Non-spectral computed tomography (CT) scanner configured to generate spectral volumetric image data

Patent metadata

Field	Value
Publication number	US-11510641-B2
Application number	US-201916965201-A
Country	US
Kind code	B2
Filing date	Jan 30, 2019
Priority date	Jan 31, 2018
Publication date	Nov 29, 2022
Grant date	Nov 29, 2022

Abstract

A non-spectral computed tomography scanner (102) includes a radiation source (112) configured to emit x-ray radiation, a detector array (114) configured to detect x-ray radiation and generate non-spectral data, and a memory (134) configured to store a spectral image module (130) that includes computer executable instructions including a neural network trained to produce spectral volumetric image data. The neural network is trained with training spectral volumetric image data and training non-spectral data. The non-spectral computed tomography scanner further includes a processor (126) configured to process the non-spectral data with the trained neural network to produce spectral volumetric image data.

First claim

The invention claimed is:

1. A non-spectral 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 non-spectral data;
a memory configured to store a spectral image module that includes a neural network trained with training spectral volumetric image data and training non-spectral data; and
a processor configured to process the non-spectral data with the trained neural network to produce spectral volumetric image data.

2. The scanner of claim 1 , wherein the neural network is further trained with scanner geometry and physics information.

3. The scanner of claim 1 , wherein the non-spectral data includes non-spectral projection data, and the processor is configured to process the non-spectral projection data using the trained neural network to produce the spectral volumetric image data.

4. The scanner of claim 3 , wherein the training non-spectral data includes training non-spectral projection data.

5. The scanner of claim 4 , wherein the non-spectral data includes non-spectral volumetric image data, and the processor is configured to process the non-spectral volumetric image data using the trained neural network to produce the spectral volumetric image data.

6. The scanner of claim 1 , wherein the training non-spectral data includes training non-spectral volumetric image data.

7. The scanner of claim 6 , wherein the non-spectral volumetric image data is uncorrected spectral volumetric image data.

8. The scanner of claim 6 , wherein the non-spectral volumetric image data is partially corrected spectral volumetric image data, which is not corrected at least for beam hardening and scatter radiation.

9. The scanner of claim 4 , wherein the neural network is trained to minimize a difference between the spectral volumetric image data generated by the neural network and the training non-spectral data.

10. The scanner of claim 6 , wherein the non-spectral volumetric image data is corrected spectral volumetric image data.

11. A non-spectral 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 non-spectral data;
a memory configured to store a spectral image module that includes a neural network trained; and
a processor configured to train the neural network with training spectral volumetric image data and training non-spectral data to generate spectral volumetric image data from the non-spectral data.

12. The scanner of claim 11 , wherein the training non-spectral data includes training non-spectral projection data.

13. The scanner of claim 12 , wherein the neural network is trained to minimize a difference between the spectral volumetric image data generated by the neural network and the training non-spectral projection data.

14. The scanner of claim 12 , wherein the non-spectral data includes non-spectral projection data, and the processor is further configured to process the non-spectral projection data with the trained neural network to produce spectral data.

15. The scanner of claim 11 , wherein the training non-spectral data includes training non-spectral volumetric image data.

16. The scanner of claim 15 , wherein the neural network is trained to minimize a difference between the spectral volumetric image data generated by the neural network and the training non-spectral volumetric image data.

17. A non-transitory computer readable storage medium encoded with computer readable instructions, which, when executed by a processor of a computing system, cause the processor to:
emit x-ray radiation with a radiation source;
detect the emitted x-ray radiation and generate a signal indicative of the emitted x-ray radiation;
reconstruct the signal and generate non-spectral volumetric image data; and
deploy a neural network trained to produce spectral volumetric image data from the generated non-spectral volumetric image data.

18. The non-transitory computer readable storage medium of claim 17 , wherein the generated non-spectral volumetric image data is obtained from non-spectral projection data generated by a non-spectral computed tomography scanner.

19. A method used in a non-spectral computed tomography scanner, the method comprising:
emitting x-ray radiation;
detecting the emitted x-ray radiation;
generating non-spectral data indicative of the emitted x-ray radiation;
storing a spectral image module that includes a neural network trained with training spectral volumetric image data and training non-spectral data; and
processing the non-spectral data with the trained neural network to produce spectral volumetric image data.

Assignees

• Koninklijke Philips Nv

Inventors

• Bai Chuanyong
• Zhu Yang-Ming
• Lu Sheng
• Xu Shiyu
• Dang Hao
• Lai Hao
• Mcknight Douglas B
• Wang Hui

Classifications

• A61B6/032 Primary

  Transmission computed tomography [CT] · CPC title

• A61B6/482

  involving multiple energy imaging · CPC title

• A61B6/52 Primary

  Devices using data or image processing specially adapted for radiation diagnosis · CPC title

• G06N3/08

  Learning methods · CPC title

• G16H30/40

  for processing medical images, e.g. editing · CPC title