Patent Description:
Cardiovascular disease (CVD) is one of the leading causes of deaths worldwide. CVD generally refers to conditions that involve narrowed or blocked blood vessels that can lead to reduced or absent blood and therefore oxygen supply to the sections distal to the stenosis, resulting in, for instance, chest pain (angina) and ischemia. A very important aspect in the prevention and treatment of CVD is the functional assessment of such narrowed or blocked blood vessels.

At the moment X-ray angiography is the standard technique for anatomical assessment of the coronary arteries and the diagnosis of coronary artery disease. During X-ray angiography several different two-dimensional images, also called two-dimensional projections, of the object under examination can be obtained from different views or perspectives by rotating the arm, holding the X-ray source and the image intensifier, with reference to the patient.

Although objectivity, reproducibility and accuracy in assessment of lesion severity has improved by means of quantitative coronary analysis tools, the functional significance of atherosclerotic lesions, which is the most important prognostic factor in patients with coronary artery disease, cannot be appreciated by conventional angiography which quantifies the obstruction severity based on extracted geometric features.

For intermediate coronary lesions (<NUM>% - <NUM>%), for instance, it is not always obvious if the stenosis is a risk for the patient and if it is desired to take action. Overestimation of the severity of the stenosis can cause a treatment which in hindsight would not have been necessary. Therefore exposing the patient to risks that are not necessary. Underestimation of the stenosis, however, could induce risks because the patient is left untreated when the stenosis is in reality severe. Especially for these situations it is desired to have an additional functional assessment to aid in a good decision making.

Fractional Flow Reserve (FFR) has been used increasingly over the last <NUM>-<NUM> years as a method to identify and effectively target the coronary lesion most likely to benefit from percutaneous coronary intervention (PCI). FFR is a technique used to measure pressure differences across a coronary artery stenosis to determine the likelihood that the stenosis impedes oxygen delivery to the heart muscle. The technique involves percutaneous inserting a pressure-transducing wire inside the coronary artery and measuring the pressure behind and before the lesion. This is best done in a hyperemic state; in the case of maximum hyperemia, blood flow to the myocardium is proportional to the myocardium perfusion pressure. FFR therefore provides a quantitative assessment of the functional severity of the coronary lesion as described in <NPL>. <CIT> and <CIT> also propose assessment of the coronary system.

Both the ESC and ACC/AHA guidelines recommend the use of FFR in patients with intermediate coronary stenosis (<NUM>% - <NUM>%). Around <NUM> FFR measurements are annually performed in the EU.

FFR, however, has some disadvantages. The technique is associated with the additional cost of a pressure wire which can be only be used once. Furthermore, measuring FFR requires invasive catheterisation with the associated cost and procedure time. Also, in order to induce (maximum) hyperemia, additional drug infusion (adenosine or papaverine) is required, which is an extra burden for the patient.

A method, that reduces costs and improves patient management, is virtual fractional flow reserve (vFFR). In vFFR, computational fluid dynamics (CFD) computations are used to estimate non-invasively the coronary blood flow circulation and derive the fractional flow reserve resulting from a coronary lesion.

One of the most difficult aspects of vFFR is the coupling of the different aspects of the computations (anatomical as well as functional) without having high computational complexity, but still incorporating as much patient specific information as needed for accurate computations.

Sophisticated numerical models have been developed that combine computational fluid dynamics (CFD) and finite element models (FEM) in order to derive patient-specific diagnostic information such as <NPL>.

One of the largest challenges is to apply realistic boundary conditions in order to simulate dynamic blood flow in the extracted geometry of the imaged vascular system.

In <NPL>" pressure gradients are computed using CFD in which the geometry of the aorta is extracted from MRA. Additional MR Phase contrast imaging is performed to measure the velocity which is used as boundary conditions.

In <NPL>, lumped parameter models of the heart, systemic circulation and coronary microcirculation are coupled to a patient specific 3D model of the aortic root and epicardial coronary arteries extracted from CTA. Disadvantages of these approaches are that all calculations are performed exclusively in 3D. This results in a method that is of high computational complexity. Furthermore, due to the fact that these methods required MR or CT imaging, they cannot be used during the intervention in which x-ray angiography is the standard imaging modality.

In order to keep the computational demands on a feasible level a reduced model can be used in the calculation. That is, sections of the coronary tree can be represented by a one-dimensional network or zero-dimensional (lumped) model.

This multi-scale approach was adopted by <NPL> to compute physiologically realistic pressure and flow waveforms in coronary vessels at baseline conditions. 3D CFD simulations were coupled with an analytical 1D model of the circulation and a lumped-parameter model of the coronary resistance. However some underlying assumptions of these methods provide limitations as described by <NPL>. For instance the assumption that the myocardium is healthy. Flow, for example, depends on the amount of viable myocardium and oxygen consumption. Furthermore, vascular remodelling and collateral flow are not considered, therefore the assumption is made that no collateral arteries are present which feed the coronary vessel bed distal to the lesion.

The status of the myocardium microvasculature indicates if a certain portion of the heart can be regarded to be healthy. For instance the presence of ischemia is an indication that a certain portion of the heart is not supplied with enough blood for example due to an (earlier) infarction (<FIG>). This has an effect on the microvascular resistance and should be adjusted accordingly in the model calculations.

Furthermore, the presence of collateral flow is an adaptation of the vessels where the collateral vessels provide the heart with blood by bypassing the lesion (<FIG>). The effect of this is that, even in the case of a very severe stenosis (for instance a total occlusion), the sections distal to the stenosis have adapted blood flow. Therefore in practice the effect of the stenosis is not necessarily severe, and not always a revascularization is the preferred treatment.

When collateral flow is present, this also has an effect on the calculations and should therefore be compensated. However, due to their size these collateral vessels are not commonly visible on X-ray angiography images and further steps are needed to determine the presence of the collateral flow based on X-ray angiography.

There is thus the need for a patient specific method that can be used during an intervention, which has low computational complexity and can cope with the status of the myocardial microvasculature and collateral flow.

It is thus an object to provide a method for quantitatively assessing a flow according to claim <NUM>, a computer product according to claim <NUM> and a X-Ray imaging device according to claim <NUM>.

The characteristics of the invention and the advantages derived therefrom will be more apparent from the following description of non-limiting embodiments, illustrated in the annexed drawings, in which:.

<FIG> shows a flow chart illustrating the operations according to an embodiment of the present application. The operations employ an imaging system capable of acquiring and processing two-dimensional images of a vessel organ (or portion thereof) or other object of interest. For example a single plane or bi-plane angiographic system can be used such as those manufactured, for example, by Siemens (Artis zee Biplane) or Philips (Allura Xper FD).

<FIG> is a functional block diagram of an exemplary single plane angiographic system, which includes an angiographic imaging apparatus <NUM> that operates under commands from user interface module <NUM> and will provide data to data processing module <NUM>. The single plane angiographic imaging apparatus <NUM> captures a two-dimensional X-ray image of the vessel organ of interest for example in the postero-anterior (PA) direction. The single plane angiographic imaging apparatus <NUM> typically includes an X-ray source and detector pair mounted on an arm of a supporting gantry. The gantry provides for positioning the arm of the X-ray source and detector at various angles with respect to a patient who is supported on a table between the X-ray source and detector. The data processing module <NUM> may be realized by a personal computer, workstation or other computer processing system. The data processing module <NUM> processes the two-dimensional image captured by the single plane angiographic imaging apparatus <NUM> to generate data as described herein. The user interface module <NUM> interacts with the user and communicates with the data processing module <NUM>. The user interface module <NUM> can include different kinds of input and output devices, such as a display screen for visual output, a touch screen for touch input, a mouse pointer or other pointing device for input, a microphone for speech input, a speaker for audio output, a keyboard and/or keypad for input, etc. The data processing module <NUM> and the user interface module <NUM> cooperate to carry out the operations of <FIG> as described below.

The operations of <FIG> can also be carried out by software code that is embodied in a computer product (for example, an optical disc or other form of persistent memory such as a USB drive or a network server). The software code can be directly loadable into the memory of a data processing system for carrying out the operations of <FIG>. Such data processing system can also be physically separated from the angiographic system used for acquiring the images making use of any type of data communication for getting such images as input.

In this example it is assumed that the imaging system has acquired and stored at least two two-dimensional images of an object of interest. Any image device capable of providing two-dimensional angiographic images can be used for this purpose. For example a bi-plane or single plane angiographic system can be used such as those manufactured, for example, by Siemens (Artis zee Biplane) or Philips (Allura Xper FD).

At <NUM>, the data processing module <NUM> is fed by at least two bi-dimensional images of the tree, or part of the tree, of conduits which have been obtained from different perspectives.

The data processing module at <NUM> generates a 3D reconstruction using the two dimensional images. At <NUM> the data processing module <NUM> makes calculations based on the 3D reconstruction to determine geometrical features of the conduits such as diameters, lengths, curvatures, centrelines or the like. These features are used to determine functional parameters, such as flow and pressure, in at least a segment of interest of the tree.

At <NUM> the data processing module <NUM> retrieves from a storage unit or select from any available data source a multi-scale functional model, also called within the present description 1D/0D model, of the tree upon user input as thought for example by Kim et al, "Patient-specific modelling of blood flow and pressure in human coronary arteries". When the tree is a coronary tree and the perfused organ the myocardium of the heart, the 1D/0D model is a heart model that may be, for example, chosen between a number of predetermined models comprising a left dominant, right dominant, balanced or small right/left dominant model of the coronary tree depending on heart type.

If in the data processing model the segment of interest is identified, the part of the functional model related to the segment of interest can be adjusted at <NUM> using the functional parameter derived from the geometrical features of the 3D reconstruction.

At <NUM>, the data processing module may perform quantitative image analysis to correct the functional model. This can, for instance, be done by taking into account the myocardium status or presence of the collateral flow. For instance the presence of ischemia is an indication that a certain portion of the heart is not supplied with enough blood for example due to an (earlier) infarction. Also the presence of collateral flow can make the stenosis less severe because the blood flow may bypass the coronary lesion in the main artery and supply enough oxygenated blood to the tissue distal to the coronary lesion.

At <NUM>, the processing module performs quantitative flow analysis. The vFFR value for each centreline point of the 3D reconstruction is, for example, calculated and shown on a display for the user.

Embodiments are particularly advantageous in coronary tree analysis based on two-dimensional (2D) angiographic X-ray images and they will be mainly disclosed with reference to this field, particularly for coronary arteries flow assessment, with the myocardium being the perfused organ. It is however to be understood that myocardium and arteries of the coronary tree can be substituted with any object perfused by a bed of tubular organs. FFR determination is one of the goals, although it can be appreciated that the teaching of the present disclosure can be used for determining any other flow parameter thanks to a smart flow analysis based on a 3D reconstruction coupled in a 1D/0D model of a tree of conduits in general, the coronary tree in particular.

An embodiment is now disclosed with reference to <FIG>. The therein-depicted operations can, obviously, be performed in any logical sequence and can be omitted in parts. More details about the patient specific 3D reconstruction and model as shown by operation <NUM> in <FIG> is described by operations <NUM> to <NUM> as presented in <FIG>. In <FIG> operations <NUM> and <NUM> describe in more detail the patient specific multi-scale functional model (1D/0D) as described by operation <NUM> of <FIG>. For clarity, the coupling of the 3D model and 1D/0D model as depicted by operation <NUM> in <FIG> is divided in smaller operations (<NUM> to <NUM>) and they are depicted in <FIG>. Clarification of adjustment of the coupled model by the patient specific cardiac status as depicted in <FIG> by operation <NUM> has been given by the operations <NUM> to <NUM> in <FIG>.

Turning to <FIG>, in order to determine an accurate vFFR for a coronary lesion, a patient specific model is generated by the processing module <NUM>, hereinafter also called processor. In order to get a good understanding of the coronary blood flow and pressure drop, the entire coronary tree is preferably considered. When a 3D model of the entire coronary tree is made, this has some disadvantages. First, an imaging modality is required that cannot be used during the procedure (for instance CT). Second, this approach will lead to calculations that are too time consuming and are in the order of several hours. Therefore an embodiment provides for the coronary tree to be modeled as a 1D/0D model. This simplification decreases the computational complexity significantly. The first operation of the embodiment is therefore to input/construct a patient specific 1D/0D model as shown in operation <NUM> of <FIG>.

In order to construct a patient specific 1D/0D model, at <NUM> additional information specific for the patient, such as a heart type, is input from the user, particularly which type of the coronary dominant system, is applicable for the patient. A heart model, for example, depends on the heart type. The heart type is determined by the blood supply of the posterior descending artery. The posterior descending artery is supplied from blood by the right coronary artery, the left coronary artery or by both the right and left coronary artery. If e.g. the right coronary artery is the main supplier of the posterior descending artery, it is said that the patient has a right coronary dominance heart type. Likewise, a left coronary dominance heart type or balanced heart type are defined. For all heart types a specific heart model is developed. These heart models contain geometrical features (for instance diameters, vessel length and curvature) for all vessels in the coronary tree and take into account the coronary dominance. The vessel diameters, for instance, of e.g. a right coronary dominant heart model are different from the vessel diameters in a left coronary dominant heart model. The same is true for the vessel length.

At <NUM>, the heart type information is used by the processor to retrieve a standard 1D model to be used for this patient. The 1D model includes information on generalized diameter, length and spatial orientation of the skeleton of each segment within the coronary tree.

To improve this 1D model, the skeleton of the coronary tree (line through center of coronary arteries) may be used. For example a predefined skeleton model can be used, determined from averaged skeletons extracted from CT Angiography (CTA) image data. To make it more patient specific, predefined skeletons can be created for different heart models/types. The skeleton of the coronary tree is divided into multiple segments with specified diameter. This diameter can also be included in the predefined skeleton models as taught by <NPL>.

Furthermore the length and diameter of the segments can be determined from for example the CTA image data. Furthermore, the curvature of the arteries is taken into account by extracting the curvature per segment using for instance the skeleton of the coronary tree. If the local curvature is above a defined threshold, the segment is divided into multiple smaller segments, in this way the segment curvature is minimized and the segment's axial direction can be described by a Cartesian coordinate as expected for a 1D model. For every, subdivided segment a pressure loss reaction is defined based on the local curvature.

Next to this, the bifurcation angle between the main branch and side branches for all vessel bifurcations in the vessel tree are calculated using for instance the skeleton of the coronary tree. For every segment at a bifurcation in the vessel tree, a pressure loss is calculated taken into account the local bifurcation angle between the main vessel and side branch.

In case CTA data from the patient is available the actual skeleton, diameters, vessel length, vessel curvature and bifurcation angles can be determined to construct a patient specific 1D model. A 1D model is derived relating flow and pressure in these segments. This model can deal with wall deformation by taking into account mechanical properties of the wall and assuming axial symmetry, radial displacements and a constant pressure along each segment. This model can then be applied to each segment.

The 1D models of each of these segments are then coupled to construct one entire 1D model for the entire coronary tree taken into account pressure loss due to local curvature changes and bifurcation angles.

The 1D model has certain boundary conditions that apply, for example, at the end points of the vessels. Because the coronary vessels keep on branching into smaller vessels for a substantial amount, the vessel cannot be modelled in 1D entirely.

At a certain point the vessels are no longer modelled by the 1D model, but are lumped, however still containing the characteristics. This lumping is in practice a 0D model.

For the lumping of the endpoints of the vessels, often the hydraulic-electrical analogue is used. The blood pressure and flow-rate can be represented by voltage and current and the effects of friction and inertia in blood flow and of vessel elasticity can be described by using resistance, inductance and capacitance respectively. By doing this, the methods for analysis of electric circuits can be applied to cardiovascular dynamics. An example of a 0D cardiovascular system analysis model is the RC Windkessel model as shown in <FIG> and summarized by <NPL>.

At the vessel ends of the coronary tree, lumped parameter models are applied. The initial values of these components can be determined using scaling laws as taught by <NPL>.

The vessels of the coronary tree are modeled by 1D model and the vessel endpoints are lumped by 0D model, this makes a 1D/0D model. An example of a 1D/0D model for a subset of the coronary tree is shown in <FIG>.

The heart model contains geometrical features for all vessels in the coronary tree based on the coronary dominance. These geometrical features are based on common values as described in <NPL>. Using patient specific 3D geometrical features of a segment of the coronary tree, this segment in the heart model can be set to the patient specific geometrical features instead of the common geometrical features.

In <FIG>, at <NUM> the processor makes a patient specific 3D reconstruction of a subset of interest of the coronary tree which includes the coronary lesion of interest using multiple two-dimensional images as known in the art as, for instance, taught in "<NPL>. An example is shown in <FIG>.

At <NUM> in the 3D reconstruction a segment of interest is identified. This can be done manually, upon user input, or automatically or semi-automatically. This can either be done in the two-dimensional images with the processor identifying the corresponding segment in the 3D reconstruction or directly in the 3D reconstruction (as shown in <FIG>). For example, the processor may determine a zone with a narrowing or blockage of flow and thus identify a part of the image comprising such a zone. Alternatively or in combination, a user can indicate a region of interest in the 3D reconstruction or in one or both the two-dimensional images with the processor elaborating such information to determine the position of such zone in the 3D reconstruction.

To be able to perform further CFD calculations, at <NUM> a volume mesh is applied by the processor to the 3D reconstruction. To increase accuracy and computational speed of the CFD calculations element size and shape of the volume mesh can be varied throughout the vessel as taught, for instance, by <NPL>. These adjustments can depend on location in the vessel (e.g. smaller elements near vessel boundaries) and geometric properties/features like local curvature and diameter/area changes.

For example in regions of the vessel with high curvature, high resolution volumetric elements are used whereas, for regions with low curvature, low resolution elements are used. This is done to minimize the amount of elements in the volume mesh.

Because it is computationally complex to insert the 3D reconstruction into the 1D/0D model, a simplification of the 3D reconstruction is advantageously made. One way of doing this is by replacing the 3D reconstruction with a reduced 3D model made as shown in operation <NUM> of <FIG>. An advantage of this is the reduction of computation time.

Instead of using the 3D reconstruction, the 3D reconstruction is replaced by an equation that represents the pressure-flow relation. This equation is then used for further calculations. The equation can be extracted by the processor by means of CFD simulations.

CFD numerical methods and algorithms can be used to solve equations of fluid dynamics, for instance the coronary flow and pressure. These equations are based on conservation laws of classical physics (conservation of mass, momentum and energy). From these laws partial differential equations are derived and, where possible, simplified as taught by<NPL>. Simulations are used to simulate the interaction of blood with the lumen defined by boundary conditions (inlet and outlet conditions).

For these CFD simulations varying flow values can be used by the processor as a boundary condition to obtain pressure data. Optionally these calculations can be uploaded to a cloud and performed on multiple systems or uploaded to a high performance computing cluster to decrease the computation time.

Optionally, another way for reducing more computation time is to determine the pressure-flow relation based on geometric features of the 3D reconstruction as taught by <NPL>. A model can be created using features from the 3D geometry like minimum diameter, diameter changes and curvature.

The outcome of this operation performed by the processor is a fitted equation for the segment of interest that calculates a pressure drop for a given flow value.

At <NUM>, the processor can adjust the heart model using the variation between the patient specific geometrical features and the common geometrical features in the segment of the coronary tree.

In order to make the constructed 1D/0D model more accurate and to incorporate the 3D morphology of the lesion of interest, 3D information of a segment of interest of the coronary tree can be used. This information can be obtained from two-dimensional angiographic images.

After simplifying the 3D reconstruction, the reduced 3D model representing the segment of interest can be inserted in the 1D/0D model. However, certain input is required to guarantee a correct coupling. First, the segment of interest as indicated earlier by the user has a certain position within the 1D/0D model. At this position, the information of the 3D reconstruction is inserted. Therefore at <NUM> the processor receives an input from the user to indicate which segment(s) of the heart model (for instance the AHA model as shown in <FIG>) is represented by the 3D reconstruction. In contrast to the 3D model, the 1D model can cope with area changes, by taking into account the elasticity of the vessel wall. An advantage of using a coupled 1D - reduced 3D model is the absence of discontinuities (for example area discontinuities) at the interfaces of the models. At the position of the segment of interest, at <NUM> the processor adds the reduced 3D model to the 1D/0D model as an extra coupling condition.

Furthermore, the dimensions of the 1D/0D model are adjusted to fit the actual patient specific situation. To accomplish this, the processor performs the adjustments of the 1D/0D model to the 3D model using geometrical features extracted from the 3D reconstructed geometry. For instance at <NUM> the inlet radius of the 3D reconstruction segment can be used to adjust the generalized diameters within the complete 1D model.

To make the calculations even more accurate, the 1D/0D model can be adjusted to make it even more patient specific. Different aspects can be taken into account, for instance myocardium status, the presence of collateral flow, LV wall motion, coronary motion and patient information. These aspects have not been solved previously by prior art and are of great importance for the accurate determination of the virtual fractional flow reserve in patients.

In order to improve the calculations, it is important to know the status of the myocardium microvasculature as indicated by operation <NUM> of <FIG>. As the status of the myocardium microvasculature indicates if a certain portion of the heart can be regarded to be healthy, the status has an effect on the microvascular resistance and should be adjusted accordingly in the model calculations.

For instance the presence of ischemia is an indication that a certain portion of the heart is not supplied with enough blood for example due to an (earlier) infarction.

The status of the myocardium microvasculature can be determined by the processor by performing myocardial blush calculations. Myocardial blush is for instance calculated using two-dimensional angiographic images. In a frame of an angiographic image run a region of interest is defined distal to the expected infarct area. A motion correction between the frames in the image run is calculated using for instance correlation technique. The region of interest is shifted per frame according to the calculated motion offset. A background mask is composed in every frame of the image run by for instance a median filter. The average pixel intensity in the region of interest per image mask (e.g. <NUM> by <NUM> pixels) is calculated by subtracting the calculated background mask to image intensity of the original image for all images in the image run. In this way only image intensity of small sized structures are taken into account over time. In this way the myocardial blush can be quantified over time within the region of interest. The myocardial blush calculations as known in the art, as taught, for instance, by <NPL>. This can be done for one or multiple large or small sections of the heart.

Because the calculations are performed on two-dimensional angiographic images, the section of the heart that the user wishes to investigate suffers from foreshortening and superimposing. However in order to accurately determine the myocardium status, these effects is preferably minimized.

This can, for instance, be done by performing a 3D blush measurement. That is, by performing a blush measurement in both projection used to construct the 3D reconstruction or any other bi-dimensional projection. In each image the user indicates a region in which the measurements should be performed. Using the geometric information belonging to both image perspectives, for example rotation, angulation, magnification, an intersection region of the images can be calculated. Using this information, a distinction can be made between, for example, the posterior side or anterior side of the myocardium.

Another manner to minimize the effect of foreshortening and superimposing is by the use of a three-dimensional imaging modality such as from CT or MR.

When using CT imaging information, for example, a patient specific anatomical model of the patient's heart muscle can be made.

The CT data can be registered by the processor to the two-dimensional X-ray images that were used to create the 3D reconstruction as described in step <NUM> of <FIG>. This can be done, for instance, by <NPL>.

By back projecting the information of the CT images onto the two-dimensional angiographic images, a more accurate semi 3D reconstruction can be made. This results in a more accurate indication of where the myocardium status measurement is to be performed. This information can for instance include information on rotational and angulation position of the X-ray imaging device, aiding in the effort to minimize foreshortening and superimposing.

When the myocardial blush is measured in specific regions of the heart, the results are used to adjust components of the 1D/0D model. However, when the blush is measured in a larger section of the heart, the results can be used to adjust the 1D/0D model between patients.

As an example of adjusting specific components of the heart model using myocardial status, at <NUM> the processor adjusts end-resistances of the model as shown in <FIG>. For example an increased microvascular resistance of a specific part of the heart is measured using blush. This can be incorporated in the model by adjusting the value of the end-resistance belonging to the coronary artery supplying that region of the heart with blood using a weighting factor.

At <NUM>, the processor adjusts the 1D/0D model for the presence of collateral flow. Collateral flow is an important factor for the calculations because the blood flow may bypass the coronary lesion in the main artery and supply enough oxygenated blood to the tissue distal to the coronary lesion, making the stenosis less severe. There are two types of collaterals, those across lesions and those that arise from other coronary artery/arteries. In order to obtain accurate vFFR results, the calculations have to be adjusted for the presence of collateral flow. The possible presence of collateral flow can be determined as taught in the art by <NPL> and further improving this method with the use of more generic velocity information based on one or multiple angiographic images by using densitometric and geometric information.

For instance, at multiple frames from an angiographic image sequence, the propagation of the contrast along for instance a coronary artery can be determined. By defining the contrast density along the coronary artery at different frames within the angiographic image sequence, and measuring the length of the contrast density front based on densitometry information the contrast velocity can be extracted. In order to make the length measurement more accurate a 3D reconstruction of the coronary can be used. The length as determined in the 3D reconstruction is not subjected to, for example, foreshortening. Based on the local diameter and/or cross sectional area of the coronary combined with velocity information, the flow can be determined. Furthermore densitometric information from and around the coronary lesion also has a relation to the flow through that specific section of the coronary. For instance densitometric calculations can be performed just outside the coronary. If the densitometric calculations indicate that there is contrast present just outside the coronary, this indicates the presence of collateral flow. This cannot be established without densitometric information because the collaterals are too small for imaging.

When a biplane imaging system is used, such as those manufactured, for example, by Siemens (Artis zee Biplane) or Philips (Allura Xper FD), the collateral flow measurements can be made more accurate. The temporal resolution of the measurements can be improved by using the delay between each acquired frame with respect to frontal and lateral imaging source.

In the case of presence of collateral flow bypassing the lesion, at <NUM> the processor adjusts the 1D/0D model to cope with the amount of collateral flow. In the case of collateral flow across the lesion from the same artery, the 1D model is adjusted, for example, by adding one or more elements for the collateral flow parallel to the lesion in the 1D model as illustrated in <FIG>.

In the case of collateral flow arising from other coronary arteries, at <NUM> the processor adjusts the 1D/0D model accordingly. For example, extra 0D elements can be added simulating the collateral flow as illustrated in <FIG>.

Additionally, because the flow in rest state in the stenotic segment is determined, these calculations can be used to adjust the 0D parameters to adjust the 1D/0D model to patient specific measurements.

Optionally left ventricular X-ray angiogram images can be used to analyze the wall motion of the left ventricle. Using a two-dimensional image (either monoplane or biplane) of the end diastolic phase of the heart and of the end systolic phase of the heart, the wall motion of the myocardium can be determined. The wall motion provides additional information about the cardiac status regarding specific regions of the heart. Furthermore, left ventricular X-ray angiograms provides an estimation of the myocardium mass.

For example, abnormal wall motion indicates diseased heart muscle tissue having less microvascular blood flow. The end-resistance of the coupled 1D/0D model, representing this tissue with abnormal wall motion, can then be decreased accordingly.

Optionally using 4D information (3D + time) of the coronary vessels, the motion of the coronary vessels can be determined. This motion provides local information about the cardiac muscle status.

For example, coronary vessel motion deviating from normal motion can indicate diseased heart muscle tissue. Similar to the "LV wall motion" the end-resistance belonging to the part of the heart with abnormal motion can be adjusted.

A further embodiment provides for the use of patient information to improve the adjustment of the 1D/0D model parameters to make the model more patient specific as shown at <NUM> of <FIG>. For instance patient height, weight, gender, age and heart type are used to calculate a correction factor for components of the 1D/0D models. This is done by the processor using methods well known in the art, for instance <NPL>.

Adjustment of the 1D/0D model components can be done, for instance, by adjusting 1D/0D model components between patients.

For example, tall and heavy weight patients have more cardiac mass and therefore more cardiac tissue that needs to be supplied with blood. The number of coronary arteries remains the same so there needs to be a difference in microvasculature. Therefore the end-resistance of each artery (representing the microvasculature) needs to be adopted accordingly.

Optionally, the patient specific myocardial condition and/or the myocardial arterial vulnerability is taken into account. The myocardial condition and/or myocardial arterial vulnerability can be assessed by biomarkers as shown in operation <NUM> of <FIG>. There are numerous biological parameters, which provide clear evidence of for instance inflammation, deviating metabolic processes and pathological processes as described in <NPL>. The biological parameters can be assessed by for instance blood analysis. An example of a biomarker that can be assessed by blood analysis is troponin. Elevation of troponin levels in the blood are an indication for acute myocardial infarction and heart muscle damage due to ischemia.

Incorporating biomarkers to adjust the functional model for the condition of the myocardium and/or myocardial arteries vulnerability makes the functional model more patient specific.

All calculations discussed so far, are performed and measured under rest. However, for accurate vFFR calculations hyperemia is advantageously simulated.

At <NUM>, the 1D/0D model is adjusted by the processor to deal with the hyperemic state. All the measurements performed for the calculations are done in rest state of the patient. Then, the 1D/0D model is adjusted accordingly and incorporating presence of collateral blood flow and myocardium vasculature status. This has as a large advantage that no hyperemic has to be induced in the patient and therefore no adenosine or papaverine needs to be administered to the patient. Adjustment of the model for hyperemia can for example be done by changing the end-resistances using scaling factors. This because in hyperemia, the demand of blood perfusion through the heart tissue increases by vasodilatation of the microvasculature. Vasodilatation can be modeled by decreasing the resistance.

Optionally information obtained from MR perfusion, CT perfusion and / or SPECT acquisitions can be used to add information regarding the myocardium microvascular flow during stress for a patient.

Because the adjustment for hyperemic state is done at this stage, all the patient specific adjustments to the 1D/0D model are also taken into account. However, if the x-ray imaging was performed during hyperemia, operation <NUM> can be omitted.

At <NUM> the processor calculates the vFFR. The 1D/0D model as described above is solved at <NUM> using the aortic pressure as inlet boundary condition. As a result, the vFFR value over the segment of interest is known, that is the distal pressure divided by the proximal pressure. This is known for each centerline point of the 3D reconstruction.

Just to make things clear, <FIG> describes an example step-by-step approach in the case of a stenosis in the left coronary artery. In this example aortic pressure is used as input boundary condition for adjusting pressure values in the 1D model. Information on blood pressure can be rendered available from a manometer connected to a catheter or by a cuff measurement on the patients arm.

In the present description it is made reference to a coronary tree where a stenotic artery exists, it is however to be appreciated by the skilled person that embodiments may consider also the case where several stenoses are present at different positions of the coronary tree. Each stenosis can, in fact, be calculated as a reduced 3D model to be inserted in the 1D/0D model to obtain a more accurate representation of reality.

Operations can be performed by processor unit on a standalone system or included directly in, for instance, an x-ray fluorographic system or any other image system to acquire two dimensional angiographic images. <FIG> illustrates an example of a high-level block diagram of an x-ray cinefluorograpic system. In this block diagram an example is shown on how embodiments could integrate in such a system.

Portions of the system (as defined by various functional blocks) may be implemented with dedicated hardware, analog and / or digital circuitry, and / or one or more processors operating program instructions stored in memory.

The X-ray system of <FIG> includes an X-ray tubes <NUM> with a high voltage generator <NUM> that generates an X-ray beam <NUM>.

The high voltage generator <NUM> controls and delivers power to the X-ray tube <NUM>. The high voltage generator <NUM> applies a high voltage across the vacuum gap between the cathode and the rotating anode of the X-ray tube <NUM>.

Due to the voltage applied to the X-ray tube <NUM>, electron transfer occurs from the cathode to the anode of the X-ray tube <NUM> resulting in X-ray photon-generating effect also called Bremsstrahlung. The generated photons form an X-ray beam <NUM> directed to the image detector <NUM>.

An X-ray beam <NUM> consists of photons with a spectrum of energies that range up to a maximum determined by among others the voltage and current submitted to the X-ray tube <NUM>.

The X-ray beam <NUM> then passes through the patient <NUM> that lies on an adjustable table <NUM>. The X-ray photons of the X-ray beam <NUM> penetrate the tissue of the patient to a varying degree. Different structures in the patient <NUM> absorb different fractions of the radiation, modulating the beam intensity.

The modulated X-ray beam <NUM>' that exits from the patient <NUM> is detected by the image detector <NUM> that is located opposite of the X-ray tube. This image detector <NUM> can either be an indirect or a direct detection system.

In case of an indirect detection system, the image detector <NUM> consists of a vacuum tube (the X-ray image intensifier) that converts the X-ray exit beam <NUM>' into an amplified visible light image. This amplified visible light image is then transmitted to a visible light image receptor such as a digital video camera for image display and recording. This results in a digital image signal.

In case of a direct detection system, the image detector <NUM> consists of a flat panel detector. The flat panel detector directly converts the X-ray exit beam <NUM>' into a digital image signal.

The digital image signal resulting from the image detector <NUM> is passed through a digital image processing unit <NUM>. The digital image processing unit <NUM> converts the digital image signal from <NUM> into a corrected X-ray image (for instance inverted and/or contrast enhanced) in a standard image file format for instance DICOM. The corrected X-ray image can then be stored on a hard drive <NUM>.

Furthermore the X-ray system of <FIG> consists of a C-arm <NUM>. The C-arm holds the X-ray tube <NUM> and the image detector <NUM> in such a manner that the patient <NUM> and the adjustable table <NUM> lie between the X-ray tube <NUM> and the image detector <NUM>. The C-arm can be moved (rotated and angulated) to a desired position to acquire a certain projection in a controlled manner using the C-arm control <NUM>. The C-arm control allows for manual or automatic input for adjustment of the C-arm in the desired position for the X-ray recording at a certain projection.

The X-ray system of <FIG> can either be a single plane or a bi-plane imaging system. In case of a bi-plane imaging system, multiple C-arms <NUM> are present each consisting of an X-ray tube <NUM>, an image detector <NUM> and a C-arm control <NUM>.

Additionally, the adjustable table <NUM> can be moved using the table control <NUM>. The adjustable table <NUM> can be moved along the x, y and z axis as well as tilted around a certain point.

Furthermore a measuring unit <NUM> is present in the X-ray system. This measuring unit contains information regarding the patient that is an input for the calculations, for instance information regarding aortic pressure, biomarkers, and/or height, length etc..

A contrast control unit that is present in the X-ray system is described in <NUM>. Using this contrast control unit <NUM>, the user can control the contrast injection system <NUM> of the patient <NUM> in order to inject a contrast agent into the patient <NUM> to be able to perform densitometric image analysis using the general processing unit <NUM>.

A general unit <NUM> is also present in the X-ray system. This general unit <NUM> can be used to interact with the C-arm control <NUM>, the table control <NUM>, the digital image processing unit <NUM>, the measuring unit <NUM> and the contrast control unit <NUM>.

An embodiment is implemented by the X-ray system of <FIG> as follows. A clinician or other user acquires at least two X-ray angiographic images of a patient <NUM> by using the C-arm control <NUM> to move the C-arm <NUM> to a desired position relative to the patient <NUM>. The patient <NUM> lies on the adjustable table <NUM> that has been moved by the user to a certain position using the table control <NUM>.

The X-ray images are then generated using the high voltage generator <NUM>, the X-ray tube <NUM>, the image detector <NUM> and the digital image processing unit <NUM> as described above. These images are then stored on the hard drive <NUM>. Using these X-ray images, the general processing unit <NUM> generates a 3D reconstruction, integrates a reduced model of the 3D reconstruction into a multifunctional model and adjust the functional model using geometrical features of the 3D reconstruction.

The general processing unit <NUM> can adjust the functional model using the information of the measuring unit <NUM>.

Using this contrast control unit <NUM>, the user can control the contrast injection system <NUM> of the patient <NUM> in order to inject a contrast agent into the patient <NUM> to be able to perform quantitative image analysis using the general processing unit <NUM>.

Claim 1:
Computer implemented method for quantitative flow analysis of a tree of conduits perfusing an organ from at least two bi-dimensional images of the tree or part of the tree obtained from different perspectives by an angiographic system, the method comprising the following steps:
a) making a 3D reconstruction of at least part of the tree from said at least two bi-dimensional images;
b) identifying a segment of interest within the 3D reconstruction semi-automatically upon user input;
c) making calculations based on the 3D reconstruction to determine geometrical features of the conduits such as diameters, lengths, curvatures, centrelines or the like;
d) receiving indication from the user on the heart type to retrieve from a storage unit or select from any available data source a functional model of the tree to be considered for the flow analysis and to input the location of the segment of interest within such model, wherein the functional model consists of a 1D model and a 0D model, wherein the 1D model comprises multiple segments identifying the conduits forming the tree with end parts connected with the 0D model to take the boundary conditions into account, wherein the 1D model includes information on generalized diameter, length and spatial orientation of the skeleton of each segment within the tree and the 0D model comprises lumped parameters using a hydraulic-electrical analogue;
e) using the geometric features extracted from the 3D reconstruction to elaborate pressure equations of the functional model;
f) adjusting the part of the functional model related to the segment of interest using geometrical features of the 3D reconstruction by fitting an equation for the segment of interest that calculates a pressure drop for a given flow value to reduce computation time;
g) performing quantitative flow analysis based on the functional model so obtained which is displayed for the user.