System and method for performing tomographic image acquisition and reconstruction

Patent metadata

Field	Value
Publication number	US-10055861-B2
Application number	US-201615294533-A
Country	US
Kind code	B2
Filing date	Oct 14, 2016
Priority date	Jun 19, 2009
Publication date	Aug 21, 2018
Grant date	Aug 21, 2018

Abstract

Systems and methods for tomographic reconstruction of an image include systems and methods for producing images from k-space data. A k-space data set of an imaged object is acquired using know k-space data acquisition systems and methods. A portion of the k-space data set is sampled so as to collect some portion of the k-space data. An image is then reconstructed from the collected portion of the k-space data set according to a convex optimization model.

First claim

What is claimed is:

1. A method for producing images, comprising:
acquiring a k-space data set of an imaged object, the k-space data set generated by a magnetic resonance imaging system;
collecting a portion of the k-space data set by rotating a data collecting pattern around an axis in one or more trajectory planes of the data collecting pattern; and
reconstructing an image from the collected portion of the k-space data set according to a convex optimization model.

2. The method of claim 1 , wherein the convex optimization model includes a weighting factor representative of expected noise properties within the k-space data set.

3. The method of claim 1 , wherein the convex optimization model includes a weighting factor representative of a priori attributes of the imaged object.

4. The method of claim 1 , the collecting according to a perturbed data collecting pattern having a degree of incoherence incorporated into the data collecting pattern by introducing one or more pseudo-random shifts into one or more trajectory planes of the data collecting pattern.

5. The method of claim 1 , wherein the data collecting pattern includes a spiral pattern.

6. The method of claim 1 , wherein the data collecting pattern includes a radial pattern.

7. The method of claim 1 , wherein the data collecting pattern includes a pattern comprising a plurality of parallel sampling lines.

8. The method of claim 1 , wherein the reconstructing of the image according to the convex optimization model includes generating image data using an iterative process, the iterative process including updating a value of a norm weighting factor to prevent penalizing of discontinuities in the reconstructed image.

9. The method of claim 1 , wherein the generating of image data uses an approximation of an l=0 norm of a discretization of total variation of image intensities.

10. The method of claim 9 , wherein an iteration of the iterative process includes updating a value of a homotopic parameter and updating a value of a quadratic relaxation parameter.

11. The method of claim 10 , wherein respective values of the homotopic parameter and the quadratic relaxation parameter are fixed in relation to each other according to a predetermined relationship.

12. The method of claim 11 , wherein each iteration of the iterative process includes:
increasing the value of the quadratic relaxation parameter according to a predetermined rate, and
decreasing the value of the homotopic parameter according to the value of the quadratic relaxation parameter and the predetermined relationship between the quadratic relaxation parameter and the homotopic parameter.

13. The method of claim 10 , wherein said iterative process is an outer iterative process, and wherein each iteration of the outer iterative process includes one or more iterations of an inner iterative process.

14. The method of claim 13 , wherein each iteration of the inner iterative process includes updating a value of a relaxation variable based at least in part on the value of the homotopic parameter and the value of the quadratic relaxation parameter.

15. The method of claim 14 , wherein each iteration of the inner iterative process includes updating image data based at least in part on the value of the relaxation variable.

16. The method of claim 1 , wherein the generating of image data uses one of an l=1 norm of a discretization of total variation of image intensities and an l=2 norm of a discretization of total variation of image intensities.

17. The method of claim 1 , wherein the norm weighting factor is based at least in part on a smoothed image data.

18. The method of claim 17 , wherein the updating of the value of the norm weighting factor includes generating the smoothed image data using a Gaussian kernel.

19. The method of claim 1 , wherein said iterative process is an outer iterative process, wherein each iteration of the outer iterative process includes one or more iterations of an inner iterative process.

20. The method of claim 19 , wherein each iteration of the inner iterative process includes updating a value of a relaxation variable based at least in part on a value of a homotopic parameter and a value of a quadratic relaxation parameter.

21. The method of claim 20 , wherein each iteration of the inner iterative process includes updating image data based at least in part on the value of the relaxation variable.

22. The method of claim 1 , wherein the reconstructing of the image includes generating image data representative of the imaged object.

23. The method of claim 22 , wherein the reconstructing of the image includes outputting the image data to at least one of a display, a printer, and a memory device.

24. An system for producing images, comprising:
a memory for receiving and storing a k-space data set of an imaged object, the k-space data set generated by a magnetic resonance imaging system; and
a computing unit for collecting a portion of the k-space data set according to a data collecting pattern by rotating the data collecting pattern around an axis in the one or more trajectory planes and reconstructing an image from the collected portion of the k-space data set according to a convex optimization model.

25. The system of claim 24 , wherein the convex optimization model includes a weighting factor representative of expected noise properties within the k-space data set.

26. The system of claim 24 , wherein the convex optimization model includes a weighting factor representative of a priori attributes of the imaged object.

27. The system of claim 24 , wherein the generating of the image data by the computing unit uses an approximation of an l=0 norm of a discretization of total variation of image intensities.

28. The system of claim 27 , wherein an iteration of the iterative process includes updating a value of a homotopic parameter and updating a value of a quadratic relaxation parameter.

29. The system of claim 28 , wherein respective values of the homotopic parameter and the quadratic relaxation parameter are fixed in relation to each other according to a predetermined relationship.

30. The system of claim 24 , wherein the generating of the image data by the computing unit uses one of an l=1 norm of a discretization of total variation of image intensities and an l=2 norm of a discretization of total variation of image intensities.

31. The system of claim 24 , wherein the norm weighting factor is based at least in part on a smoothed image data.

32. The system of claim 24 , wherein the computing unit generates image data representative of the imaged object.

33. The system of claim 32 , wherein the computing unit outputs the image data to at least one of a display, a printer, and a memory device.

34. A computer program product comprising a non-transient, machine-readable medium storing instructions which, when executed by at least one programmable processor, cause the at least one programmable processor to perform operations comprising:
acquiring a k-space data set of an imaged object, the k-space data set generated by a magnetic resonance imaging system;
collecting a portion of the k-space data set by rotating a data collecting pattern around an axis in one or more trajectory planes of the data collecting pattern

Assignees

• Viewray Tech Inc

Inventors

• Dempsey James F
• Zeng Qingguo
• Nana Roger
• Patrick John Lester
• Eagan Timothy P
• Shvartsman Shmaryu M

Classifications

• G01R33/4826 Primary

  in three dimensions · CPC title

• G06T2211/424

  Iterative · CPC title

• G01R33/5608

  Data processing and visualization specially adapted for MR, e.g. for feature analysis and pattern recognition on the basis of measured MR data, segmentation of measured MR data, edge contour detection on the basis of measured MR data, for enhancing measured MR data in terms of signal-to-noise ratio by means of noise filtering or apodization, for enhancing measured MR data in terms of resolution by means for deblurring, windowing, zero filling, or generation of gray-scaled images, colour-coded images or images displaying vectors instead of pixels (image data processing or generation, in general G06T) · CPC title

• G01R33/5611

  Parallel magnetic resonance imaging, e.g. sensitivity encoding [SENSE], simultaneous acquisition of spatial harmonics [SMASH], unaliasing by Fourier encoding of the overlaps using the temporal dimension [UNFOLD], k-t-broad-use linear acquisition speed-up technique [k-t-BLAST], k-t-SENSE (structural details of arrays of sub-coils G01R33/3415) · CPC title

• G06T7/0012

  Biomedical image inspection · CPC title