A computer-implemented medical method of 2D/3D image co-registration, the method comprising the steps: providing 2D X-ray image data of a vascular structure containing a contrast agent (step S1), wherein the vascular structure contains a vascular malformation, - providing 3D image data of the vascular structure (step S2), - transforming the 3D image data into transformed 2D image data (step S3), - overlaying the 2D X-ray image data and the transformed 2D image data and calculating a correspondence between the 2D X-ray image data and the transformed 2D image data thereby determining a co-registration between the 2D X-ray image data and the 3D image data (step S4), - determining in the co-registered 2D X-ray image data the vascular malformation (step S5), and - calculating a 3D representation of the vascular malformation based on the determined vascular malformation of the co-registered 2D X-ray image data (step S6), wherein the malformation is an Arteriovenous malformation, and wherein the 2D X-ray image data image the vascular structure and a distribution of the contrast agent in a distribution state, which allows for a distinction between arterial inflow into the AVM and venous outflow of the AVM, and wherein the 2D X-ray image data are a plurality of images showing a time sequence of the distribution of the contrast agent in the vascular structure, and wherein the method further comprises the steps: - identifying a best image out of the plurality of images with respect to the distinction between the arterial inflow into the AVM and the venous outflow of the AVM, and - summing all images of the plurality of images up to a point of time at which the identified best image was generated. providing 2D X-ray image data of a vascular structure containing a contrast agent (step S1), wherein the vascular structure contains a vascular malformation, - providing 3D image data of the vascular structure (step S2), - transforming the 3D image data into transformed 2D image data (step S3), - overlaying the 2D X-ray image data and the transformed 2D image data and calculating a correspondence between the 2D X-ray image data and the transformed 2D image data thereby determining a co-registration between the 2D X-ray image data and the 3D image data (step S4), - determining in the co-registered 2D X-ray image data the vascular malformation (step S5), and - calculating a 3D representation of the vascular malformation based on the determined vascular malformation of the co-registered 2D X-ray image data (step S6), - providing 3D image data of the vascular structure (step S2), - transforming the 3D image data into transformed 2D image data (step S3), - overlaying the 2D X-ray image data and the transformed 2D image data and calculating a correspondence between the 2D X-ray image data and the transformed 2D image data thereby determining a co-registration between the 2D X-ray image data and the 3D image data (step S4), - determining in the co-registered 2D X-ray image data the vascular malformation (step S5), and - calculating a 3D representation of the vascular malformation based on the determined vascular malformation of the co-registered 2D X-ray image data (step S6), wherein the malformation is an Arteriovenous malformation, and wherein the 2D X-ray image data image the vascular structure and a distribution of the contrast agent in a distribution state, which allows for a distinction between arterial inflow into the AVM and venous outflow of the AVM, and wherein the 2D X-ray image data are a plurality of images showing a time sequence of the distribution of the contrast agent in the vascular structure, and wherein the method further comprises the steps: - identifying a best image out of the plurality of images with respect to the distinction between the arterial inflow into the AVM and the venous outflow of the AVM, and - summing all images of the plurality of images up to a point of time at which the identified best image was generated. - identifying a best image out of the plurality of images with respect to the distinction between the arterial inflow into the AVM and the venous outflow of the AVM, and - summing all images of the plurality of images up to a point of time at which the identified best image was generated.