A method for computing a pressure across a particular heart vessel, the method comprising: receiving image data defining a three-dimensional image volume representative of an anatomical region of interest comprising a cardiac vascular structure, the image data comprising a plurality of voxels; identifying features characterizing local variations of intensity in the image data on a per-voxel basis using an intensity model which identifies a region of interest in the 3D Image Volume where the heart vessel is expected to be present based on differences in image intensity and its variations between individual voxels of the Image Volume, wherein the intensity model is a Bayesian Classifier trained using intensity features present in previously acquired image data; and deriving a plurality of weighting values corresponding to the plurality of voxels using the features characterizing local variations of intensity using the intensity model, wherein from a weight the probability that a particular voxel is inside the region of interest can be derived; launching a computational model directly on the 3D image volume using the obtained weights, wherein model calculation is performed across the entire 3D image volume and wherein the weights are used to define a force value that is strong if the flow is going through what appears to be the vessel and that provides a lot of resistance for flow in areas that appear to be tissue; wherein the computational model provides a measure of fluid dynamics within the cardiac vascular structure, wherein the measure of fluid dynamics corresponds to pressure differences within the cardiac vascular structure, wherein the computational model is designed for hemodynamic flow modeling using computational fluid dynamics, wherein the computational model utilizes local image intensity and its variations to direct the computations towards the desired biomarker, wherein the mathematical formulation for the fluid flow applied by the computational model is determined by: ρ ∂ u ∂ t + ∇. uu = − ∇ p + μ ∇ 2 u + F where u is the blood velocity, p is the blood pressure, ρ and μ are the blood density and viscosity respectively, wherein the term F is the force which applies a drag term, allowing the area to be modeled without prior segmentation, wherein this force F is modeled by the following equation: F = − μ α u − 1 2 C 2 ρ u u where the weighting terms α and C 2 represent local effective material properties which depend on the image intensity and its local variations, wherein the weighting terms α and C 2 are selected so as to establish that most of the flow is in the vascular network, wherein the forces are taken to be negative in velocity, since the force acts to retard the flow, acting as the drag term. receiving image data defining a three-dimensional image volume representative of an anatomical region of interest comprising a cardiac vascular structure, the image data comprising a plurality of voxels; identifying features characterizing local variations of intensity in the image data on a per-voxel basis using an intensity model which identifies a region of interest in the 3D Image Volume where the heart vessel is expected to be present based on differences in image intensity and its variations between individual voxels of the Image Volume, wherein the intensity model is a Bayesian Classifier trained using intensity features present in previously acquired image data; and deriving a plurality of weighting values corresponding to the plurality of voxels using the features characterizing local variations of intensity using the intensity model, wherein from a weight the probability that a particular voxel is inside the region of interest can be derived; launching a computational model directly on the 3D image volume using the obtained weights, wherein model calculation is performed across the entire 3D image volume and wherein the weights are used to define a force value that is strong if the flow is going through what appears to be the vessel and that provides a lot of resistance for flow in areas that appear to be tissue; wherein the computational model provides a measure of fluid dynamics within the cardiac vascular structure, wherein the measure of fluid dynamics corresponds to pressure differences within the cardiac vascular structure, wherein the computational model is designed for hemodynamic flow modeling using computational fluid dynamics, wherein the computational model utilizes local image intensity and its variations to direct the computations towards the desired biomarker, wherein the mathematical formulation for the fluid flow applied by the computational model is determined by: ρ ∂ u ∂ t + ∇. uu = − ∇ p + μ ∇ 2 u + F where u is the blood velocity, p is the blood pressure, ρ and μ are the blood density and viscosity respectively, wherein the term F is the force which applies a drag term, allowing the area to be modeled without prior segmentation, wherein this force F is modeled by the following equation: F = − μ α u − 1 2 C 2 ρ u u where the weighting terms α and C 2 represent local effective material properties which depend on the image intensity and its local variations, wherein the weighting terms α and C 2 are selected so as to establish that most of the flow is in the vascular network, wherein the forces are taken to be negative in velocity, since the force acts to retard the flow, acting as the drag term.