US 12,279,900 B2
User interface for X-ray tube-detector alignment
Roger Steadman Booker, Aachen (DE); Walter Ruetten, Linnich (DE); and Matthias Simon, Aachen (DE)
Assigned to KONINKLIJKE PHILIPS N.V., Eindhoven (NL)
Appl. No. 17/642,413
Filed by KONINKLIJKE PHILIPS N.V., Eindhoven (NL)
PCT Filed Jul. 5, 2021, PCT No. PCT/EP2021/068432
§ 371(c)(1), (2) Date Mar. 11, 2022,
PCT Pub. No. WO2022/008397, PCT Pub. Date Jan. 13, 2022.
Claims priority of application No. 20184465 (EP), filed on Jul. 7, 2020.
Prior Publication US 2023/0117579 A1, Apr. 20, 2023
Int. Cl. A61B 6/00 (2024.01); A61B 6/08 (2006.01); A61B 6/42 (2024.01); A61B 6/58 (2024.01)
CPC A61B 6/4441 (2013.01) [A61B 6/08 (2013.01); A61B 6/4291 (2013.01); A61B 6/4405 (2013.01); A61B 6/582 (2013.01); A61B 6/587 (2013.01); A61B 6/588 (2013.01)] 10 Claims
OG exemplary drawing
 
1. A system for controlling X-ray imaging, comprising:
a memory for storing a plurality of instructions comprising a pre-trained machine learning model; and
an X-ray imaging apparatus comprising an X-ray source, an X-ray detector with no rigid mechanical coupling between the X-ray source and the X-ray detector, and controller circuitry;
wherein a processor is coupled to the memory and configured to execute the pre-trained machine learning model to compute output correction information for adjusting an imaging geometry of the X-ray imaging apparatus to achieve a target imaging geometry, wherein the controller circuitry is configured to provide a user instruction for the imaging geometry adjustment, modulated based on the output correction information, and wherein the processor is configured to execute the pre-trained machine learning model previously trained on training data including specific user's responses to previous user instructions for imaging geometry adjustments, and wherein multiple pre-trained or trainable machine learning models are associated with different users.