Method and device for reconstructing CT image and storage medium

Patent metadata

Field	Value
Publication number	US-10896527-B2
Application number	US-201816045303-A
Country	US
Kind code	B2
Filing date	Jul 25, 2018
Priority date	Jul 25, 2017
Publication date	Jan 19, 2021
Grant date	Jan 19, 2021

Abstract

A method and device for reconstructing a CT image and a storage medium are disclosed. CT scanning is performed on an object to be inspected to obtain projection data. The projection data is processed using a first convolutional neural network to obtain processed projection data. The first convolutional neural network comprises a plurality of convolutional layers. A back-projection operation is performed on the processed projection data to obtain a reconstructed image.

First claim

We claim:

1. A method for reconstructing a Computed Tomography (CT) image, comprising:
performing CT scanning on an object to be inspected to obtain projection data;
processing the projection data by using a first convolutional neural network to obtain processed projection data, wherein the first convolutional neural network comprises a plurality of convolutional layers;
performing a back-projection operation on the processed projection data to obtain a reconstructed image; and
processing the reconstructed image by using a second convolutional neural network to obtain a resultant image.

2. The method according to claim 1 , wherein the CT scanning is one of the following: detector under-sampling scanning, sparse-angle scanning, intra-reconstruction scanning, finite angle scanning, and linear trajectory scanning, and the first convolutional neural network is a pooling layer-free convolutional neural network.

3. The method according to claim 1 , wherein the CT scanning is circular scanning or helical scanning, and the first convolutional neural network further comprises a plurality of pooling layers disposed after respective convolutional layers, and a fully connected layer.

4. The method according to claim 1 , further comprising a step of:
before processing the projection data by using a first convolutional neural network, filtering the projection data by using a ramp filter.

5. The method according to claim 1 , wherein the reconstructed image is locally smoothed by using the second convolutional neural network to obtain the resultant image.

6. The method according to claim 1 , wherein a convolutional kernel of a convolutional layer in the first convolutional neural network has a dimension of a detector pixel sequence and another dimension of a scanning angle, and a scale of the convolutional kernel of the convolutional layer in the first convolutional neural network on the dimension of the detector pixel sequence is set independently from a scale of the convolutional kernel of the convolutional layer in the first convolutional neural network on the dimension of the scanning angle.

7. The method according to claim 6 , wherein the scale of the convolutional kernel of the convolutional layer in the first convolutional neural network on the dimension of the detector pixel sequence is greater than the scale of the convolutional kernel of the convolutional layer in the first convolutional neural network on the dimension of the scanning angle.

8. The method according to claim 1 , wherein the first convolutional neural network comprises at least three convolutional layers, each of which has an activation function for performing a non-linear operation on projection data processed by the convolutional layer.

9. The method according to claim 1 , wherein the first convolutional neural network further comprises a back-projection layer for performing a back-projection operation on projection data processed by the convolutional layer.

10. The method according to claim 9 , wherein a length-width size parameter of a convolutional kernel of a convolutional layer in the first convolutional neural network which is closest to the back-projection layer is 1*1.

11. The method according to claim 1 , wherein the second convolutional neural network comprises an image domain initial convolutional layer and an end convolutional layer for processing the reconstructed image in an image domain.

12. The method according to claim 11 , wherein each convolutional layer included in the image domain initial convolutional layer has an activation function, and the end convolutional layer has no activation function.

13. A device for reconstructing a Computed Tomography (CT) image, comprising:
a CT scanning apparatus configured to perform CT scanning on an object to be inspected to obtain projection data;
a processor configured to:
process projection data by using a first convolutional neural network to obtain processed projection data, and perform a back-projection operation on the processed projection data to obtain a reconstructed image, wherein the first convolutional neural network comprises a plurality of convolutional layers,
process the reconstructed image by using a second convolutional neural network to obtain a resultant image.

14. The device according to claim 13 , wherein the CT scanning apparatus is configured to perform one of the following: detector under-sampling scanning, sparse-angle scanning, intra-reconstruction scanning, finite angle scanning, and linear trajectory scanning, and the first convolutional neural network is a pooling layer-free convolutional neural network.

15. The device according to claim 13 , wherein the CT scanning apparatus is configured to perform circular scanning or helical scanning, and the first convolutional neural network further comprises a plurality of pooling layers disposed after respective convolutional layers, and a fully connected layer.

16. The device according to claim 15 , wherein the processor is further configured to:
locally smooth the reconstructed image by using the second convolutional neural network to obtain the resultant image.

17. A non-transitory computer-readable medium having computer programs stored thereon, which when being executed by a processor, cause the processor to perform the following steps:
processing projection data by using a first convolutional neural network to obtain processed projection data, wherein the projection data is obtained by performing CT scanning on an object to be inspected, and the first convolutional neural network comprises a plurality of convolutional layers;
performing a back-projection operation on the processed projection data to obtain a reconstructed image; and
processing the reconstructed image by using a second convolutional neural network to obtain a resultant image.

Assignees

• Univ Tsinghua
• Nuctech Co Ltd

Inventors

• Xing Yuxiang
• Zhang Li
• Li Jianmin
• Chen Zhiqiang
• Gu Jianping

Classifications

• G06T12/20 Primary

  Inverse problem, i.e. transformations from projection space into object space · CPC title

• G06T12/10 Primary

  Image preprocessing, e.g. calibration, positioning of sources or scatter correction · CPC title

• G06N3/045

  Combinations of networks · CPC title

• G06N3/08

  Learning methods · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title