Method and device for operating a medical imaging device for the positionally correct representation of non-anatomical structures during an imaging examination

1. A method for operating a medical imaging device for the positionally correct representation of non-anatomical structures during an imaging examination, the method comprising:

providing a first 3D image which contains at least one anatomical structure;

extracting at least one anatomical model from the at least one anatomical structure of the first 3D image;

providing at least two 2D update images captured simultaneously or at different times;

extracting non-anatomical structures from a first subset of the 2D update images;

extracting anatomical structures from a second subset of the 2D update images;

calculating a non-anatomical 3D image from at least two 3D limited projection geometry reconstructions, wherein calculating the non-anatomical 3D image comprises:

calculating the at least two 3D limited projection geometry reconstructions from the extraction of the non-anatomical structures; and

removing artifacts from the at least two 3D limited projection geometry reconstructions using a machine learning method;

reconstructing an anatomical 3D image from the extraction of the anatomical structures;

registering the anatomical 3D image with the first 3D image by determining a coordinate transformation; and

creating a navigation volume based on the at least one anatomical model and the non-anatomical 3D image by using the determined coordinate transformation.

2. The method of claim 1 , wherein the navigation volume is additionally created based on the anatomical 3D image by using the determined coordinate transformation.

3. The method of claim 1 , wherein providing the first 3D image comprises receiving the first 3D image from an X-ray C-arm apparatus, a computer tomography system, or a magnetic resonance tomography system.

4. The method of claim 1 , wherein providing the 2D update images comprises receiving the 2D update images from an X-ray C-arm apparatus or a computed tomography system.

5. The method of claim 1 , wherein, in the absence of non-anatomical structures in a 2D update image of the first subset, the non-anatomical 3D image is further reconstructed.

6. The method of claim 1 , wherein newly acquired 2D update images are added to at least one of the first subset and the second subset, and wherein at least one of the non-anatomical 3D images and the anatomical 3D images are reconstructed at a temporal rate corresponding to a function of an acquisition rate of the 2D update images.

7. The method of claim 1 , further comprising, when a new 2D update image has been acquired:

adding the new 2D update image to the second subset of the provided 2D update images; and

at least partially reconstructing the anatomical 3D image again in a further reconstruction, wherein all temporally older 2D update images are reused or removed for the further reconstruction.

8. The method of claim 1 , wherein the calculation of the non-anatomical 3D image from the at least two 3D limited projection geometry reconstructions is performed by using a machine learning method comprising a neural network.

9. The method of claim 1 , further comprising performing a movement correction to correct for movements of a patient having the anatomical structure.

10. The method of claim 1 , wherein the extraction of the anatomical and non-anatomical structures from the 2D update images is performed using at least one machine learning method comprising a neural network.

11. The method of claim 1 , wherein the non-anatomical 3D image is calculated based at least in part on the 2D update images by using a single machine learning method comprising a neural network.

12. The method of claim 1 , wherein the non-anatomical structures extracted from the first subset of the 2D update images comprise non-anatomical structures that belong to a predetermined object class or combination of object classes.

13. The method of claim 1 , further comprising separately reconstructing a subset of the non-anatomical structures that belong to a predetermined object class or combination of object classes.

14. The method of claim 1 , wherein the non-anatomical structures comprise at least one of a guide wire and a catheter.

15. A gantry-based medical imaging system for carrying out an imaging examination and representation of non-anatomical structures, the device comprising:

a provisioning unit configured to provide a first 3D image, the first 3D image including at least one anatomical structure;

at least two image chains configured to record at least one of 2D update images and 3D images;

a computing unit comprising one or more processors configured by computer-executable instructions stored in the computing unit to at least:

extract an anatomical model from the at least one anatomical structure in the first 3D image;

extract anatomical and non-anatomical structures from the 2D update images;

calculate 3D limited projection geometry reconstructions from the extraction of non-anatomical structures;

calculate a non-anatomical 3D image from at least two of the 3D limited projection geometry reconstructions, wherein calculating the non-anatomical 3D image comprises removing artifacts from the at least two 3D limited projection geometry reconstructions using a machine learning method;

calculate an anatomical 3D image from the extraction of anatomical structures; and

create a navigation volume from the anatomical model and the non-anatomical 3D image by determining a coordinate transformation, wherein the coordinate transformation causes the anatomical model to be positioned correctly in the navigation volume; and

a display configured to display the navigation volume.