PROVIDING A BLOOD FLOW PARAMETER SET FOR A VASCULAR MALFORMATION

Abstract

A computer-implemented method for providing a blood flow parameter set for a vascular malformation includes receiving time-resolved image data. The image data maps a change over time in a vessel section of an examination subject. The vessel section includes the vascular malformation. A time-resolved image of the vessel section is reconstructed from the image data. The vascular malformation is segmented in the image of the vessel section. An afferent and an efferent vessel are identified at the vascular malformation based on the image of the vessel section. An average blood flow velocity parameter and a vessel cross-sectional area parameter are determined for each of the afferent and the efferent vessel. The method includes determining and providing the blood flow parameter set for the vascular malformation based on the average blood flow velocity parameters and the vessel cross-sectional area parameters.

Claims

1. A computer-implemented method for providing a blood flow parameter set for a vascular malformation, the computer-implemented method comprising: receiving time-resolved image data, wherein the time-resolved image data maps a change over time in a vessel section of an examination subject, and wherein the vessel section includes the vascular malformation; reconstructing a time-resolved image of the vessel section from the time-resolved image data; segmenting the vascular malformation in the time-resolved image of the vessel section; identifying at least one afferent vessel at the vascular malformation based on the time-resolved image of the vessel section; identifying at least one efferent vessel at the vascular malformation based on the time-resolved image of the vessel section; determining an average blood flow velocity parameter for each of the at least one afferent vessel and the at least one efferent vessel; determining a vessel cross-sectional area parameter for each of the at least one afferent vessel and the at least one efferent vessel; determining the blood flow parameter set for the vascular malformation based on the average blood flow velocity parameters and the vessel cross-sectional area parameters; and providing the blood flow parameter set.

2. The computer-implemented method of claim 1, wherein the blood flow parameter set comprises at least one first blood flow parameter that corresponds to the at least one afferent vessel, wherein the blood flow parameter set comprises at least one second blood flow parameter that corresponds to the at least one efferent vessel, wherein the computer-implemented method further comprises comparing a sum of the at least one first blood flow parameter with a sum of the at least one second blood flow parameter, and wherein the computer-implemented method is carried out repeatedly as of a predetermined discrepancy between the sums, starting with the identifying of the at least one afferent vessel.

3. The computer-implemented method of claim 1, further comprising: determining a vessel section model based on the segmented vascular malformation, the determining of the vessel section model comprising adapting a volume mesh model; determining a porosity parameter for the vascular malformation based on the vessel section model; and determining a permeability parameter for the vascular malformation based on the vessel section model, wherein determining the blood flow parameter set comprises determining a pressure ratio between the at least one afferent vessel and the at least one efferent vessel based on the porosity parameter, the permeability parameter, the average blood flow velocity parameters, and the vessel cross-sectional area parameters.

4. The computer-implemented method of claim 3, wherein determining the blood flow parameter set comprises applying a trained function to input data, wherein the input data is based on the porosity parameter, the permeability parameter, the average blood flow velocity parameters, and the vessel cross-sectional area parameters, and wherein at least one parameter of the trained function is based on a comparison between a training pressure ratio and a comparison pressure ratio.

5. The computer-implemented method of claim 4, wherein determining the blood flow parameter set further comprises determining a three-dimensional pressure distribution.

6. The computer-implemented method of claim 1, wherein the time-resolved image data maps a contrast medium bolus in the vessel section, and wherein determining the average blood flow velocity parameter is based on a change in intensity over time in the time-resolved image of the vessel section due to the contrast medium bolus.

7. The computer-implemented method of claim 3, wherein the time-resolved image data maps a contrast medium bolus in the vessel section, and wherein determining the average blood flow velocity parameter is based on a change in intensity over time in the time-resolved image of the vessel section due to the contrast medium bolus.

8. The computer-implemented method of claim 7, wherein the porosity parameter is determined based on a ratio between a volume of the vascular malformation and a volume of the contrast medium bolus within the vascular malformation.

9. The computer-implemented method of claim 7, wherein the time-resolved image of the vessel section has a number of voxels, and wherein reconstructing a time-resolved image of the vessel section comprises assigning a bolus arrival time to each of the number of voxels in which the at least one afferent vessel, the at least one efferent vessel, the vascular malformation, or any combination thereof is imaged.

10. The computer-implemented method of claim 9, wherein identifying the at least one afferent vessel, identifying the at least one efferent vessel, or a combination thereof is based on a comparison of the bolus arrival time of different voxels of the time-resolved image of the vessel section.

11. The computer-implemented method of claim 1, wherein the blood flow parameter set includes a temporal blood volume flow parameter for each of the at least one afferent vessel and the at least one efferent vessel, and wherein the temporal blood volume flow parameters are determined based on the respective average blood flow velocity parameter and the respective vessel cross-sectional area parameter.

12. A computer-implemented method for providing a trained function, the computer-implemented method comprising: receiving average training blood flow velocity parameters, training vessel cross-sectional area parameters, and a segmented training vascular malformation, the receiving comprising applying a computer-implemented method for providing a blood flow parameter set for a vascular malformation, the computer-implemented method for providing the blood flow parameter set comprising: receiving time-resolved image data, wherein the time-resolved image data maps a change over time in a vessel section of an examination subject, and wherein the vessel section includes the vascular malformation; reconstructing a time-resolved image of the vessel section from the time-resolved image data; segmenting the vascular malformation in the time-resolved image of the vessel section; identifying at least one afferent vessel at the vascular malformation based on the time-resolved image of the vessel section; identifying at least one efferent vessel at the vascular malformation based on the time-resolved image of the vessel section; determining an average blood flow velocity parameter for each of the at least one afferent vessel and the at least one efferent vessel; determining a vessel cross-sectional area parameter for each of the at least one afferent vessel and the at least one efferent vessel; determining the blood flow parameter set for the vascular malformation based on the average blood flow velocity parameters and the vessel cross-sectional area parameters; and providing the blood flow parameter set, wherein the average blood flow velocity parameters are provided as the average training blood flow velocity parameters, the vessel cross-sectional area parameters are provided as the training vessel cross-sectional area parameters, and the segmented vascular malformation is provided as the training vascular malformation; determining a training vessel section model based on the training vascular malformation, the determining of the training vessel section model comprising adapting a volume mesh model; determining a training porosity parameter for the training vascular malformation based on the training vessel section model; determining a training permeability parameter for the training vascular malformation based on the training vessel section model; determining a comparison pressure ratio between the at least one afferent vessel and the at least one efferent vessel based on the training porosity parameter, the training permeability parameter, the average training blood flow velocity parameters, and the training vessel cross-sectional area parameters; determining a training pressure ratio between the at least one afferent vessel and the at least one efferent vessel, the determining of the training pressure ratio comprising applying the trained function to input data, wherein the input data is based on the training porosity parameter, the training permeability parameter, the average training blood flow velocity parameters, and the training vessel cross-sectional area parameters; adjusting at least one parameter of the trained function based on a comparison between the training pressure ratio and the comparison pressure ratio; and providing the trained function.

13. A medical imaging device comprising: a processor configured to provide a blood flow parameter set for a vascular malformation, the provision of the blood flow parameter set comprising: receipt of a time-resolved image data, wherein the time-resolved image data maps a change over time in a vessel section of an examination subject, and wherein the vessel section includes the vascular malformation; reconstruction of a time-resolved image of the vessel section from the time-resolved image data; segmentation of the vascular malformation in the time-resolved image of the vessel section; identification of at least one afferent vessel at the vascular malformation based on the time-resolved image of the vessel section; identification of at least one efferent vessel at the vascular malformation based on the time-resolved image of the vessel section; determination of an average blood flow velocity parameter for each of the at least one afferent vessel and the at least one efferent vessel; determination of a vessel cross-sectional area parameter for each of the at least one afferent vessel and the at least one efferent vessel; determination of the blood flow parameter set for the vascular malformation based on the average blood flow velocity parameters and the vessel cross-sectional area parameters; and provision of the blood flow parameter set, wherein the medical imaging device is configured to acquire time-resolved image data, receive the time-resolved image data, provide the time-resolved image, or any combination thereof.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Exemplary embodiments of the invention are shown in the drawings and are described in more detail hereinbelow. The same reference signs are used for like features in different figures, in which:

[0083] FIG. 1 shows a schematic view of an embodiment of a computer-implemented method for providing a blood flow parameter set for a vascular malformation;

[0084] FIG. 2 shows a schematic view of one example of data flow in the computer-implemented method for providing a blood flow parameter set for a vascular malformation;

[0085] FIGS. 3 to 6 show schematic views of different embodiments of the computer-implemented method for providing a blood flow parameter set for a vascular malformation;

[0086] FIG. 7 shows a schematic view of one embodiment of a computer-implemented method for providing a trained function;

[0087] FIG. 8 shows a schematic view of one embodiment of a provider unit;

[0088] FIG. 9 shows a schematic view of one embodiment of a training unit; and

[0089] FIG. 10 shows a schematic view of one embodiment of a medical C-arm X-ray apparatus.

DETAILED DESCRIPTION

[0090] FIG. 1 schematically illustrates an embodiment of a computer-implemented method for providing a blood flow parameter set for a vascular malformation. In the embodiment shown, in a first act a), time-resolved image data BD (e.g., image data) may be received REC-BD. The image data BD maps a change over time in a vessel section VS of an examination subject 31. Further, the vessel section VS may include the vascular malformation MF. In a second act b), a time-resolved image ABB of the vessel section VS may be reconstructed PROC-ABB from the image data BD. After this, in a third act c), the vascular malformation MF may be segmented SEG-MF in the image ABB of the vessel section VS. Further, in act d1), at least one afferent vessel FV may be identified ID-FV at the vascular malformation MF based on the image ABB of the vessel section VS. Also, in a further act d2), at least one efferent vessel DV may be identified ID-DV at the vascular malformation MF based on the image ABB of the vessel section VS. After this, in act e1), an average blood flow velocity parameter may be determined DET-AV in each case for the at least one afferent vessel AV-FV and the at least one efferent vessel AV-DV. Further, in a further act e2), a vessel cross-sectional area parameter may be determined DET-VCSA in each case for the at least one afferent vessel VCSA-FV and the at least one efferent vessel VCSA-DV. After this, in act f1), the blood flow parameter set BFP for the vascular malformation MF may be determined DET-BFP based on the average blood flow velocity parameters AV-FV, VA-DV and the vessel cross-sectional area parameters VCSA-FV, VCSA-DV.

[0091] The blood flow parameter set BFP may, for example, include information concerning the volume flow rate in relation to the at least one afferent vessel and/or the at least one efferent vessel. The volume flow rate {dot over (V)} may in this case be determined, for example, as a product from the respective average blood flow velocity parameter AV-FV or AV-DV and the associated vessel cross-sectional area parameter VCSA-FV or VCSA-DV:

{dot over (V)}.sub.FV=AV-FV.Math.VCSA-FV  (1)

{dot over (V)}.sub.DV=AV-DV.Math.VCSA-DV  (2)

[0092] The blood flow parameter set BFP may further be provided PROV-BFP in act g).

[0093] In addition, the image data BD may map a contrast medium bolus in the vessel section VS. In such a case, act e1) may be based on a change in intensity over time in the image ABB of the vessel section VS due to the contrast medium bolus.

[0094] The image ABB of the vessel section VS may also have a number of voxels. The reconstruction PROC-ABB in act b) assigns a bolus arrival time to each of the voxels in which the at least one afferent vessel FV and/or the at least one efferent vessel DV and/or the vascular malformation MF is depicted. In this case, the identification of the at least one afferent vessel ID-FV in act d1) and/or the identification of the at least one efferent vessel ID-DV in act d2) may be based on a comparison of the bolus arrival time of different voxels of the image ABB of the vessel section VS.

[0095] The blood flow parameter set BFP may also include a temporal blood volume flow parameter in each case for the at least one afferent vessel FV and the at least one efferent vessel DV. In this case, the temporal blood volume flow parameter may be determined based on the respective average blood flow velocity parameter AV-FV or AV-DV and the respective vessel cross-sectional area parameter VCSA-FV or VCSA-DV.

[0096] FIG. 2 schematically illustrates the data flow of an embodiment variant of the method for providing PROV-BFP a blood flow parameter set BFP for a vascular malformation MF. The vessel section VS of the examination subject 31 is depicted in the image data BD against the tissue background TB. Further, the vessel section VS in the image ABB of the vessel section VS may be reconstructed three-dimensionally. In this case, the image ABB of the vessel section VS may include multiple three-dimensional image datasets to each of which time information is assigned. This enables the image ABB of the vessel section also to map a change over time in the vessel section VS three-dimensionally. After the segmenting SEG-MF of the vascular malformation in the image ABB of the vessel section VS, the at least one afferent vessel FV and the at least one efferent vessel DV at the vascular malformation MF may be identified ID-FV, ID-DV. Further, the vessel cross-sectional area parameters may be determined DET-VCSA for the at least one afferent vessel VCSA-FV and the at least one efferent vessel VCSA-DV. In addition, the average blood flow velocity parameters may be determined DET-AV for the at least one afferent vessel AV-FV and the at least one efferent vessel AV-DV. After this, the blood flow parameter set BFP for the vascular malformation MF may be determined DET-BFP based on the average blood flow velocity parameters AV-FV or AV-DV and the vessel cross-sectional area parameters VCSA-FV or VCSA-DV.

[0097] In the embodiment of the computer-implemented method for providing PROV-BFP a blood flow parameter set BFP for a vascular malformation MF illustrated schematically in FIG. 3, the blood flow parameter set BFP may include at least one first blood flow parameter BFP-FV corresponding to the at least one afferent vessel FV. The blood flow parameter set BFP may also include at least one second blood flow parameter BFP-DV corresponding to the at least one efferent vessel DV. In this case, the method may also include act f2), in which a sum of the at least one first blood flow parameter BFP-FV is compared COMP-BFP with a sum of the at least one second blood flow parameter BFP-DV. The comparison may include, for example, a validity condition with regard to the sum of the volume flow rate of the at least one afferent vessel and the sum of the volume flow rate of the at least one efferent vessel:

Σ{dot over (V)}.sub.FV=Σ{dot over (V)}.sub.DV  (3)

[0098] The method may in this case be executed repeatedly starting at act d1) as of a predetermined discrepancy between the sums. If the result of the comparison is that the sums lie within the predetermined discrepancy, the blood flow parameter set BFP may be provided PROV-BFP.

[0099] FIG. 4 schematically illustrates a further embodiment of the computer-implemented method for providing PROV-BFP a blood flow parameter set BFP for a vascular malformation MF. In this case, in act c2), a vessel section model VM may be determined DET-VM based on the segmented vascular malformation MF by adapting a volume mesh model. Next, a porosity parameter PP1 may be determined DET-PP1 for the vascular malformation MF based on the vessel section model VM. A permeability parameter PP2 may also be determined DET-PP2 for the vascular malformation MF based on the vessel section model VM. In addition, in act f1), a pressure ratio PR between the at least one afferent vessel FV and the at least one efferent vessel DV may be determined DET-BFP based on the porosity parameter PP1, the permeability parameter PP2, the average blood flow velocity parameters AV-FV and AV-DV, and the vessel cross-sectional area parameters VCSA-FV and VCSA-DV. Further, a three-dimensional pressure distribution may be determined in act f1).

[0100] In the embodiment of the computer-implemented method for providing PROV-BFP a blood flow parameter set BFP for a vascular malformation MF schematically illustrated in FIG. 5, act f1) may be performed by applying a trained function TF-PR to input data. The input data may be based here on the porosity parameter PP1, the permeability parameter PP2, the average blood flow velocity parameters AV-FV or AV-DV, and the vessel cross-sectional area parameters VCSA-FV or VCSA-DV.

[0101] FIG. 6 schematically illustrates a further embodiment of the computer-implemented method for providing PROV-BFP a blood flow parameter set BFP for a vascular malformation MF. In this case, in act e3), a spatial volume VOL-MF of the vascular malformation MF may, for example, be determined DET-VOL-MF based on the vessel section model VM. A spatial volume VOL-CM of the contrast medium bolus within the vascular malformation MF may also be determined DET-VOL-CM in act e3). The porosity parameter PP1 may then be determined DET-PP1 based on a ratio between the volume of the vascular malformation VOL-MF and the volume of the contrast medium bolus VOL-CM within the vascular malformation MF.

[0102] Darcy's law may be applied for the flow Q of a fluid in a porous medium (e.g., the vascular malformation MF). This is derivable by a homogenization of the Navier-Stokes equations:

[00001]$\begin{array}{cc}\stackrel{.}{V}=\frac{\mathrm{PP}.Math..Math.2.Math.\mathrm{CSA}.Math.\left(p_1-p_2\right)}{\mu .Math.L},& \left(4\right)\end{array}$

where PP2 denotes the permeability parameter of the vascular malformation, μ denotes the dynamic viscosity of the fluid, CSA denotes the vessel cross-sectional area (e.g., at the cross-sectional areas with the at least one afferent and efferent vessel FV and DV respectively), and L denotes a spatial distance between two spatial points, with the pressure p.sub.1 and p.sub.2 prevailing respectively at the two spatial points.

[0103] From Equation (4), it may be derived that:

[00002]$\begin{array}{cc}q=\frac{\stackrel{.}{V}}{\mathrm{CSA}}=\frac{\mathrm{PP}.Math..Math.2}{\mu }.Math.\text{∇}.Math.p,& \left(5\right)\end{array}$

where ∇p denotes the pressure gradient between the cross-sectional areas of the vascular malformation MF with the at least one afferent and efferent vessel FV and DV, respectively (e.g., along the spatial distance L), and q denotes the volume flow rate normalized to the vessel cross-sectional area CSA.

[0104] It follows from this that the pressure gradient ∇p is indirectly proportional to the permeability parameter PP2 of the vessel:

[00003]$\begin{array}{cc}\text{∇}.Math.p\propto \frac{1}{\mathrm{PP}.Math..Math.2}.& \left(6\right)\end{array}$

[0105] The permeability parameter PP2 may be predefinable at the same time. Further, the porosity parameter PP1 for the vascular malformation MF may be determined as:

[00004]$\begin{array}{cc}\mathrm{PP}.Math..Math.1=\frac{{\mathrm{VOL}}_V}{\mathrm{VOL}.Math.\text{-}.Math.\mathrm{MF}},& \left(7\right)\end{array}$

[0106] where VOL.sub.V denotes the spatial volume of the vascular malformation MF that may not be filled by a fluid, where:

VOL.sub.V=VOL-MF−VOL-CM  (8).

[0107] Further, an average velocity ν of the fluid may be determined as:

[00005]$\begin{array}{cc}v=\frac{q}{\mathrm{PP}.Math..Math.1}.& \left(9\right)\end{array}$

[0108] Darcy's law may be applied, for example, for a laminar flow that often occurs in hemodynamics. Alternatively, Equation (4) may be supplemented by an inertia term (e.g., a Forchheimer term).

[0109] FIG. 7 schematically illustrates an embodiment of the computer-implemented method for providing PROV-TF-PR a trained function TF-PR. In this case, average training blood flow velocity parameters TAV-FV and TAV-DV and training vessel cross-sectional area parameters TVCSA-FV and TVCSA-DV may be received REC-TAV-TVCSA by applying PT1 an embodiment of the computer-implemented method for providing a blood flow parameter set PROV-BFP for a vascular malformation MF. At the same time, the average blood flow velocity parameters AV-FV and AV-DV may be provided as the training blood flow velocity parameters TAV-FV and TAV-DV. Further, the vessel cross-sectional area parameters VCSA-FV and VCSA-DV may be provided as the training vessel cross-sectional area parameters TVCSA-FV and TVCSA-DV. Also, a training vascular malformation TMF may be received REC-TMF. The segmented vascular malformation MF is provided as the training vascular malformation TMF. A training vessel section model TVM may be determined DET-VM based on, for example, the training vascular malformation TMF by adapting a volume mesh model. A training porosity parameter TPP1 may also be determined DET-PP1 for the training vascular malformation TMF based on the training vessel section model TVM. In addition, a training permeability parameter TPP2 may be determined DET-PP2 for the training vascular malformation TMF based on the training vessel section model TVM. After this, a comparison pressure ratio CPR between the at least one afferent vessel FV and the at least one efferent vessel DV may be determined DET-BFP based on the training porosity parameter TPP1, the training permeability parameter TPP2, the average blood flow velocity parameters TAV-FV and TAV-DV, and the training vessel cross-sectional area parameters TVCSA-FV and TVCSA-DV. In a further act, a training pressure ratio TPR between the at least one afferent vessel FV and the at least one efferent vessel DV may be determined by applying the trained function TF-PR to input data. In the process, the input data may be based on the training porosity parameter TPP1, the training permeability parameter TPP2, the average training blood flow velocity parameters TAV-FV and TAV-DV, and the training vessel cross-sectional area parameters TVCSA-FV and TVCSA-DV. Next, at least one parameter of the trained function TF-PR may be adjusted ADJ-TF-PR based on a comparison between the training pressure ratio TPR and the comparison pressure ratio CPR. The trained function TF-PR may be provided PROV-TF-PR in a further act.

[0110] FIG. 8 schematically illustrates one embodiment of a provider unit PRVS including an interface IF, a computing unit CU, and a memory unit MU. The provider unit PRVS may be embodied to carry out a computer-implemented method of one or more of the present embodiments for providing PROV-BFP a blood flow parameter set BFP for a vascular malformation MF and corresponding aspects, in that the interface IF and the computing unit CU are embodied to perform the corresponding method acts. The interface IF may be embodied in this case for receiving REC-BD the time-resolved image data BD. Further, the computing unit CU may be embodied to reconstruct PROC-ABB the time-resolved image ABB of the vessel section VS from the image data BD. The computing unit CU may be further embodied to segment SEG-MF the vascular malformation MF in the image ABB of the vessel section VS. The computing unit CU may also be embodied to identify ID-FV at least one afferent vessel FV at the vascular malformation MF based on the image ABB of the vessel section VS. The computing unit CU may further be embodied to identify ID-DV at least one efferent vessel DV at the vascular malformation MF based on the image ABB of the vessel section VS. The computing unit CU may further be embodied to determine DET-AV an average blood flow velocity parameter in each case for the at least one afferent vessel AV-FV and the at least one efferent vessel AV-FV. Further, the computing unit CU may be embodied for determining DET-VCSA a vessel cross-sectional area parameter in each case for the at least one afferent vessel VCSA-FV and the at least one efferent vessel VCSA-DV. The computing unit CU may also be embodied for determining DET-BFP the blood flow parameter set BFP for the vascular malformation MF based on the average blood flow velocity parameters AV-FV and AV-DV and the vessel cross-sectional area parameters VCSA-FV and VCSA-DV. In addition, the interface IF may be embodied for providing PROV-BFP the blood flow parameter set BFP for the vascular malformation MF.

[0111] FIG. 9 schematically illustrates one embodiment of a training unit TRS including a training interface TIF, a training computing unit TCU, and a training memory unit TMU. The training unit TRS may be embodied to carry out an embodiment of a computer-implemented method for providing a trained function PROV-TF-PR and corresponding aspects, in that the training interface TIF and the training computing unit TCU are embodied to perform the corresponding method acts.

[0112] In this case, the training interface TIF may be embodied for receiving the average training blood flow velocity parameters TAV-FV and TAV-DV, the training vessel cross-sectional area parameters TVCSA-FV and TVCSA-DV, and the training vascular malformation TMF by applying a variant of the computer-implemented method for providing PROV-BFP a blood flow parameter set BFP for a vascular malformation MF. In this case, the average blood flow velocity parameters AV-FV and AV-DV may be provided as the average training blood flow velocity parameters TAV-FV and TAV-DV, the vessel cross-sectional area parameters VCSA-FV and VCSA-DV may be provided as the training vessel cross-sectional area parameters TVCSA-FV and TVCSA-DV, and the segmented vascular malformation MF may be provided as the training vascular malformation TMF. Further, the training computing unit TCU may be embodied for determining DET-VM a training vessel section model TVM based on the training vascular malformation TMF by adapting a volume mesh model. Further, the training computing unit TCU may be embodied for determining DET-PP1 a training porosity parameter TPP1 for the training vascular malformation TMF based on the training vessel section model TVM. Further, the training computing unit TCU may be embodied for determining DET-PP2 a training permeability parameter TPP2 for the training vascular malformation TMF based on the training vessel section model TVM. Further, the training computing unit TCU may be embodied for determining DET-BFP a comparison pressure ratio CPR between the at least one afferent vessel FV and the at least one efferent vessel DV based on the training porosity parameter TPP1, the training permeability parameter TPP2, the average training blood flow velocity parameters TAV-FV and TAV-DV, and the training vessel cross-sectional area parameters TVCSA-FV and TVCSA-DV. Further, the training computing unit TCU may be embodied for determining a training pressure ratio TPR between the at least one afferent vessel FV and the at least one efferent vessel DV by applying the trained function TF-PR to input data. The input data is based on the training porosity parameter TPP1, the training permeability parameter TPP2, the average training blood flow velocity parameters TAV-FV and TAV-DV, and the training vessel cross-sectional area parameters TVCSA-FV and TVCSA-DV. Further, the training computing unit TCU may be embodied for adjusting ADJ-TF-PR at least one parameter of the trained function TF-PR based on a comparison between the training pressure ratio TPR and the comparison pressure ratio CPR. Further, the training interface TCU may be embodied for providing PROV-TF-PR the trained function TF-PR.

[0113] The provider unit PRVS and/or the training unit TRS may, for example, be a computer, a microcontroller, or an integrated circuit. Alternatively, the provider unit PRVS and/or the training unit TRS may be a real or virtual network of interconnected computers (e.g., a technical term for a real network is “cluster”, a technical term for a virtual network is “cloud”). The provider unit PRVS and/or the training unit TRS may also be embodied as a virtual system that is implemented on a real computer or a real or virtual network of interconnected computers (e.g., virtualization).

[0114] An interface IF and/or a training interface TIF may be a hardware or software interface (e.g., PCI bus, USB or Firewire). A computing unit CU and/or a training computing unit TCU may have hardware elements or software elements (e.g., a microprocessor or a field programmable gate array (FPGA)). A memory unit MU and/or a training memory unit TMU may be realized as a volatile working memory known as RAM (random access memory) or as a nonvolatile mass storage device (e.g., hard disk, USB stick, SD card, solid state disk (SSD)).

[0115] The interface IF and/or the training interface TIF may, for example, include a number of sub-interfaces that perform different acts of the respective methods. In other words, the interface IF and/or the training interface TIF may also be understood as a plurality of interfaces IF or as a plurality of training interfaces TIF. The computing unit CU and/or the training computing unit TCU may, for example, include a number of sub-computing units that perform different acts of the respective methods. In other words, the computing unit CU and/or the training computing unit TCU may also be understood as a plurality of computing units CU or as a plurality of training computing units TCU.

[0116] FIG. 10 schematically illustrates one embodiment of a medical C-arm X-ray apparatus 37, by way of example, for an embodiment of a medical imaging device. In this case, the medical C-arm X-ray apparatus 37 may include, for example, an embodiment of a provider unit PRVS for providing PROF-BFP a blood flow parameter set BFP for a vascular malformation MF. In this case, the medical imaging device 37 (e.g., the provider unit PRVS) is embodied for carrying out an embodiment of a computer-implemented method for providing PROV-BFP a blood flow parameter set BFP for a vascular malformation MF.

[0117] In this case, the medical C-arm X-ray apparatus 37 also includes a detector unit 34 and an X-ray source 33. In order to acquire the time-resolved image data BD, the arm 38 of the C-arm X-ray apparatus 37 may be mounted so as to be movable about one or more axes. The medical C-arm X-ray apparatus 37 may also include a movement device 39 that enables the C-arm X-ray apparatus 37 to move in space.

[0118] In order to acquire the time-resolved image data BD of the vessel section VS of the examination subject 31 arranged on a patient support and positioning device 32, the provider unit PRVS may send a signal 24 to the X-ray source 33. The X-ray source 33 may thereupon emit an X-ray beam (e.g., a cone beam and/or fan beam and/or parallel beam). When the X-ray beam, following an interaction with the vessel section VS of the examination subject 31 that is to be imaged, is incident on a surface of the detector unit 34, the detector unit 34 may send a signal 21 to the provider unit PRVS. The provider unit PRVS may receive REC-BD the time-resolved image data BD, for example, with the aid of the signal 21.

[0119] In addition, the medical C-arm X-ray apparatus 37 may include an input unit 42 (e.g., a keyboard) and/or a visualization unit 41 (e.g., a monitor and/or display). The input unit 42 may be integrated in the visualization unit 41 (e.g., in the case of a capacitive input display). This enables the medical C-arm X-ray apparatus 37 (e.g., the proposed computer-implemented method for providing PROV-BFP a blood flow parameter set BFP for a vascular malformation MF) to be controlled by an input by a member of the operating staff at the input unit 42. For this purpose, the input unit 42 may, for example, send a signal 26 to the provider unit PRVS.

[0120] The visualization unit 41 may also be embodied to display information and/or graphical representations of information of the medical imaging device 37 and/or the provider unit PRVS and/or further components. For this purpose, the provider unit PRVS may, for example, send a signal 25 to the visualization unit 41. For example, the visualization unit 41 may be embodied for displaying a graphical representation of the time-resolved image data BD and/or the image ABB of the vessel section VS and/or the vessel section model VM and/or the segmented vascular malformation MF and/or the three-dimensional pressure distribution and/or the blood flow parameter set. In one embodiment, a graphical (e.g., color-coded) representation of the image ABB of the vessel section VS and/or of the vessel section model VM and/or of the three-dimensional pressure distribution may be displayed on the visualization unit 41. The graphical representation of the image ABB of the vessel section VS and/or of the vessel section model VM and/or of the three-dimensional pressure distribution may also include an overlay (e.g., a weighted overlay).

[0121] The schematic views contained in the described figures do not depict a scale or proportions of any kind.

[0122] The methods described in detail in the foregoing, as well as the illustrated devices, are exemplary embodiments that may be modified in the most diverse ways by the person skilled in the art without departing from the scope of the invention. Further, the use of the indefinite articles “a” or “an” does not exclude the possibility that the features in question may also be present more than once. Similarly, the terms “unit” and “element” do not rule out the possibility that the components in question consist of a plurality of cooperating subcomponents, which, if necessary, may also be distributed in space.

[0123] The elements and features recited in the appended claims may be combined in different ways to produce new claims that likewise fall within the scope of the present invention. Thus, whereas the dependent claims appended below depend from only a single independent or dependent claim, it is to be understood that these dependent claims may, alternatively, be made to depend in the alternative from any preceding or following claim, whether independent or dependent. Such new combinations are to be understood as forming a part of the present specification.

[0124] While the present invention has been described above by reference to various embodiments, it should be understood that many changes and modifications can be made to the described embodiments. It is therefore intended that the foregoing description be regarded as illustrative rather than limiting, and that it be understood that all equivalents and/or combinations of embodiments are intended to be included in this description.