US 12,433,562 B2
Scan parameter adaption during a contrast enhanced scan
Michael Grass, Buchholz in der Nordheide (DE); Rolf Dieter Bippus, Hamburg (DE); Thomas Koehler, Norderstedt (DE); and Sandra Simon Halliburton, Shaker Heights, OH (US)
Assigned to KONINKLIJKE PHILIPS N.V., Eindhoven (NL)
Appl. No. 16/963,270
Filed by KONINKLIJKE PHILIPS N.V., Eindhoven (NL)
PCT Filed Jan. 17, 2019, PCT No. PCT/EP2019/051136
§ 371(c)(1), (2) Date Jul. 20, 2020,
PCT Pub. No. WO2019/141769, PCT Pub. Date Jul. 25, 2019.
Claims priority of provisional application 62/619,216, filed on Jan. 19, 2018.
Prior Publication US 2020/0337668 A1, Oct. 29, 2020
Int. Cl. A61B 6/00 (2024.01); A61B 6/03 (2006.01); A61B 6/04 (2006.01); A61B 6/42 (2024.01); A61B 6/46 (2024.01); A61B 6/50 (2024.01)
CPC A61B 6/545 (2013.01) [A61B 6/032 (2013.01); A61B 6/0407 (2013.01); A61B 6/4233 (2013.01); A61B 6/467 (2013.01); A61B 6/504 (2013.01); A61B 6/481 (2013.01); A61B 6/482 (2013.01); A61B 6/488 (2013.01)] 12 Claims
OG exemplary drawing
 
1. A computed tomography system for non-spectral and spectral imaging, comprising:
an X-ray radiation source configured to emit X-ray radiation that traverses an examination region;
an X-ray radiation sensitive detector array configured to detect X-ray radiation traversing the examination region and generate a view of line integrals;
a subject support table top configured to translate in the examination region for a scan based on at least one scan parameter, wherein the at least one scan parameter is a speed of the subject support table top; and
at least one processor configured to:
determine a contrast agent concentration from the view of line integrals;
adjust the at least one scan parameter based on the determined concentration;
forward project pre-scan volumetric image data to estimate a view of line integrals corresponding to a same angular position as the generated view of line integrals; and
adjust the at least one scan parameter based on a difference between the estimated and generated views of line integrals; and
a neural network configured to adjust the at least one scan parameter, wherein the neural network is trained with a machine or deep learning algorithm to adapt the speed of the subject support table top during a scan based on the contrast agent concentration.