US 12,260,620 B2
Method for providing a trainable function for determination of synthetic image data
Martin Kraus, Fuerth (DE)
Assigned to SIEMENS HEALTHINEERS AG, Forchheim (DE)
Filed by Siemens Healthcare GmbH, Erlangen (DE)
Filed on Mar. 15, 2022, as Appl. No. 17/694,958.
Claims priority of application No. 10 2021 202 672.9 (DE), filed on Mar. 18, 2021.
Prior Publication US 2022/0301289 A1, Sep. 22, 2022
Int. Cl. G06V 10/774 (2022.01); G06V 10/74 (2022.01)
CPC G06V 10/774 (2022.01) [G06V 10/761 (2022.01); G06V 2201/03 (2022.01)] 15 Claims
OG exemplary drawing
 
1. A computer-implemented method for providing a trainable function for determination of synthetic image data, the method
comprising:
receiving first medical image data,
the first medical image data being based on a first medical imaging of an examination object;
receiving second medical image data,
the second medical image data being based on a second medical imaging of the examination object, the first and the second medical imaging differ in at least one of the imaging modality used or in an imaging protocol used, the first and the second medical image data are registered with one another;
determining the synthetic image data by applying the trainable function to the first medical image data;
determining a measure of similarity with a similarity function by comparison of the synthetic image data and the second medical image data, the determining the measure of similarity is based on an optimization method, and the optimization method comprises a maximization of the measure of similarity by at least one of a geometrical or photometric transformation of at least one of the synthetic image data or of the second medical image data, the optimization method including,
determining at least one first part area of the synthetic image data,
determining a plurality of second part areas of the second medical image data,
determining a plurality of part measures of similarity between the at least one first part area and the plurality of second part areas, and
determining the similarity function based on the plurality of part measures of similarity, wherein
the similarity function is based on a comparison of the first part area of the synthetic image data and a selected second part area of the second medical image data, and
the selected second part area corresponds to that area of the plurality of second part areas with the maximum part measure of similarity;
adjusting at least one parameter of the trainable function by optimization of the similarity function based on the measure of similarity; and
providing the trainable function.