identified in conjunction with the third ratio.

[0063] Because the two different spatial locations are arranged along different spatial segments of at least one blood vessel, the time points at which the first and second contrast agent flows are perfused at these two different spatial locations can be different from each other. In particular, the spatial location mapped by at least one first image point can be perfused by the first and second contrast agent flows respectively before the spatial location mapped by at least one second image point.

[0064] Determining a first ratio between the temporal intensity curves of at least one first image point in the registered first and second datasets may include determining a first quotient or a first difference between the temporal intensity curves of at least one first image point in the registered first dataset and the temporal intensity curves of at least one first image point in the registered second dataset. Therefore, the first ratio may map the deviation between the first and second contrast agent flows at the spatial location mapped by the at least one first image point.

[0065] Furthermore, determining the second ratio between the temporal intensity curves of at least one second image point in the registered first and second datasets may include determining a second quotient or a second difference between the temporal intensity curves of at least one second image point in the registered first dataset and the temporal intensity curves of at least one second image point in the registered second dataset. Therefore, the second ratio may map the deviation between the first and second contrast agent flows at the spatial location mapped by the at least one second image point.

[0066] Determining the third ratio may include determining the quotient or difference between the first ratio and the second ratio, particularly the quotient or difference between the first quotient and the second quotient, or the first difference and the second difference. Here, in addition to the deviation between the first and second contrast agent flows at spatial locations mapped by at least one first image point and at least one second image point, the third ratio may also map the spatial deviation between the first and second contrast agent flows at two different spatial locations.

[0067] The proposed implementation can advantageously achieve spatiotemporal comparison between the first and second contrast agent streams.

[0068] In another advantageous embodiment of the proposed method, the region of interest may have vascular malformations, particularly arteriovenous malformations and / or venous malformations and / or capillary malformations, and / or stenosis and / or aneurysm. The at least one vascular segment may be afferent, particularly for supply, and / or efferent, particularly for export, relative to the vascular malformation and / or stenosis and / or aneurysm. Here, at least one first image point maps an internal or proximal spatial location relative to the vascular malformation and / or stenosis and / or aneurysm, respectively, and at least one second image point maps a distal spatial location. Alternatively, at least one first image point maps a proximal spatial location relative to the vascular malformation and / or stenosis and / or aneurysm, respectively, and at least one second image point maps an internal spatial location.

[0069] The proximal spatial location relative to the vascular malformation and / or stenosis and / or aneurysm can represent a spatial location in at least one vascular segment that is perfused with first and second contrast agent flows prior to the vascular malformation and / or stenosis and / or aneurysm. In particular, the proximal spatial location can be arranged in the afferent portion of at least one vascular segment. In this case, the spatial location arranged proximal to the vascular malformation and / or stenosis and / or aneurysm can be arranged in at least one vascular segment in a particularly direct spatial proximity to the vascular malformation and / or stenosis and / or aneurysm, particularly adjacent to or spatially spaced from the vascular malformation and / or stenosis and / or aneurysm.

[0070] Furthermore, the distal spatial location relative to the vascular malformation and / or stenosis and / or aneurysm can represent a spatial location in at least one vascular segment that is perfused temporally by the first and second contrast agent flows after the vascular malformation and / or stenosis and / or aneurysm. In particular, the distal spatial location can be arranged in the efferent portion of at least one vascular segment. In this case, the spatial location distal to the vascular malformation and / or stenosis and / or aneurysm can be arranged in at least one vascular segment in a particularly direct spatial proximity to the vascular malformation and / or stenosis and / or aneurysm, particularly adjacent to or spatially spaced from the vascular malformation and / or stenosis and / or aneurysm.

[0071] Advantageously, two different spatial locations, proximal, distal, or internal to vascular malformations and / or stenosis and / or aneurysms, can be arranged through at least one vascular segment that is hemodynamically connected.

[0072] Because the two different spatial locations are arranged in different spaces within at least one vascular segment, the time points at which the first and second contrast agent flows are perfused at these two different spatial locations can be different from each other. In particular, the spatial location mapped by at least one first image point can be perfused by the first and second contrast agent flows respectively before the spatial location mapped by at least one second image point.

[0073] Advantageously, changes in vascular malformations, stenosis, and / or aneurysms, particularly those resulting from treatment and / or surgery and / or intervention, may have occurred between the first and second time periods, especially before the initiation of the method. Therefore, within or distal to the vascular malformation and / or stenosis and / or aneurysm, flow dynamics, particularly at least one hemodynamic parameter, may have changed. Advantageously, the altered flow dynamics can be mapped by a third ratio, and in particular, the injection dynamics are removed from the altered flow dynamics.

[0074] The proposed implementation can advantageously enable quantitative comparison of the first and second contrast agent flows in order to assess changes in vascular malformations, stenosis and / or aneurysms.

[0075] In another advantageous embodiment of the proposed method, injection parameters for the first and second contrast agent streams can be received separately. Furthermore, multiple time-intensity curves can be normalized based on the injection parameters. Identification of deviations between the first and second contrast agent streams can include a comparison of normalized time-intensity curves of spatially corresponding image points from registered first and second datasets.

[0076] Receiving injection parameters for the first and second contrast agent streams may, in particular, include acquiring and / or reading from a computer-readable data storage unit and / or receiving from a data storage unit, such as a database. Alternatively, the injection parameters may be provided by the supply unit of the injection device, especially an automated injection device. Alternatively or additionally, the injection parameters may be received by a medical operator via additional input through an input unit.

[0077] The injection parameters for the first and second contrast agent flows can describe the injection rate, particularly the volumetric flow rate and / or injection pressure and / or contrast agent concentration and / or injection time and / or spatial catheter position, respectively. By standardizing the time-intensity curves based on the injection parameters, deviations between the first and second contrast agent flows can be identified, and the injection dynamics of the first and second contrast agent flows can be eliminated from these deviations.

[0078] In a second aspect, the present invention relates to a providing unit designed to implement the proposed method for providing a comparison dataset.

[0079] The advantages of the proposed providing unit are substantially consistent with the advantages of the proposed method for providing a comparison dataset. The features, advantages, or alternative embodiments mentioned herein can also be applied to other claimed technical solutions, and vice versa.

[0080] The providing unit may advantageously include a computing unit, a storage unit, and / or an interface. Here, the providing unit, and particularly its components, may be designed to implement the individual steps of the proposed method for providing a comparison dataset. In particular, the interface may be designed to provide time-resolved first and second datasets and to provide a comparison dataset. Furthermore, the computing unit and / or storage unit may be designed to register the first and second datasets spatially and temporally, and to identify discrepancies between the first and second contrast agent flows.

[0081] In a third aspect, the present invention relates to a medical imaging apparatus having a proposed providing unit, wherein the medical imaging apparatus is designed to record and / or provide a first dataset and a second dataset.

[0082] Medical imaging equipment can be designed, for example, as magnetic resonance imaging (MRT) systems and / or computed tomography (CT) systems and / or medical X-ray equipment and / or positron emission tomography (PET) systems and / or ultrasound equipment. Furthermore, the medical imaging equipment can be designed to record first and second datasets of an object being examined, including a region of interest. Alternatively, the medical imaging equipment can be designed to record first and second image data, particularly first and second projected images, of the object being examined, including a region of interest. In this case, the medical imaging equipment can also be designed to reconstruct and provide a first dataset from the first image data. Furthermore, the medical imaging equipment can be designed to reconstruct and provide a second dataset from second image data.

[0083] The advantages of the proposed medical imaging device are substantially consistent with the advantages of the proposed method for providing comparative datasets and / or the proposed providing unit. The features, advantages, or alternative embodiments mentioned herein can also be applied to other claimed technical solutions, and vice versa.

[0084] In a fourth aspect, the present invention relates to a computer program product having a computer program that can be directly loaded into the memory of a providing unit, the computer program having multiple program segments so as to implement all steps of a method for providing a comparison dataset when these program segments are executed by the providing unit.

[0085] The present invention may also relate to a computer-readable storage medium on which a plurality of program segments capable of being read and executed by a providing unit are stored, so that when these program segments are executed by the providing unit, all steps of the method for providing a comparison dataset are implemented.

[0086] The largely software-based implementation has the following advantages: currently used supply units and / or training units can also be easily updated and modified via software to operate in accordance with the invention. In addition to the computer program, such a computer program product may, if necessary, include additional components, such as documentation and / or additional parts, and hardware components, such as hardware keys (dongles, etc.) for software use. Attached Figure Description

[0087] Embodiments of the invention are shown in the accompanying drawings and described in more detail below. The same features in different figures are referred to by the same reference numerals. In the drawings:

[0088] Figure 1 An advantageous implementation of the proposed method for providing comparative datasets is illustrated schematically;

[0089] Figure 2The temporal intensity curves of the registered first and second datasets, which correspond to each other spatially, are schematically shown.

[0090] Figures 3 to 5 Further advantageous implementations of the proposed method for providing comparative datasets are illustrated schematically;

[0091] Figure 6 The region of interest, which includes an aneurysm and at least one vascular segment, is schematically shown.

[0092] Figure 7 and Figure 8 The temporal intensity curves at different spatial locations along the vascular segment are schematically shown.

[0093] Figure 9 The proposed supply unit is shown schematically;

[0094] Figure 10 The proposed medical imaging device is shown schematically. Detailed Implementation

[0095] Figure 1 An advantageous implementation of the proposed method for providing a PROV-CD comparison dataset CD is illustrated schematically. In this method, a first dataset D1, time-resolved (PROV-D1), can be provided, which maps to a first contrast agent flow in the region of interest (ROI) of the examined object during a first time period. A second dataset D2, time-resolved (PROV-D2), can also be provided, which maps to a second contrast agent flow in the ROI of the examined object during a second time period following the first time period. Furthermore, the first dataset D1 and the second dataset D2 can be spatially registered to each other (SREG-D1-D2). Furthermore, the perfusion direction of the mappings of the first and / or second contrast agent flows can be used to identify at least one, particularly incoming, vascular segment of the ROI in the ID-V first dataset D1 and the second dataset D2. Furthermore, the first dataset D1 and the second dataset D2 can be temporally registered to minimize the time difference in perfusion between the mappings of the first and second contrast agent flows in at least one vascular segment. Furthermore, the deviation between the ID-DIFF first and second contrast agent flows can be identified by comparing the registered first dataset D1.REG and the registered second dataset D2.REG. In addition, a PROV-CD comparison dataset CD can be provided based on the first registered dataset D1.REG and the second registered dataset D2.REG, wherein the comparison dataset CD has at least one parameter P.DIFF representing the bias.

[0096] Figure 2A schematic diagram showing the spatially corresponding temporal intensity curves of the registered first dataset D1.REG and the registered second dataset D2.REG. Advantageously, the registered first dataset D1.REG and the registered second dataset D2.REG can have multiple image points, each with its own temporal intensity curve I. D1.REG and I D2.REG Here, the identification of the deviation between the first and second contrast agent flows, ID-DIFF, can include the temporal intensity curves I of spatially corresponding image points of the registered first dataset D1.REG and the registered second dataset D2.REG. D1.REG and I D2.REG The comparison, especially the time-intensity curve I D1.REG and I D2.REG The slope, variance, mean intensity value, maximum intensity value, and / or cumulative intensity value are compared. Furthermore, the at least one parameter P.DIFF characterizing the bias can be obtained using the temporal intensity curves I of spatially corresponding image points from the registered first dataset D1.REG and the registered second dataset D2.REG. D1.REG and I D2.REG The parameters are determined by comparison, and describe the fill delay and / or flow rate ratio.

[0097] By spatial and temporal registration of the first dataset D1.REG and the second dataset D2.REG, the temporal intensity curves that correspond to each other spatially are I. D1.REG and I D2.REG The perfusion can be mapped relative to the first and second contrast agent flows, especially relative to the arrival time t of the contrast agent mass. BAT Temporal registration. Advantageously, it allows for the comparison of spatially corresponding temporal intensity curves I. D1.REG and I D2.REG This is to determine the parameter P.DIFF, which characterizes the bias. For example, the arrival time t of the contrast agent mass can be determined. BAT and until the time intensity curve I is reached or exceeded D1.REG and I D2.REG The time difference between predetermined intensity threshold time points. For example, the maximum intensity value can be predetermined for the intensity threshold, especially the fill intensity value I. Fill The intensity value is 33%. In this case, the arrival time t of the contrast agent mass can be determined. BAT And correspondingly until the predetermined intensity threshold is reached or exceeded, especially 33% I Fill Time point t 33％，D1.REG t 33％，D2.REG The time difference between them. Alternative locations can determine the arrival time t of the contrast agent bolus. BAT and until a predetermined area threshold is reached or exceeded under the time-intensity curve, for example, from the time t of the contrast agent mass arrival.BAT The time difference between the points where the area under the initial intensity curve is 5% of the total area at that time. By comparing the time difference (t... 33％ , D1.REG -t BAT ) and (t 33％，D2.REG -t BAT It can identify changes in the filling rate of at least one vascular segment between the first and second time periods.

[0098] Figure 3 A schematic diagram illustrating another advantageous embodiment of the proposed method for providing a PROV-CD comparison dataset CD is shown. The provision of the first dataset D1, PROV-D1, includes receiving first image data ID1 pre-acquired by REC-ID1. Furthermore, RECO-D1, the first dataset D1, can be reconstructed from the first image data ID1. Similarly, the provision of the second dataset D2, PROV-D2, may include receiving second image data D2 pre-acquired by REC-ID2. The second dataset D2 can be reconstructed from the second image data ID2.

[0099] Advantageously, the first image data ID may have multiple first projected images of the object to be inspected from at least partially different first projection directions. A time-resolved first dataset D1 can be reconstructed from the first projected images to form RECO-D1. Furthermore, the second image data ID2 may have multiple second projected images of the object to be inspected from at least partially different second projection directions. A time-resolved second dataset D2 can be reconstructed from the second projected images to form RECO-D2.

[0100] Figure 4 Further advantageous embodiments of the proposed method for providing a PROV-CD comparison dataset CD are illustrated schematically. Here, the first image data ID1 and / or the second image data ID2 may have at least one mask image, particularly mask images MI1 and MI2 respectively. Furthermore, the first image data ID1 may have multiple first padding images FI1. The reconstruction RECO-D1 of the first dataset D1 may include subtracting at least one mask image MI1 from the multiple first padding images FI1 by subtracting DIFF-MI1-FI1. Similarly, the second image data ID2 may have multiple second padding images FI2. The reconstruction RECO-ID2 of the second dataset D2 may include subtracting at least one mask image MI2 from the multiple second padding images FI2 by subtracting DIFF-MI2-FI2.

[0101] Figure 5A schematic diagram illustrating another advantageous embodiment of the proposed method for providing a PROV-CD comparison dataset CD is shown. Here, injection parameters IP for the first and second contrast agent streams (REC-IP) can be received separately. Furthermore, the time-intensity curves can be normalized based on the injection parameters IP. Additionally, the identification ID-DIFF of the deviation between the first and second contrast agent streams can include a comparison of normalized time-intensity curves of spatially corresponding image points from a registered first dataset D1.REG and a registered second dataset D2.REG.

[0102] exist Figure 6 An exemplary region of interest is schematically shown in the diagram, comprising an aneurysm AV and at least one vascular segment V. The at least one vascular segment V may be afferent, particularly for supply, and / or efferent, particularly for export, relative to the aneurysm AV. Herein, Figure 6 The direction of flow FD in at least one vascular segment V, particularly the perfusion direction of the first and second contrast agent flows, is indicated by arrows.

[0103] Advantageously, at least one first image point and at least one second image point can be determined in the first registered dataset D1.REG and the second registered dataset D2.REG, respectively, these image points mapping two distinct spatial locations within at least one vascular segment V. Specifically, relative to the aneurysm AV, at least one first image point can map to either the internal spatial location P2 or the proximal spatial location P1, and at least one second image point can map to the distal spatial location P3. Alternatively, relative to the aneurysm AV, at least one first image point can map to the proximal spatial location P1, and at least one second image point can map to the internal spatial location P2. Hereinafter, these three distinct spatial locations P1, P2, and P3 are also referred to as the first spatial location P1, the second spatial location P2, and the third spatial location P3.

[0104] Advantageously, the ID-DIFF for identifying the deviation between the first and second contrast agent flows may include determining a first ratio between the time-intensity curves, particularly the slope, variance, mean intensity value, maximum intensity value, and / or cumulative intensity value of the time-intensity curves, for at least one first image point in the registered first dataset D1.REG and second dataset D2.REG. Furthermore, the ID-DIFF for identifying the deviation may include determining a second ratio between the time-intensity curves, particularly the slope, variance, mean intensity value, maximum intensity value, and / or cumulative intensity value of the time-intensity curves, for at least one second image point in the registered first dataset D1.REG and second dataset D2.REG. A third ratio between the first and second ratios may also be determined, wherein the ID-DIFF deviation is identified by means of the third ratio.

[0105] exist Figure 7The image shows the temporal intensity curves of the first registered dataset D1.REG in schematic diagram I. D1.REG,P1 I D1.REG,P2 and I D1.REG,P3 These image points map at least one vascular segment V in Figure 6 The image shows different spatial locations P1, P2, and P3. The following describes the temporal intensity curves (I) of the image points from the first registered dataset D1.REG. D1.REG,P1 I D1.REG,P2 and I D1.REG,P3 The comparison and / or analysis can be similarly applied to the temporal intensity curves of the image points in the second dataset D2.REG for registration. D2.REG,P1 I D2.REG,P2 and I D2.REG,P3 (Not shown here).

[0106] Favorably, time-intensity curve I D1.REG,P1 I D1.REG,P2 and I D1.REG,P3 The perfusion can be mapped separately in time relative to the first contrast agent stream, especially relative to the arrival time t of the contrast agent bolus. BAT Register in time.

[0107] To determine the first and second ratios, the arrival time t of the contrast agent mass can be determined. BAT The time difference between the time point until the predetermined intensity threshold of the corresponding time-intensity curve is reached or exceeded. For example, the maximum intensity value can be predetermined for the intensity threshold, especially the fill intensity value I. Fill 33% of the strength value.

[0108] For example, relative to the aneurysm AV, at least one first image point can map the proximal spatial location P1, and at least one second image point can map the distal spatial location P3. In this case, the arrival time t of the contrast agent bolus can be determined. BAT And the corresponding time intensity curve I until it reaches or exceeds at least one first image point. D1.REG,P1 and I D2.REG,P1 The predetermined intensity threshold, especially 33% I Fill Time point t 33％,D1.REG,P1 t 33％,D2.REG,P1 Time difference between them:

[0109] R P1 =(t 33％，D2.REC，P1 -t 33％，D1.REG，P1 (1). In addition, the second ratio can be determined as the arrival time t of the contrast agent mass. BAT And the corresponding time intensity curve I until it reaches or exceeds at least one second image point. D1.REG,P3 and I D2.REG,P3 The predetermined intensity threshold, especially 33% IFill Time point t 33％,D1.REG,P3 t 33％,D2.REG,P3 Time difference between them:

[0110] R P3 =(t 33％，D2，REG，P3 -t 33％，D1，REG，P3 (2).

[0111] In addition, the third ratio DR P3,P1 It can be determined as the second ratio R P3 And the first ratio R P1 Difference:

[0112] DR P3，P1 =R P3 -R P1

[0113] =(t 33％，D2，REG，P3 -t 33％，D1，REG，P3 )-(t 33％，D2，REG，P1 -t 33％，D1，REG，P1 (3).

[0114] In this case, the third ratio DR P3,P1 The filling delay can be described as the change in third spatial position P3 relative to first spatial position P1 between the first and second time periods. At least one parameter P.DIFF characterizing the deviation can, for example, include the third ratio DR. P3,P1 .

[0115] Alternatively, relative to the aneurysm AV, at least one first image point can map the internal spatial location P2, and at least one second image point can map the distal spatial location P3. In this case, the calculation of the first ratio RP2 can be adapted accordingly to the second spatial location P2 mapped by at least one first image point. Furthermore, the third ratio DR... P3,P2 The filling delay that can be described by the change of the third spatial position P3 relative to the second spatial position P2 between the first and second time periods is:

[0116] DR P3，P2 =(R P3 -R PZ )

[0117] =(t 33％，D2，REG，P3 -t 33％D1，REG，P3 )-(t 33％，D2，REG，P2 -t 33％，D1，REG，P2 (4).

[0118] Figure 8 The temporal intensity curves (I) of the image points in the first registered dataset D1.REG are shown. D1.REG,P1 I D1.REG,P2 and I D1.REG,P3The schematic diagram shows that these image points map to at least one vascular segment V in... Figure 6 The different spatial locations P1, P2, and P3 are shown in the figure. Here, the time-intensity curve I... D1.REG,P1 I D1.REG,P2 and I D1.REG,P3 The same slope is shown only for the sake of illustration. Furthermore, the time-intensity curve I... D1.REG,P1 I D1.REG,P2 and I D1.REG,P3 In this implementation, they are not time-registered with each other.

[0119] To determine the relative flow changes between a first spatial location of interest IP1, particularly a first spatial location P1 and / or a second spatial location P2, and a first reference location RP1, particularly a third spatial location P3, located downstream of the first spatial location of interest IP1 in at least one vascular segment V, Fick's law, particularly the law of conservation of mass, can be applied:

[0120]

[0121]

[0122] Among them, Q IP1 V can represent the cumulative mass of the contrast agent. IP1 C represents volumetric flow rate. IP1 and C RP1 This indicates the contrast agent concentration at the corresponding spatial location IP1 or RP1.

[0123] The contrast agent accumulation rate can reach a local maximum when the difference in contrast agent concentration between the first spatial location of interest (IP1) and the first spatial reference location (RP1) becomes maximum. This local maximum can occur at the time when the first and / or second contrast agent flow is perfused at the downstream first reference location (RP1), particularly at the time t when the contrast agent mass reaches the first spatial reference location (RP1). BAT,RP1 achieve.

[0124] Therefore, from equation (6), we get:

[0125]

[0126]

[0127] Because of C IP1 ∝I IP1 ,therefore

[0128] C IP1 (t BAT，RP1 )=k·I IP1 (t BAT，RP1 (9).

[0129] From equations (8) and (9), we can derive:

[0130]

[0131] Among them, V rel，IP1 =K·V IP1 .

[0132] Furthermore, from equations (7) and (9), we can derive:

[0133]

[0134] The ratio between the relative volumetric flows mapped in the first and second registered datasets D1.REG and D2.REG is derived according to equation (10):

[0135]

[0136] in this case, Describe the ratio of volumetric flow rate at a first location of interest IP1 in at least one vascular segment V during the first and second time periods.

[0137] Assuming the injection parameters of the first and second contrast agent flows are constant and the vessel volume in at least one vessel segment remains substantially the same between the first and second time periods, it can be assumed that...

[0138]

[0139] From equations (11) to (13), we can derive:

[0140]

[0141] By using the relative volumetric flow rate for the first spatial location of interest, IP1 Relative volumetric flow rate to the second spatial location of interest, IP2 The flow rate FR can be determined using equation (14) for comparison. IP1，IP2 :

[0142]

[0143] Here, the second spatial location of interest (IP2) can represent an additional spatial location within at least one vascular segment V that differs from the first spatial location of interest (IP1). Furthermore, a second spatial reference location (RP2) can be located downstream of the second spatial location of interest (IP2) within at least one vascular segment V. Specifically, the first reference location (RP1) and the second reference location (RP2) can be the same or different.

[0144] Using the second spatial location P2 as the first location of interest IP1 and the third spatial location P3 as the first reference location RP1 and also as the second location of interest IP2, the relative flow FR is obtained. P2，P3 :

[0145]

[0146] RP2 is located downstream of the third spatial location P3.

[0147] Figure 9 A schematic diagram illustrating an advantageous embodiment of the proposed providing unit PRVS is shown. The providing unit PRVS may advantageously include a computing unit CU, a storage unit MU, and / or an interface IF. In this case, the providing unit PRVS, and in particular its components, may be designed to implement the individual steps of the proposed method for providing the PROV-CD comparison dataset CD. Specifically, the interface IF may be designed to provide time-resolved first and second datasets of PROV-D1 and PROV-D2 and to provide the PROV-CD comparison dataset. Furthermore, the computing unit CU and / or the storage unit MU may be designed to spatially register SREG-D1-D2 and temporally register the first dataset D1 and the second dataset D2 of TREG-D1-D2, and to identify the deviation between the first and second contrast agent flows of ID-DIFF.

[0148] Figure 10 A schematic diagram of a medical C-arm X-ray device 37 is shown as an example of the proposed medical imaging apparatus. Here, the medical C-arm X-ray device 37 advantageously includes a detector 34, particularly an X-ray detector, and an X-ray source 33. The medical C-arm X-ray device 37 can be advantageously designed to record and / or provide a first dataset D1 and a second dataset D2. In particular, the medical C-arm X-ray device can be designed to record first image data ID1 comprising a plurality of first projected images of the subject 31 under examination and second image data ID2 comprising a plurality of second projected images of the subject 31 under examination.

[0149] To record the first and second projected images, the arm 38 of the C-arm X-ray device 37 is movably supported about one or more axes. Furthermore, the medical C-arm X-ray device 37 may include a movement device 39 capable of moving the C-arm X-ray device 37 in space. Additionally, the providing unit PRVS for recording the first and second projected images of the subject 31 arranged on the patient support device 32 can send a signal 24 to the X-ray source 33. The X-ray source 33 can then emit an X-ray beam. When the X-ray beam strikes the surface of the detector 34 after interacting with the subject 31, the detector 34 can send a signal 21 to the providing unit PRVS. The providing unit PRVS can, for example, receive the first and second projected images by means of signal 21.

[0150] Furthermore, the system may include an input unit 42, such as a keyboard, and a display device 41, such as a monitor and / or display. The input unit 42 may preferably be integrated into the display device 41, for example, in the case of a capacitive and / or resistive input display. In this case, control of the medical C-arm X-ray device 37 can be achieved through input by a medical operator at the input unit 42, particularly enabling the proposed method for providing the PROV-CD comparison dataset CD. For this purpose, the input unit 42 may, for example, send a signal 26 to the providing unit PRVS.

[0151] Display device 41 can be advantageously designed for displaying, in particular, superimposed or side-by-side, graphical representations of the registered first dataset D1.REG and / or the registered second dataset D2.REG and / or the comparison dataset CD, especially at least one parameter P.DIFF representing the deviation. For this purpose, the providing unit PRVS can send signal 25 to display device 41. Furthermore, at least one display parameter of display device 41 for displaying the graphical representation can be adjusted, for example, by color encoding, based on the comparison dataset CD, especially at least one parameter P.DIFF representing the deviation.

[0152] The schematic diagrams included in the described figures do not depict scale or size proportions in any way.

[0153] Finally, it should be reiterated that the methods and apparatus described in detail above are merely embodiments, and those skilled in the art can modify these embodiments in different ways without departing from the scope of the invention. Furthermore, the indefinite article "a" does not exclude the existence of multiple related features. Similarly, the terms "unit" and "element" do not exclude the possibility that a related component is composed of multiple sub-components that work together, and these sub-components may, if necessary, be spatially distributed.

Claims

1. A method for providing a (PROV-CD) comparison dataset (CD), the method comprising: - Provides a time-resolved first dataset (D1) (PROV-D1), which maps to a first contrast agent flow in the region of interest of the examined object (31) within a first time period; - Provides a second time-resolved dataset (D2) (PROV-D2), which maps to the second contrast agent flow in the region of interest of the examined object (31) within a second time period following the first time period; - Spatial registration (SREG-D1-D2) of the first dataset (D1) and the second dataset (D2); - Identify (ID-V) images of at least one particularly incoming vascular segment (V) of the region of interest in the first dataset (D1) and the second dataset (D2) by means of the perfusion direction mapped by the first and / or second contrast agent flows; - Register (TREG-D1-D2) the first dataset (D1) and the second dataset (D2) in time to minimize the time difference of perfusion of the mapping of the first and second contrast agent flows in the at least one vascular segment (V); - The discrepancy between the first and second contrast agent flows was identified (ID-DIFF) by comparing the first registered dataset (D1.REG) and the second registered dataset (D2.REG); -The comparison dataset (CD) is provided based on the first registered dataset (D1.REG) and the second registered dataset (D2.REG). The comparison dataset (CD) has at least one parameter (P.DIFF) characterizing the bias.

2. The method of claim 1, wherein, The provision (PROV-D1) of the first dataset (D1) includes receiving (REC-ID1) pre-acquired first image data (ID1). The first dataset (D1) is reconstructed (RECO-D1) from the first image data (ID1). The provision (PROV-D2) of the second dataset (D2) includes receiving (REC-ID2) pre-acquired second image data (ID2). The second dataset (D2) is reconstructed from the second image data (ID2) (RECO-D2).

3. The method according to claim 2, wherein The first image data (ID) has multiple first projected images of the inspected object (31) from at least partially different first projection directions. The time-resolved first dataset (D1) is reconstructed from the first projected image. The second image data (ID2) comprises multiple second projection images of the object being inspected (31) from at least partially different second projection directions. The time-resolved second dataset (D2) is reconstructed from the second projected image.

4. The method according to claim 2 or 3, wherein The first image data (ID1) and / or the second image data (ID2) have at least one mask image (MI1, MI2). The first image data (ID1) has multiple first filled images (FI1). The reconstruction (RECO-D1) of the first dataset (D1) includes subtracting at least one mask image (MI1, MI2) from a plurality of first filled images (FI1) by subtracting (DIFF-MI1-FI1). The second image data (ID2) has multiple second filled images (FI2). The reconstruction (RECO-D2) of the second dataset (D2) includes subtracting at least one mask image (MI1, MI2) from a plurality of second padding images (FI2) by (DIFF-MI2-FI2).

5. The method according to any one of the preceding claims, wherein, The first registered dataset (D1.REG) and the second registered dataset (D2.REG) each have multiple image points, and these image points each have a time-intensity curve. The identification of the deviation between the first and second contrast agent flows includes comparing the temporal intensity curves of spatially corresponding image points in the registered first dataset (D1.REG) and the registered second dataset (D2.REG), particularly comparing the slope, variance, mean intensity value, maximum intensity value, and / or cumulative intensity value of the temporal intensity curves. Specifically, the at least one characterization bias parameter (P.DIFF) is determined by comparing the temporal intensity curves of spatially corresponding image points in the first registered dataset (D1.REG) and the second registered dataset (D2.REG), the parameter describing the fill delay and / or flow ratio.

6. The method according to claim 5, wherein At least one first image point and at least one second image point are determined in the first registered dataset (D1.REG) and the second registered dataset (D2.REG), respectively. These first and second image points map to two different spatial locations within the at least one vascular segment (V). Wherein, the spatial position mapped by at least one second image point is arranged downstream relative to the spatial position mapped by at least one first image point. The arrival time of the contrast agent mass is defined as the time point at which the first and second contrast agent streams are infused at the spatial location mapped by at least one second image point. The deviation (ID-DIFF) is identified based on the arrival time of the contrast agent mass at at least one second pixel and the comparison of the temporal intensity curves of at least one first pixel in the registered first dataset (D1.REG) and the registered second dataset (D2.REG).

7. The method according to claim 5, wherein At least one first image point and at least one second image point are determined in the first registered dataset (D1.REG) and the second registered dataset (D2.REG), respectively. These image points map to two different spatial locations within the at least one vascular segment (V). The identification of the deviation (ID-DIFF) between the first and second contrast agent flows includes: - Determine the first ratio between the temporal intensity curves of at least one first image point in the first registered dataset (D1.REG) and the second registered dataset (D2.REG); - Determine a second ratio between the temporal intensity curves of at least one second image point in the first registered dataset (D1.REG) and the second registered dataset (D2.REG); - Determine a third ratio between the first ratio and the second ratio. Among them, the third ratio (ID-DIFF) is used to identify the bias (DIFF).

8. The method according to claim 6 or 7, wherein The region of interest has vascular malformations and / or stenosis and / or aneurysm (AV). Wherein, the at least one vascular segment (V) is afferent and / or efferent relative to vascular malformation and / or stenosis and / or aneurysm (AV). Wherein, the at least one first image point maps to the internal or proximal spatial location relative to vascular malformation and / or stenosis and / or aneurysm (AV), respectively, and the at least one second image point maps to the distal spatial location; Alternatively, the at least one first image point maps to a proximal spatial location relative to vascular malformation and / or stenosis and / or aneurysm (AV), respectively, and the at least one second image point maps to an internal spatial location.

9. The method according to any one of claims 5 to 8, wherein The injection parameters (IP) for the first and second contrast agent streams are received (REC-IP) respectively. Among them, multiple time-intensity curves were standardized based on injection parameters (NORM-IC). The identification of the deviation (DIFF) between the first and second contrast agent flows (ID-DIFF) includes a comparison of the normalized temporal intensity curves of spatially corresponding image points in the registered first dataset (D1.REG) and the registered second dataset (D2.REG).

10. A providing unit (PRVS) designed to implement the method according to any one of the preceding claims.

11. A medical imaging device comprising a providing unit (PRVS) according to claim 10, wherein, The medical imaging device is designed to record and / or provide (PROV-D1, PROV-D2) a first dataset (D1) and a second dataset (D2).

12. A computer program product having a computer program capable of being directly loaded into the memory of a providing unit (PRVS), the computer program having a plurality of program segments for implementing all steps of the method according to any one of claims 1 to 9 when the program segments are executed by the providing unit (PRVS).

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