System and method for performing tomographic image acquisition and reconstruction

Patent metadata

Field	Value
Publication number	US-10825209-B2
Application number	US-201816105848-A
Country	US
Kind code	B2
Filing date	Aug 20, 2018
Priority date	Jun 19, 2009
Publication date	Nov 3, 2020
Grant date	Nov 3, 2020

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 non-transitory computer-readable medium containing instructions that configure a processor to perform operations comprising:
acquiring a k-space data set of an imaged object, the k-space data set generated by a tomographic 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.

2. The non-transitory computer-readable medium of claim 1 , wherein the data collecting pattern includes a sampling line having a plurality of sampling points, and the plurality of sampling points each have a weighting factor.

3. The non-transitory computer-readable medium of claim 2 , wherein the weighting factor is higher at the sampling points near a center of the k-space data set.

4. The non-transitory computer-readable medium of claim 2 , wherein the weighting factor is higher at the sampling points near expected ridges in the k-space data set.

5. The non-transitory computer-readable medium of claim 1 , wherein the reconstructing of the image is performed with a model that includes a weighting factor representative of a priori attributes of the imaged object.

6. A non-transitory computer-readable medium containing instructions that configure a processor to perform operations comprising:
acquiring a k-space data set of an imaged object, the k-space data set generated by at least one of a magnetic resonance imaging system, a computed tomography system, or a positron emission tomography 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.

7. The non-transitory computer-readable mediumof claim 6 , wherein the data collecting pattern includes a sampling line having a plurality of sampling points, and the plurality of sampling points each have a weighting factor.

8. The non-transitory computer-readable medium of claim 7 , wherein the weighting factor is higher at the sampling points near a center of the k-space data set.

9. The non-transitory computer-readable medium of claim 7 , wherein the weighting factor is higher at the sampling points near expected ridges in the k-space data set.

10. The non-transitory computer-readable medium of claim 6 , wherein the reconstructing of the image is performed with a model that includes a weighting factor representative of a priori attributes of the imaged object.

11. A non-transitory computer-readable medium containing instructions that configure a processor to perform operations comprising:
acquiring a k-space data set of an imaged object, the k-space data set generated by a tomographic imaging system;
collecting a portion of the k-space data set by rotating a data collecting pattern to sample a 3-D k-space; and
reconstructing an image from the collected portion of the k-space data set.

12. The non-transitory computer-readable medium of claim 11 , wherein the data collecting pattern includes a sampling line having a plurality of sampling points, and the plurality of sampling points each have a weighting factor.

13. The non-transitory computer-readable medium of claim 12 , wherein the weighting factor is higher at the sampling points near a center of the k-space data set.

14. The non-transitory computer-readable medium of claim 12 , wherein the weighting factor is higher at the sampling points near expected ridges in the k-space data set.

15. The non-transitory computer-readable medium of claim 11 , wherein the reconstructing of the image is performed with a model that includes a weighting factor representative of a priori attributes of the imaged object.

16. The non-transitory computer-readable medium of claim 11 , wherein the reconstructing of the image is performed with a model that includes updating a value of a norm weighting factor preventing penalizing of discontinuities in the reconstructed image.

17. The non-transitory computer-readable medium of claim 11 , wherein the reconstructing of the image is performed with a model that includes a weighting factor representative of expected noise properties within the k-space data set.

18. The non-transitory computer-readable medium of claim 11 , wherein the collecting comprising perturbing a portion of the data collecting pattern in or out of a sampling plane.

19. The non-transitory computer-readable mediumof claim 11 , the collecting comprising introducing a pseudo-random shift into one or more trajectories 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

• G06T12/20 Primary

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

• G06T7/0012

  Biomedical image inspection · 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

• 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

• G01R33/4826 Primary

  in three dimensions · CPC title