SYSTEMS AND METHODS FOR DEBLURRING DATA CORRUPTED BY SHIFT VARIANT BLURRING

• US 20110064326A1
• Filed: 11/19/2010
• Published: 03/17/2011
• Est. Priority Date: 01/09/2007
• Status: Active Grant

• Current Assignee: University of Utah Research Foundation (State of Utah)
• Original Assignee: University of Utah Research Foundation (State of Utah)
• Inventors: ZENG, Gengsheng Lawrence
• Granted Patent: US 8,218,889 B2
• Time in Patent Office: Days
• US Class Current: 382/255
• CPC Class Codes: G06T 2207/10104 Positron emission tomography [PET]
• CPC Class Codes: G06T 2207/10108 Single photon emission computed tomography [SPECT]
• CPC Class Codes: G06T 2207/30004 Biomedical image processing
• CPC Class Codes: G06T 5/73 Deblurring; Sharpening

Abstract

The present invention provides methods, systems and machine readable medium including machine readable code for deblurring data corrupted by shift variant blurring. A first version of data having shift variant blurring characterized by a first shift variant point spread function is provided. A target shift invariant point spread function is selected. A second shift variant point spread function is derived wherein a combination of the first and second shift variant point spread functions generates the target shift invariant point spread function. The second shift variant point spread function is applied to the first version of the data thereby generating a second version of the data having shift invariant blurring characterized by the target shift invariant point spread function. A linear shift invariant filter is applied to the second version of the data thereby generating a deblurred version of the data.

Claims

1. A method of deblurring data corrupted by shift variant blurring, the method comprising:

providing a first version of data having shift variant blurring characterized by a first shift variant point spread function;

applying a second shift variant point spread function to the first version of the data, thereby generating a second version of the data, wherein the second version of the data is a blurrier version of the data than is the first version of the data; and

applying a linear shift invariant filter to the second version of the data to generate a deblurred version of the data.

2. The method of claim 1, wherein the first version of data comprises a three dimensional image, the method further comprising:

determining a collimator specific point spread function characterizing a first component of the shift variant blurring attributable to a collimator specific blurring effect in an image domain;

determining a target specific scattering point spread function characterizing a second component of the shift variant blurring attributable to a target specific scattering effect in an image domain; and

deriving the first variant point spread function based on the collimator specific point spread function and the target specific point spread function.

3. The method of claim 2, wherein determining a collimator specific point spread function in the image domain comprises determining the collimator specific point spread function based on collimator data selected from a group consisting of manufacturer supplied collimator specification data, collimator projection data, and collimator detector point spread function data.

4. The method of claim 2, wherein determining a collimator specific point spread function in the image domain comprises determining a collimator specific point spread function using a reconstruction algorithm selected from a group consisting of a filtered backprojection (FBP) reconstruction algorithm and an iterative reconstruction algorithm.

5. The method of claim 1, further comprising:

selecting a target shift invariant point spread function; and

deriving the second shift variant point spread function, wherein a combination of the first and second shift variant point spread functions generates the target shift invariant point spread function.

6. The method of claim 5, wherein selecting a target shift invariant point spread function comprises selecting a target shift invariant point spread function from a group consisting of a point spread function of a blurriest section of the first version of the data, a point spread function that exceeds a point spread function of blurriest section of the first version of the data, and a point spread function of a relatively blurry section of the first version of the data.

7. The method of claim 5, wherein deriving the second shift variant point spread function comprises:

determining a Fourier transform of the target shift invariant point spread function;

determining a Fourier transform of the first shift variant point spread function;

generating a Fourier transform of the second shift variant point spread function by dividing the Fourier transform of the target shift invariant point spread function by the Fourier transform of the first shift variant point spread function; and

determining the inverse Fourier transform of the second shift variant point spread function thereby generating the second shift variant point spread function.

8. The method of claim 1, wherein deriving the second shift variant point spread function comprises:

determining a variance of the target shift invariant point spread function;

determining a variance of the first shift variant point spread function;

generating a variance of the second shift variant point spread function by subtracting the variance of the first shift variant point spread function from the variance of the target shift invariant point spread function; and

deriving the second shift variant point spread function from the variance of the second shift variant point spread function.

9. The method of claim 1, wherein applying the second shift variant point spread function to the first version of the data comprises applying the second shift variant point spread function to the first version of the data using a rotational convolution method.

10. The method of claim 1, wherein applying a linear shift invariant filter to the second version of the data comprises applying a linear shift invariant filter selected from a group consisting of a non-iterative filter, an iterative filter, a convolution procedure based filter, a frequency domain multiplication procedure based filter, and a fast Fourier domain de-blurring filter.

11. A machine readable medium for storing a machine executable program for deblurring data corrupted by shift variant blurring, comprising:

machine readable code for providing a first version of data having shift variant blurring characterized by a first shift variant point spread function;

machine readable code for applying a second shift variant point spread function to the first version of the data thereby generating a second version of the data, wherein the second version of the data is a blurrier version of the data than the first version of the data; and

machine readable code for applying a linear shift invariant filter to the second version of the data thereby generating a deblurred version of the data.

19. The machine readable medium of claim 11, wherein the machine readable code for applying the second shift variant point spread function to the first version of the data comprises machine readable code for applying the second shift variant point spread function to the first version of the data using a rotational convolution method.

20. The machine readable medium of claim 11, wherein the machine readable code for applying a linear shift invariant filter to the second version of the data comprises machine readable code for applying a linear shift invariant filter selected from a group consisting of a non-iterative filter, an iterative filter, a convolution procedure based filter, a frequency domain multiplication procedure based filter, and a fast Fourier domain de-blurring filter.

15. The machine readable medium, further comprising:

machine readable code for selecting a target shift invariant point spread function; and

machine readable code for deriving the second shift variant point spread function, wherein a combination of the first and second shift variant point spread functions generates the target shift invariant point spread function;

12. The machine readable medium of claim 15, wherein the first version of data comprises a three dimensional image, the machine readable medium further comprising:

machine readable code for determining a collimator specific point spread function characterizing a first component of the shift variant blurring attributable to a collimator specific blurring effect in an image domain;

machine readable code for determining a target specific scattering point spread function characterizing a second component of the shift variant blurring attributable to a target specific scattering effect in an image domain; and

machine readable code for deriving the first variant point spread function based on the collimator specific point spread function and the target specific point spread function.

13. The machine readable medium of claim 12, wherein the machine readable code for determining a collimator specific point spread function in the image domain comprises machine readable code for determining the collimator specific point spread function based on collimator data selected from a group consisting of manufacturer supplied collimator specification data, collimator projection data, and collimator detector point spread function data.

14. The machine readable medium of claim 12, wherein the machine readable code for determining a collimator specific point spread function in the image domain comprises machine readable code for determining a collimator specific point spread function using Novikov'"'"'s filtered backprojection (FBP) algorithm.

16. The machine readable medium of claim 15, wherein the machine readable code for selecting a target shift invariant point spread function comprises machine readable code for selecting a target shift invariant point spread function from a group consisting of a point spread function of a blurriest section of the first version of the data, a point spread function that exceeds a point spread function of a blurriest section of the first version of the data, and a point spread function of a relatively blurry section of the first version of the data.

17. The machine readable medium of claim 15, wherein the machine readable code for deriving the second shift variant point spread function comprises:

machine readable code for determining a Fourier transform of the target shift invariant point spread function;

machine readable code for determining Fourier transform of the first shift variant point spread function;

machine readable code for generating a Fourier transform of the second shift variant point spread function by dividing the Fourier transform of the target shift invariant point spread function by the Fourier transform of the first shift variant point spread function; and

machine readable code for determining the inverse Fourier transform of the second shift variant point spread function thereby generating the second shift variant point spread function.

18. The machine readable medium of claim 15, wherein the machine readable code for deriving the second shift variant point spread function comprises:

machine readable code for determining a variance of the target shift invariant point spread function;

machine readable code for determining a variance of the first shift variant point spread function;

machine readable code for generating a variance of the second shift variant point spread function by subtracting the variance of the first shift variant point spread function from the variance of the target shift invariant point spread function; and

machine readable code for deriving the second shift variant point spread function from the variance of the second shift variant point spread function.