SYSTEMS AND METHODS FOR TAILORED IMAGE TEXTURE IN ITERATIVE IMAGE RECONSTRUCTION

• US 20200134883A1
• Filed: 10/31/2018
• Published: 04/30/2020
• Est. Priority Date: 10/31/2018
• Status: Active Grant

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Nett, Brian Edward, Fan, Jiahua, Silvaggio, Amber Christine
• Granted Patent: US 11,270,477 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 17/15 Correlation function computation including computation of convolution operations arithmetic circuits for sum of products per se, e.g. multiply-accumulators G06F7/5443; digital filters, e.g. FIR, IIR, adaptive filters H03H17/00
• CPC Class Codes: G06T 11/003 Reconstruction from projections, e.g. tomography
• CPC Class Codes: G06T 11/006 Inverse problem, transformation from projection-space into object-space, e.g. transform methods, back-projection, algebraic methods
• CPC Class Codes: G06T 15/04 Texture mapping
• CPC Class Codes: G06T 15/503 Blending, e.g. for anti-aliasing
• CPC Class Codes: G06T 2211/421 Filtered back projection [FBP]

Abstract

Methods and systems are provided for reconstructing images with a tailored image texture. In one embodiment, a method comprises acquiring projection data, and reconstructing an image from the projection data with a desired image texture. In this way, iterative image reconstruction techniques may be used to substantially reduce image noise, thereby enabling a reduction in injected contrast and/or radiation dose, while preserving an image texture familiar from analytic image reconstruction techniques.

Claims

1. A method, comprising:

acquiring projection data; and

reconstructing, from the projection data, an image with a desired image texture.

2. The method of claim 1, further comprising:

reconstructing a first image from the projection data with a first image reconstruction algorithm;

reconstructing a second image from the projection data with a second image reconstruction algorithm; and

blending the first image and the second image to generate the image with the desired image texture.

3. The method of claim 2, further comprising transforming the first image and the second image into frequency space, wherein blending the first image and the second image comprises summing a weighted combination of the first image and the second image in frequency space.

4. The method of claim 3, further comprising multiplying a frequency representation of the first image with a weighted mask to generate a masked first image and multiplying a frequency representation of the second image with an inverted weighted mask to generate a masked second image, wherein summing a weighted combination of the first image and the second image in frequency space comprises summing the masked first image and the masked second image in frequency space.

5. The method of claim 4, further comprising calculating the weighted mask from a noise power spectrum, wherein the inverted weighted mask comprises an inversion of the weighted mask.

6. The method of claim 5, wherein calculating the weighted mask from the noise power spectrum comprises determining, with a trained neural network, a contribution of the first image at each spatial frequency to the image.

7. The method of claim 2, wherein blending the first image and the second image to generate the image with the desired image texture comprises applying convolutions to the first image and the second image, and summing the images in image space.

8. The method of claim 6, further comprising calculating the convolutions from a noise power spectrum of the first image.

9. The method of claim 2, wherein the first image reconstruction algorithm comprises an iterative image reconstruction algorithm and wherein the second image reconstruction algorithm comprises filtered back projection (FBP).

10. The method of claim 1, further comprising reconstructing, from the projection data with FBP, an FBP image, and inputting the FBP image into a trained neural network to generate the image with the desired image texture.

11. The method of claim 1, wherein reconstructing the image with the desired image texture comprises reconstructing, from the projection data with an iterative image reconstruction algorithm, the image, wherein the desired image texture is enforced during at least a subset of iterations during reconstruction.

12. The method of claim 1, further comprising receiving, from a user via a user interface, an indication of the desired image texture, the desired image texture comprising a desired distribution of image noise.

13. A method, comprising:

acquiring projection data of a subject;

iteratively reconstructing a first image from the projection data;

analytically reconstructing a second image from the projection data; and

generating an image with a desired image texture from the first image and the second image, the desired image texture of the image derived from the second image.

14. The method of claim 13, further comprising:

transforming the first image and the second image into frequency space;

multiplying the transformed first image with a first mask;

multiplying the transformed second image with a second mask;

summing the masked first image and the masked second image to generate a blended image; and

transforming the blended image into image space to generate the image with the desired image texture, the desired image texture comprising higher-frequency components of the second image.

15. The method of claim 14, wherein the first mask reduces higher-frequency components of the first image relative to lower-frequency components of the first image, and wherein the second mask comprises an inversion of the first mask.

16. A system, comprising:

an x-ray source that emits a beam of x-rays toward an object to be imaged;

a detector that receives the x-rays attenuated by the object;

a data acquisition system (DAS) operably connected to the detector; and

a computing device operably connected to the DAS and configured with executable instructions in non-transitory memory that when executed cause the computing device to;

acquire, via the DAS, projection data of the object; and

reconstruct, from the projection data, an image with a desired image texture.

17. The system of claim 16, wherein the computing device is further configured with executable instructions that when executed cause the computing device to:

reconstruct a first image from the projection data with a first image reconstruction algorithm;

reconstruct a second image from the projection data with a second image reconstruction algorithm; and

blend the first image and the second image to generate the image with the desired image texture.

18. The system of claim 17, wherein the computing device is further configured with executable instructions that when executed cause the computing device to:

multiply the first image by a first mask in frequency space to generate a first masked image; and

multiply the second image by a second mask in frequency space to generate a second masked image, wherein blending the first image and the second image comprises summing the first masked image and the second masked image in frequency space to generate a blended image, and transforming the blended image into image space to generate the image with the desired image texture.

19. The system of claim 18, wherein the first image reconstruction algorithm comprises iterative image reconstruction algorithm, the second image reconstruction algorithm comprises filtered back projection, the first mask weights lower-frequency components substantially higher relative to higher-frequency components, and the second mask comprises an inversion of the first mask such that the blended image substantially comprises lower-frequency components of the first image and higher-frequency components of the second image, wherein the higher-frequency components of the second image provide the desired image texture.

20. The system of claim 16, further comprising an operator console communicatively coupled to the computing device, wherein the computing device is further configured with executable instructions that when executed cause the computing device to receive, from an operator via the operator console, an indication of the desired image texture.