US 12,315,046 B2
Low radiation dose computed tomography perfusion (CTP) with improved quantitative analysis
Shiyu Xu, Mayfield Heights, OH (US); Hao Dang, Mayfield Heights, OH (US); and Chuanyong Bai, Solon, OH (US)
Assigned to KONINKLIJKE PHILIPS N.V., Eindhoven (NL)
Appl. No. 16/965,949
Filed by KONINKLIJKE PHILIPS N.V., Eindhoven (NL)
PCT Filed Jan. 30, 2019, PCT No. PCT/EP2019/052168
§ 371(c)(1), (2) Date Jul. 29, 2020,
PCT Pub. No. WO2019/149718, PCT Pub. Date Aug. 8, 2019.
Claims priority of provisional application 62/624,871, filed on Feb. 1, 2018.
Prior Publication US 2021/0085273 A1, Mar. 25, 2021
Int. Cl. G06K 9/00 (2022.01); A61B 6/00 (2006.01); A61B 6/03 (2006.01); G06T 11/00 (2006.01)
CPC G06T 11/008 (2013.01) [A61B 6/032 (2013.01); A61B 6/481 (2013.01); A61B 6/52 (2013.01); G06T 2210/41 (2013.01); G06T 2211/441 (2023.08); G06T 2211/444 (2023.08)] 14 Claims
OG exemplary drawing
 
1. A computed tomography scanner, comprising:
a radiation source configured to emit x-ray radiation;
a detector array configured to detect the x-ray radiation and generate a signal indicative thereof;
a reconstructor configured to reconstruct the signal and generate sequential sparse time line perfusion volumetric image data; and
a processor configured to process the sequential sparse time line perfusion volumetric image data using a trained neural network to produce sequential dense time line perfusion volumetric image data, wherein the processor trains the neural network with 1) training sets of sequential sparse time line perfusion volumetric image data, 2) training sets of sequential dense time line perfusion volumetric image data, and 3) training sets of perfusion parameters generated from the training sets of sequential dense time line perfusion volumetric image data.