US 11,925,434 B2
Deep-learnt tissue deformation for medical imaging
Li Zhang, Princeton, NJ (US)
Assigned to Siemens Healthineers AG, Forchheim (DE)
Filed by SIEMENS HEALTHINEERS AG, Forchheim (DE)
Filed on Sep. 23, 2021, as Appl. No. 17/482,881.
Application 17/482,881 is a division of application No. 15/627,840, filed on Jun. 20, 2017, granted, now 11,154,196.
Prior Publication US 2022/0007940 A1, Jan. 13, 2022
Int. Cl. A61B 5/00 (2006.01); G06T 3/00 (2006.01); G06T 7/00 (2017.01); G06T 7/30 (2017.01); G06T 7/33 (2017.01)
CPC A61B 5/004 (2013.01) [A61B 5/0035 (2013.01); A61B 5/0037 (2013.01); G06T 7/30 (2017.01); G06T 7/33 (2017.01); G06T 7/337 (2017.01); G06T 3/0068 (2013.01); G06T 3/0093 (2013.01); G06T 7/0016 (2013.01); G06T 2207/30004 (2013.01)] 7 Claims
OG exemplary drawing
 
1. A method for medical image fusion by a medical imaging system, the method comprising:
acquiring first and second sets of scan data representing a patient with anatomy represented by the first set of scan data deformed relative to anatomy represented by the second set of scan data;
determining a deformation field aligning the anatomy of the first and second sets of scan data with a machine-learnt deep neural network, the machine-learnt deep neural network outputting the deformation field; and
generating a medical image from the first and second sets of scan data and the deformation field.