ESTIMATING FLOW TO VESSEL BIFURCATIONS FOR SIMULATED HEMODYNAMICS

Abstract

An apparatus for assessing a patient's vasculature and a corresponding method are provided, in which the bifurcations in a vessel of interest are identified on the basis of a local change in at least one geometric parameter value of the vessel of interest and the fluid dynamics inside the vessel of interest are adjusted to take account for said bifurcations.

Claims

1. An apparatus for assessing a vasculature, the apparatus comprising: at least one processor in communication with memory, the at least one processor configured to: receive at least one diagnostic image of the vasculature; generate, based on the at least one diagnostic image, a physiological model comprising a geometric model of a vessel of interest in the vasculature; extract, based on the geometric model, a plurality of geometric parameter values for a geometric parameter of the vessel of interest at a plurality of positions along a longitudinal axis of the vessel of interest; determine, from the plurality of geometric parameter values at the plurality of positions, a local change of at least one geometric parameter value at at least one candidate position; and predict, at the at least one candidate position, a presence of at least one vessel branch of the vessel of interest.

2. The apparatus according to claim 1, wherein the at least one diagnostic image is obtained using X-ray angiography.

3. The apparatus according to claim 1, wherein: the physiological model further comprises a lumped parameter fluid dynamics model; and the at least one processor is further configured to adapt the lumped parameter fluid dynamics model based on predicting the at least one vessel branch at the at least one candidate position.

4. The apparatus according to claim 1, wherein the at least one processor is further configured to: generate the physiological model by segmenting the vessel of interest into one or more segments; determine, for each segment, at least one segmented geometric parameter value; apply a regression model based on the at least one segmented geometric parameter value to calculate, for each segment, an averaged geometric parameter value; and predict the at least one vessel branch by predicting at least one hemodynamic parameter at the at least one candidate position based on the averaged geometric parameter value of each segment.

5. The apparatus according to claim 4, wherein the at least one processor is further configured to predict the at least one hemodynamic parameter based on predicting of fluid outflow rate.

6. The apparatus according to claim 1, wherein the at least one processor is further configured to: define a region of interest in the at least one diagnostic image based on the at least one candidate position; and output an indication of the region of interest.

7. The apparatus according to claim 6, wherein the at least one processor is further configured to: output the indication of the region of interest; and adapt the physiological model using the indication of the region of interest.

8. The apparatus according to claim 6, further comprising: a display configured to: receive the indication of the region of interest from the at least one processor; generate a first graphical representation of the at least one diagnostic image and a second graphical representation of the indication of the region of interest in the diagnostic image data; and jointly display the first graphical representation and the second graphical representation.

9. The apparatus according to claim 1, wherein the at least one processor is further configured to: receive intravascular measurement data; and predict, based on the physiological model and the intravascular measurement data, one or more hemodynamic index values at the plurality of positions along the longitudinal axis of the vessel of interest.

10. The apparatus according to claim 9, wherein the intravascular measurement data comprises at least one pressure gradient acquired in-situ for the vessel of interest.

11. The apparatus according to claim 9, wherein the one or more hemodynamic index values predicted at the plurality of positions along the longitudinal axis of the vessel of interest comprises at least one of a volumetric flow rate and/or a blood flow velocity.

12. A method for assessing a vasculature, the method comprising: receiving at least one diagnostic image of the vasculature; generating, based on the at least one diagnostic image, a physiological model comprising a geometric model of a vessel of interest in the vasculature; extracting, based on the geometric model, a plurality of geometric parameter values for a geometric parameter of the vessel of interest at a plurality of positions along a longitudinal axis of the vessel of interest; determining, from the plurality of geometric parameter values at the plurality of positions, a local change of at least one geometric parameter value at at least one candidate position; and predicting, at the at least one candidate position, a presence of at least one vessel branch of the vessel of interest.

13. The method according to claim 12, further comprising: segmenting the vessel of interest into one or more segments; determining, for each segment, at least one segmented geometric parameter value; applying a regression model on the at least one segmented geometric parameter value to calculate, for each segment, an averaged geometric parameter value; and predicting the at least one vessel branch by predicting at least one hemodynamic parameter at the at least one candidate position based on the averaged geometric parameter value of each segment.

14. The method according to claim 13, wherein the at least one hemodynamic parameter is predicted based on predicting fluid outflow rate.

15. The method according to claim 12, wherein: the physiological model further comprises a lumped parameter fluid dynamics model; and the method further comprises adapting the lumped parameter fluid dynamics model based on predicting the at least one vessel branch at the at least one candidate position.

16. A non-transitory computer-readable storage medium having stored a computer program comprising instructions, which, when executed by a processor, cause the processor to: receive at least one diagnostic image of the vasculature; generate, based on the at least one diagnostic image, a physiological model comprising a geometric model of a vessel of interest in the vasculature; extract, based on the geometric model, a plurality of geometric parameter values for a geometric parameter of the vessel of interest at a plurality of positions along a longitudinal axis of the vessel of interest; determine, from the plurality of geometric parameter values at the plurality of positions, a local change of at least one geometric parameter value at at least one candidate position; and predict, at the at least one candidate position, a presence of at least one vessel branch of the vessel of interest.

17. The non-transitory computer-readable storage medium according to claim 16, wherein the instructions, when executed by the processor, further cause the processor to: segment the vessel of interest into one or more segments; determine, for each segment, at least one segmented geometric parameter value; apply a regression model on the at least one segmented geometric parameter value to calculate, for each segment, an averaged geometric parameter value; and predict the at least one vessel branch by predicting at least one hemodynamic parameter at the at least one candidate position based on the averaged geometric parameter value of each segment.

18. The non-transitory computer-readable storage medium according to claim 17, wherein the instructions, when executed by the processor, further cause the processor to predict the at least one hemodynamic parameter based on predicting fluid outflow rate.

19. The non-transitory computer-readable storage medium according to claim 16, wherein: the physiological model further comprises a lumped parameter fluid dynamics model; and the instructions, when executed by the processor, further cause the processor to adapt the lumped parameter fluid dynamics model based on predicting the at least one vessel branch at the at least one candidate position.

20. The non-transitory computer-readable storage medium according to claim 16, wherein the instructions, when executed by the processor, further cause the processor to: receive intravascular measurement data; and predict, based on the physiological model and the intravascular measurement data, one or more hemodynamic index values at the plurality of positions along the longitudinal axis of the vessel of interest.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0102] In the following drawings:

[0103] FIG. 1 schematically illustrates an apparatus for assessing a patient's vasculature according to an embodiment.

[0104] FIG. 2A represents a plot of the segmented and averaged vessel size as well as the calculated outlet resistance as a function of the vessel length according to an embodiment.

[0105] FIG. 2B schematically illustrates the integration of the fluid outflow into a lumped parameter fluid dynamics model according to an embodiment.

[0106] FIG. 3 represents a flow chart for a method for assessing a patient's vasculature according to an embodiment.

DETAILED DESCRIPTION OF EMBODIMENTS

[0107] The illustration in the drawings is schematically. In different drawings, similar or identical elements are provided with the same reference numerals.

[0108] FIG. 1 represents schematically an exemplary embodiment of an apparatus 2 for assessing a patient's vasculature. The apparatus 2 comprises an input unit 100, a modeling unit 200, an extraction unit 300, an evaluation unit 400 and a display unit 500.

[0109] An X-ray system 1, which may for example be a C-arm system, is used to acquire X-ray angiography projection data comprising one or more diagnostic images. Thus, in the exemplary embodiment according to FIG. 1, the at least one diagnostic image 10 is a single two-dimensional (2D) X-ray angiography image.

[0110] Input unit 100 of apparatus 2 is configured to receive the single X-ray angiography image 10 from X-ray system 1. Further, input unit 100 is configured to receive, from an intravascular measurement modality, intravascular measurement data 20. In the exemplary embodiment according to FIG. 1, intravascular measurement data 20 has been obtained by introducing a pressure wire into the vessel of interest in the patient's vasculature and measuring a respective pressure gradient Δp inside the vessel of interest.

[0111] The input unit 100 provides the X-ray angiography image 10 to modeling unit 200. Modeling unit 200 is configured to receive the X-ray angiography image 10 and to perform a vessel segmentation of the vessel of interest in the vasculature imaged in the X-ray angiography image 10 to generate a physiological model comprising a geometric model of the vessel of interest.

[0112] In the exemplary embodiment according to FIG. 1, the generating of the physiological model comprising the geometric model on the basis of the X-ray angiography image 10 particularly includes a modeling of the vessel diameter of the vessel of interest for a plurality of positions along a (simulated) longitudinal axis of said vessel, i.e. along the vessel length. That is, for each segment of the segmented vessel, at least one vessel diameter is derived. Modeling unit 200 then provides the physiological model including at least the at least one vessel diameter to extraction unit 300.

[0113] Extraction unit 300 is configured to use the geometric model included in the physiological model to extract a plurality of geometric parameter values for the vessel of interest. In the exemplary embodiment according to FIG. 1, extraction unit 300 extracts a plurality of values for the vessel diameter of the vessel of interest at the plurality of positions. Since the vessel of interest has been segmented, this encompasses an extraction of a plurality of values for the vessel diameter for each of the segments of the vessel. Accordingly, in the exemplary embodiment, extraction unit 300 extracts multiple segmented vessel diameter values.

[0114] Extraction unit 300 then provides the extracted values along with the respective position for which they have been extracted to evaluation unit 400.

[0115] Evaluation unit 400 receives the X-ray angiography image 10 and the extracted values along with their respective positions. Evaluation unit 400 then calculates, for each of the position, a local average vessel diameter from the extracted values. In this exemplary embodiment, evaluation unit 400 applies a combination of a linear and an isotonic regression to the extracted values to calculate the local average vessel diameter D.sub.i for each position i. Hereby, extraction unit 400 optionally uses a monotonic decrease as an additional condition for the calculation.

[0116] Evaluation unit 400 then regards the thus calculated local average vessel diameters D.sub.i as a function of their respective positions i and identifies the candidate positions i, for which the average vessel diameter D.sub.i shows a localized decrease. Evaluation unit 400 then predicts, based on the localized decrease at least one hemodynamic parameter at each of the candidate positions i. In the particular embodiment according to FIG. 1, evaluation unit particularly predicts a fluid outflow rate for each of the candidate positions i.

[0117] In the exemplary embodiment of FIG. 1, evaluation unit 400 then regards the predicted fluid outflow rate at each of the candidate positions i and determines, for the subset of candidate positions i for which the predicted fluid outflow rate is high, i.e. is above a pre-defined threshold value, that a minor vessel branch may be branching from the vessel of interest at that particular candidate position i.

[0118] To that end, evaluation unit 400 uses the predicted fluid outflow rate to determine, for the X-ray angiography image 10, a region of interest in the proximity of the position in the X-ray angiography image 10 that corresponds to the particular candidate position i in the vessel of interest. That is, evaluation unit 400 determines a region of interest in X-ray angiography image 10 that may potentially include a vessel branch.

[0119] According to the example of FIG. 1, evaluation unit 400 then outputs an indicator for the at least one region of interest in the X-ray angiography image 10 to modeling unit 200 and display unit 500. Further, evaluation unit 400 provides the information about the predicted fluid outflow rate at each of the candidate positions i to modeling unit 200.

[0120] Modeling unit 200 then adapts the physiological model to include the potential vessel branches at candidate positions i, for which the predicted fluid outflow rate has been high. More particularly, modeling unit 200 uses the X-ray angiography image and the indication about the at least one region of interest to identify the vessel branches in the X-ray angiography image, by determining a corresponding vessel branch in the respective region of interest. On the basis of this identification, modeling unit 200 then adapts the geometric model of the vessel of interest to include these vessel branches.

[0121] Hereby, modeling unit 200 takes into account the magnitude of the predicted fluid outflow rate at each of the candidate positions i to more accurately estimate the size of each of the vessel branches. That is, modeling unit 200 estimates that higher outflow rates indicate a larger vessel branch and lower outflow rates indicate a smaller vessel branch.

[0122] Further, modeling unit 200 adapts the fluid dynamics through the vessel of interest such as to include the bifurcation at each of the candidate positions i, i.e. to take into account the fluid outflow from the vessel of interest at candidate positions i.

[0123] In the exemplary embodiment according to FIG. 1, modeling unit 200 subsequently provides the adapted physiological model to evaluation unit 400. Evaluation unit 400 then uses the adapted physiological model and the intravascular measurement data received from input unit 100 to predict a plurality of hemodynamic index values for the plurality of positions in the vessel of interest.

[0124] In the particular embodiment according to FIG. 1, the Coronary Flow Reserve (CFR=Q.sub.hyperemia/Q.sub.rest) may be estimated for the plurality of positions along the length of the vessel without the necessity to introduce a further intravascular measurement device capable of measuring blood flow into the vessel. Optionally, evaluation unit 400 provides the information about the CFR values along the longitudinal axis of the vessel of interest to display unit 500.

[0125] Display unit 500 receives X-ray angiography image 10 from input unit 100. Further, display unit 500 receives the indication for the at least one region of interest in the X-ray angiography image 10 and, optionally, information about the CFR values derived for the plurality of positions along the longitudinal axis of the vessel of interest.

[0126] Display unit 500 then generates a first graphical representation of the X-ray angiography image 10. Further, display unit 500 generates a second graphical representation of the indication of the region of interest. Optionally, display unit may also generate a third graphical representation of the CFR values. Display unit 500 then jointly displays the first graphical representation and the second graphical representation, optionally along with the third graphical representation.

[0127] In the exemplary embodiment according to FIG. 1, display unit 500 particularly generates a respective circumferential line as a graphical representation for each region of interest and inserts the respective circumferential line into the graphical representation of the X-ray angiography image 10 at the respective position of the region of interest in the image. That is the circumferential line represents the border of the respective region of interest.

[0128] Optionally, the display unit 500 may further generate a graphical representation of a plurality of CFR values along the length of the vessel of interest. Hereby, the graphical representation may particularly include the value of the CFR value and a respective arrow indicating the position for which this value has been predicted.

[0129] The display unit 500 may then display the graphical representations. This allows a user, in particular a physician, to gain a more thorough understanding of the patient's vasculature while minimizing the amount of invasive procedures needed for assessment.

[0130] In that respect, FIG. 2A schematically illustrates a plot 30 of the extracted vessel diameter and a plot 31 of the averaged vessel diameter D.sub.i, respectively (D in arbitrary units) as a function of the length of the vessel of interest (L in mm). In accordance with the exemplary embodiment according to FIG. 2A, the information that may be derived from the plots 30 and 31 may be used to adapt the physiological model used for predicting the hemodynamic index values.

[0131] In the embodiment according to FIG. 2A, it is assumed that the physiological model comprises a lumped parameter fluid dynamics model to simulate the fluid dynamics through the patients vasculature. FIG. 2B shows an exemplary embodiment of such a lumped parameter fluid dynamics model in which a segment of the vessel of interest is modeled by resistors R.sub.1, R.sub.2, R.sub.3 and R.sub.4. The vessel branches, i.e. the locations of fluid outflow from the vessel of interest, are modelled by resistors R.sub.O1 to R.sub.O4. Further, the resistor R.sub.O is used to define an outlet boundary condition at the end of the segment of the vessel of interest. In order to adapt the lumped parameter fluid dynamics model to correctly model the fluid outflow from the vessel of interest, the outlet resistance of the resistors R.sub.O1 to R.sub.O4 has to be determined. Hereby, the outflow rate for the vessel branches may particularly be determined using a regression model on the extracted vessel diameter to determine an average vessel diameter at the positions of the vessel segment, along with Murray's law to define the relation between the flow rate and the (average) vessel diameter.

[0132] More particularly, by applying the regression model on the extracted vessel diameter, the averaged vessel diameter D.sub.i for each candidate position i may be determined. Next, the local outflow rate between two candidate positions i may be determined as the difference between the volumetric flow rate Q.sub.i at candidate position i and Q.sub.i+1 at the follow-up candidate position i+1:

ΔQ(i,i+1)=k*(D.sub.i.sup.3−D.sub.i+1.sup.3).

[0133] As indicated herein above, the magnitude given by this local outflow rate may be used by the modeling unit 200 to determine the size of the vessel branches.

[0134] Further, the local outflow rate may be used to approximate the outlet resistance of resistors R.sub.Oi according to

R.sub.Oi=C/ΔQ(i,i+1)=C/k*(D.sub.i.sup.3−D.sub.i+1.sup.3),

[0135] wherein C is a corresponding boundary condition describing the inlet pressure of a healthy patient.

[0136] In that regard, FIG. 2A shows a plot 32 of the outlet resistances determined for a plurality of positions along the length of the vessel. As may be appreciated from plot 31 in FIG. 2A, the average vessel diameter D.sub.i decreases significantly at position 31′. Accordingly, the outflow rate is rather high and the corresponding outlet resistance at position 32′ is low. Since a low outlet resistance corresponds to a high outflow rate, a low outlet resistance may typically be used to represent a large vessel branch.

[0137] Thus, the lumped parameter fluid dynamics model may be adapted by selecting the resistances of resistors R.sub.Oi in accordance with the calculated outflow rates as described above. Subsequently, the thus adapted physiological model comprising the lumped parameter fluid dynamics model may be used to determine a plurality of hemodynamic index values.

[0138] To that end, the physiological model comprising the lumped parameter fluid dynamics model receives intravascular measurement data as a further input. In the exemplary embodiment described herein, the intravascular measurement data comprises a measurement of the pressure gradient Δp=p.sub.o−p.sub.a obtained in-situ from the vessel of interest. Using the adapted physiological model and the invasively obtained pressure gradient, the flow rate through the vessel of interest may be calculated using

Δp=Σ.sub.iQ.sub.iR.sub.i+Q.sub.i.sup.2V.sub.i,

[0139] where Δp is the pressure gradient, Q.sub.i is the flow rate at candidate position i, R.sub.i is the resistance at candidate position i and V.sub.i is the volume at that position. Hereby, Q.sub.i and p.sub.i at candidate position i are defined as

Q.sub.i=Q.sub.i−1−p.sub.i/R.sub.Oi and p.sub.i=p.sub.0−Σ.sub.k<iQ.sub.kR.sub.kQ.sub.k.sup.2V.sub.k.

[0140] Using the above relations the value for a flow-related hemodynamic index, such as the CFR, may be calculated from intravascular pressure-based measurements. This avoids the necessity of introducing a further catheter for flow-based measurements, thereby greatly improving the comfort of the patient.

[0141] FIG. 3 schematically represents a flow chart for a method for assessing a patient's vasculature according to an embodiment. In step S101, input unit 100 receives a single 2D X-ray angiography image 10 from X-ray system 1 and intravascular measurement data 20 from an intravascular measurement modality. In step S102, input unit 100 provides the X-ray angiography image 10 to modeling unit 200 and display unit 500. Further, input unit 100 provides the intravascular measurement data to evaluation unit 400.

[0142] In step S201, modeling unit 200 receives the X-ray angiography image 10. In step S202, modeling unit 200 segments the imaged vessel of interest and generates, on the basis of this segmentation, a physiological model comprising a geometric model of the vessel of interest. Modeling unit 200 then provides the physiological model to extraction unit 300.

[0143] In step S301, extraction unit 300 receives the physiological model and extracts, on the basis of the geometric model in said physiological model, a plurality of geometric parameter values for the vessel of interest. In the exemplary embodiment according to FIG. 3, the extraction unit 300 extracts a plurality of segmented vessel diameter values as the geometric parameter values and provides the extracted segmented values along with the respective position for which they have been extracted to evaluation unit 400.

[0144] In step, S401, evaluation unit 400 receives the X-ray angiography image 10 and the extracted values along with their respective positions from extraction unit 300. Further, evaluation unit 400 receives the intravascular measurement data from input unit 100.

[0145] In step S402, evaluation unit 400 calculates, for each of the position, a local average vessel diameter from the extracted values. According to the example of FIG. 3, this calculation is particularly performed by applying a combination of a linear and an isotonic regression to the segmented diameter values to calculate the local average vessel diameter D.sub.i for each candidate position i. Optionally, extraction unit 400 may use the additional condition of monotonic decrease during the calculation.

[0146] In step S403, evaluation unit 400 then identifies the candidate positions i, for which the average vessel diameter D.sub.i shows a localized decrease as described herein above and predicts, based on the localized decrease at least one hemodynamic parameter at each of the candidate positions i. In the exemplary embodiment of FIG. 3, this hemodynamic parameter may particularly comprise the outflow rate for each of the candidate positions i.

[0147] In step S404, evaluation unit 400 then determines, on the basis of the predicted outflow rate, the subset of candidate positions i for which the predicted outflow rate is high. Based on this, the evaluation unit predicts that at the candidate position i, a vessel branch of the vessel of interest may be found and uses this prediction to define, for the X-ray angiography image 10, a region of interest around the position in the image that corresponds to the particular candidate position i in the vessel of interest.

[0148] In step S405, outputs an indicator for the at least one region of interest in the X-ray angiography image 10 to modeling unit 200 and display unit 500. Further, evaluation unit 400 provides the information about the predicted outflow rate at each of the candidate positions i to modeling unit 200.

[0149] In step S203, modeling unit 200 adapts the physiological model to include the vessel branches at candidate positions i both, in terms of geometry and fluid dynamics. Hereby, modeling unit 200 uses the X-ray angiography image and the indication about the at least one region of interest to identify the vessel branches in the X-ray angiography image, and adapts the physiological model to include these vessel branches. Hereby, the modeling unit 200 assumes that the size of the vessel branch is larger when the outflow rate determined is higher and smaller, when the respective outflow rate is lower. Subsequently, in step S204, the modeling unit 200 provides the adapted physiological model back to evaluation unit 400.

[0150] Evaluation unit 400 then uses, in step S406 the adapted physiological model and the intravascular measurement data to predict a plurality of hemodynamic index values for the plurality of positions in the vessel of interest. In the exemplary embodiment according to FIG. 3, evaluation unit 400 particularly determines a plurality of CFR values for a respective plurality of positions along the vessel of interest and provides the information about the CFR values along the longitudinal axis of the vessel of interest to display unit 500.

[0151] In step S501, display unit 500 receives X-ray angiography image 10 from input unit 100. Further, display unit 500 receives the indication for the at least one region of interest in the X-ray angiography image 10. In the exemplary embodiment according to FIG. 3, display unit 500 further receives information about the CFR values derived for the plurality of positions along the longitudinal axis of the vessel of interest.

[0152] In step S502, display unit 500 generates a first graphical representation of the X-ray angiography image 10 and a second graphical representation of the indication of the region of interest. In this exemplary embodiment, display unit 500 further generates a third graphical representation of the CFR values. In step S503, display unit jointly displays the first, the second and the third graphical representation. Hereby, the first, second and third graphical representation may particularly embodied as described in relation to FIG. 1.

[0153] Although in above described embodiments, the diagnostic images have been obtained using X-ray angiography, it shall be understood that in other embodiments, the diagnostic images may be retrieved by other imaging methods, such as helical computed tomography or sequential computed tomography, magnetic resonance imaging, ultrasound imaging, or the like.

[0154] Further, while in the above embodiments, the modeling has been performed on the coronary physiology, in other embodiments, the modeling may likewise be performed on other image-derived physiologies of the human body. As an example, the approach may be applied to model the peripheral arteries in the human body.

[0155] It may further be understood that while in the above-embodiments, the cross sectional vessel lumen and the respective vessel diameter have been used as geometric parameters determined from the geometric model, other geometric parameters may likewise be derived.

[0156] Although in the above described embodiments the hemodynamic parameters derived based on the fluid dynamics model in the physiological model and the intravascular measurement data are related to the blood flow, it is to be understood that, likewise, other hemodynamic parameters may be derived, such as blood pressure, blood viscosity, vessel wall friction, or the like.

[0157] Further, it shall be understood that, also in the above described embodiments the fluid outflow from the vessel of interest through respective vessel branches has been assessed and modeled, the principles described above may likewise be used to assess and model the fluid inflow into the vessel of interest.

[0158] Other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure, and the appended claims.

[0159] In the claims, the word “comprising” does not exclude other elements or steps, and the indefinite article “a” or “an” does not exclude a plurality.

[0160] A single unit or device may fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.

[0161] Procedures like the receiving of the at least one diagnostic image, the generating of a physiological model, the extraction of the geometric parameter values, the determining of the local reduction of the geometric parameter values, the predicting of the vessel branch et cetera performed by one or several units or devices can be performed by any other number of units or devices. These procedures in accordance with the invention can hereby be implemented as program code means of a computer program and/or as dedicated hardware.

[0162] A computer program may be stored/distributed on a suitable medium, such as an optical storage medium or a solid-state medium, supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems.

[0163] Any reference signs in the claims should not be construed as limiting the scope.

[0164] The invention relates to an apparatus for assessing a patient's vasculature, comprising an input unit configured to receive at least one diagnostic image of the vasculature, a modeling unit configured to generate, on the basis of the at least one diagnostic image, a physiological model comprising a geometric model of a vessel of interest in the vasculature, an extraction unit configured to extract, on the basis of the geometric model, a plurality of geometric parameter values for a geometric parameter of the vessel of interest at a plurality of positions along a longitudinal axis of the vessel of interest and an evaluation unit configured to determine, from the plurality of geometric parameter values at the plurality of positions, a local reduction of at least one geometric parameter value at at least one candidate position and to predict, at the at least one candidate position, at least one vessel branch of the vessel of interest.

[0165] By means of this apparatus an improved assessment of the patient's vasculature is enabled as the effects of fluid outflow from bifurcations in each vessel of interest are taken into consideration.