METHOD AND SYSTEM FOR NOISE REDUCTION IN LOW DOSE COMPUTED TOMOGRAPHY

• US 20130089252A1
• Filed: 04/27/2011
• Published: 04/11/2013
• Est. Priority Date: 06/21/2010
• Status: Active Grant

• Current Assignee: Koninklijke Philips N.V.
• Original Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Inventors: Shechter, Gilad
• Granted Patent: US 9,189,832 B2
• Time in Patent Office: Days
• US Class Current: 382/131
• CPC Class Codes: G06T 11/005 Specific pre-processing for tomographic reconstruction, e.g. calibration, source positioning, rebinning, scatter correction, retrospective gating
• CPC Class Codes: G06T 11/008 Specific post-processing after tomographic reconstruction, e.g. voxelisation, metal artifact correction
• CPC Class Codes: G06T 2207/10081 Computed x-ray tomography [CT]
• CPC Class Codes: G06T 2207/30004 Biomedical image processing
• CPC Class Codes: G06T 5/70 Denoising; Smoothing

Abstract

A method includes de-noising projection data from a lower dose scan in the projection domain, reconstructing the de-noised projection data to generate volumetric image data, and de-noising the volumetric image data in the image domain. A system includes a projection domain processor (116) that de-noises projection data from a lower dose scan, a reconstructor (118) that reconstructs the de-noised projection data and generates image data, and an image domain processor (120) that de-noises the image data such that a noise level of the de-noise image data is substantially the same as an estimated image data noise level of image data for a higher dose scan.

Claims

1. A method, comprising:

de-noising projection data from a lower dose scan in the projection domain, wherein the lower dose scan corresponds to a scan which produces image data with a noise level that is higher than a predetermined target image data noise level;

reconstructing the de-noised projection data to generate volumetric image data; and

de-noising the volumetric image data in the image domain.

2. The method of claim 1, wherein the de-noised image data has a noise level substantially the same as the predetermined target image data noise level.

3. The method of claim 1, wherein the lower dose scan has an effective dose of sub-mSv and the higher dose scan has an effective dose of ten mSv or higher.

4. The method of claim 1, further comprising:

adaptively de-noising the projection data based on a local noise variance and a kcal variation.

5. The method of claim 4, wherein projection data having a lower local noise variance and a higher local variation are de-noised less than projection data having a higher local noise variance and a lower local variation are de-noised.

6. The method of claim 1, further comprising:

determining a noise level of the image data;

estimating a noise level for the image data of the higher dose scan; and

de-noising the image data based on the determined and estimated noise levels.

7. The method of claim 6, further comprising:

determining a ratio of the noise level of the higher dose scan to the noise level of the image of the lower dose scan; and

de-noising the image data based on the ratio.

8. The method of claim 1, wherein the de-noising in the projection domain is non-iterative de-noising.

9. The method of claim 1, wherein the de-noising in the projection domain mitigates blurring of image details.

10. A system, comprising:

a projection domain processor that de-noises projection data from a lower dose scan;

a reconstructor that reconstructs the de-noised projection data and generates image data; and

an image domain processor that de-noises the image data such that a noise level of the de-noise image data is substantially the same as an estimated image data noise level of image data for a higher dose scan.

11. The system of claim 10, further comprising:

a first parameter determiner that determines a noise variance of the projection data; and

a second parameter determiner determines a variation of the projection data, wherein the projection data is de-noised based on the noise variance and the variation.

12. The system of claim 11, wherein the variation is estimated based on a gradient scalar product.

13. The system of claim 11, further comprising:

a weight determiner that determines adaptive weights based on the noise variance and the variation.

14. The system of claim 13, further comprising:

a projection domain de-noiser that employs the weights to de-noise the projection data.

15. The system of claim 14, wherein the adaptive weights de-noise projection data having a lower local noise variance and a higher local variation less than projection data having a higher local noise variance and a lower local variation.

16. The system of claim 15, wherein the local aggressiveness of the de-noising that is based on the local noise variance and on the local variation varies strongly between different regions in the projection domain.

17. The system of claim 10, further comprising:

a de-noising factor generator that determines the decrease in noise variance of the projection data for the lower dose scan due to de-noising;

a low-dose scan projection domain noise variance estimator that estimates a noise variance of the projection data for the lower dose scan before the de-noising;

a projection variance estimator that estimates a projection variance for the lower dose scan based on the noise variance determined by the noise variance estimator and the de-noising factor determined by the de-noising factor determiner;

a high-dose scan projection domain projection noise variance estimator that estimates a projection variance for the higher dose scan;

an image noise estimator that estimates the image noise variance for the projection de-noised lower dose scan and for the higher dose scan; and

an image domain de-noiser de-noises the image data based on the image noise variances for the lower and higher dose scans.

18. The system of claim 17, wherein the de-noising factor takes into account detector crosstalk.

19. The system of claim 17, wherein the de-noising factor is generated for a subset of projections.

20. A computer readable storage medium encoded with computer executable instructions, which, when executed by a processor of a computer, cause the processor to:

employ projection domain and image domain de-noising to produce image data from a lower dose scan that has a noise level substantially the same as a noise level of a higher dose scan.