Phase estimation for retrospective motion correction

Patent metadata

Field	Value
Publication number	US-11486953-B2
Application number	US-202117465014-A
Country	US
Kind code	B2
Filing date	Sep 2, 2021
Priority date	Sep 3, 2020
Publication date	Nov 1, 2022
Grant date	Nov 1, 2022

Abstract

Techniques are disclosed related to the compensation of phase variations introduced into k-space lines, which cause imaging artifacts. The techniques utilize the detection of motion via an encoding plus motion model, which does not require the use of additional prospective or retrospective motion detection techniques. The techniques described herein use the encoding plus motion model to reconstruct an initial image from a set of motion states, and then calculate phase information from images that are projected form the initial reconstructed image using a projection onto convex sets (POCS). The phase information is incorporated into the encoding plus motion model over several iterations to minimize data consistency error, thereby generating a refined image that compensates for patient motion over the set of motion states.

First claim

What is claimed is:

1. A method for acquiring clinical images of an object that is moving during at least a portion of a magnetic resonance imaging (MRI) scan, comprising:
generating, via one or more processors using a SENSitivity Encoding (SENSE) plus motion model reconstruction, an initial image of the object using k-space data and motion parameters that are acquired over a plurality of motion states of the object;
performing, via one or more processors, a projection onto convex sets (POCS) reconstruction of the initial image to generate a plurality of projection images such that each respective one of the plurality of projection images is associated with a projection of the initial image onto each respective one of the plurality of motion states motion states;
calculating, via one or more processors, a refined image from each one of the plurality of projection images using the SENSE plus motion model that further incorporates a calculated phase difference map for each motion state corresponding to each respective one of the plurality of projection images;
iteratively repeating (i) performing the POCS reconstruction from refined images calculated in a previous iteration to calculate a plurality of projection images, and (ii) calculating a further refined image from the plurality of projection images until a data consistency error improvement of the SENSE plus motion model is less than a predetermined threshold value; and
storing, in a data storage, a calculated refined image corresponding to an iteration in which the data consistency error improvement is less than the predetermined threshold value.

2. The method of claim 1 , wherein the act of generating the initial image comprises optimizing over the data consistency error of the SENSE plus motion model.

3. The method of claim 1 , wherein the plurality of projection images are calculated by (i) evaluating the SENSE plus motion model using the refined image and the motion parameters to calculate model-generated k-space data, and (ii) replacing a portion of the model-generated k-space data with k-space data acquired during each respective one of the plurality of motion states to generate modified model-generated k-space data.

4. The method of claim 3 , wherein the plurality of projection images are calculated by applying a Hermitian matrix to the SENSE plus motion model to transform from a modified model-generated k-space data domain to an image domain.

5. The method of claim 1 , further comprising:
calculating, via one or more processors, the phase difference map of each respective one of the plurality of projection images using a low rank matrix completion algorithm.

6. The method of claim 5 , wherein the low rank matrix completion algorithm comprises a parallel imaging using eigenvector maps Iterative Self-consistent Parallel Imaging Reconstruction from Arbitrary k-Space (ESPIRiT).

7. The method of claim 1 , wherein k-space data acquired for each respective motion state represents a homogenous distribution of the k-space data across k-space.

8. The method of claim 1 , further comprising:
acquiring the motion parameters for each respective motion state using a non-imaging echo having an echo time (TE) that is less than a TE associated with one or more imaging echoes.

9. The method of claim 8 , wherein the non-imaging echo time (TE) is less than 3 milliseconds.

10. The method of claim 1 , further comprising:
acquiring the motion parameters for each respective motion state using an imaging echo having an echo time (TE) that is less than 10 milliseconds.

11. A non-transitory computer readable medium having instructions stored thereon that, when executed by one or more processors of a magnetic resonance apparatus, cause the magnetic resonance apparatus to acquire clinical images of an object that is moving during at least a portion of a magnetic resonance imaging (MRI) scan by:
generate, using a SENSitivity Encoding (SENSE) plus motion model reconstruction, an initial image of the object using k-space data and motion parameters that are acquired over a plurality of motion states of the object;
perform a projection onto convex sets (POCS) reconstruction of the initial image to generate a plurality of projection images such that each respective one of the plurality of projection images is associated with a projection of the initial image onto each respective one of the plurality of motion states motion states;
calculate a refined image from each one of the plurality of projection images using the SENSE plus motion model that further incorporates a calculated phase difference map for each motion state corresponding to each respective one of the plurality of projection images;
iteratively repeat (i) performing the POCS reconstruction from refined images calculated in a previous iteration to calculate a plurality of projection images, and (ii) calculating a further refined image from the plurality of projection images until a data consistency error improvement of the SENSE plus motion model is less than a predetermined threshold value; and
store a calculated refined image corresponding to an iteration in which the data consistency error improvement is less than the predetermined threshold value.

12. The non-transitory computer readable medium of claim 11 , wherein the instructions cause the one or more processors of the magnetic resonance apparatus to generate the initial image by optimizing over the data consistency error of the SENSE plus motion model.

13. The non-transitory computer readable medium of claim 11 , wherein the instructions cause the one or more processors of the magnetic resonance apparatus to calculate the plurality of projection images by (i) evaluating the SENSE plus motion model using the refined image and the motion parameters to calculate model-generated k-space data, and (ii) replacing a portion of the model-generated k-space data with k-space data acquired during each respective one of the plurality of motion states to generate modified model-generated k-space data.

14. The non-transitory computer readable medium of claim 13 , wherein the instructions cause the one or more processors of the magnetic resonance apparatus to calculate the plurality of projection images by applying a Hermitian matrix to the SENSE plus motion model to transform from a modified model-generated k-space data domain to an image domain.

15. The non-transitory computer readable medium of claim 11 , wherein the instructions cause the one or more processors of the magnetic resonance apparatus to calculate the phase difference map of each respective one of the plurality of projection images using a low rank matrix completion algorithm.

16. The non-transitory computer readable medium of claim 15 , wherein the low rank matrix completion algorithm comprises a parallel imaging using eigenvector maps Iterative Self-consistent Parallel Imaging Reconstruction from Arbitrary k-Space (ESPIRiT).

17. The non-transitory computer readable medium of claim 11 , wherein k-space data acquired for each respective motion state represents a homogenous distribution of the k-space data across k-space.

18. The non-transitory computer readable medium of claim 11 , wherein the instructions cause the one or more processors of the magnetic resonance apparatus to acquire the motion parameters for each respective motion state using a non-imaging echo having an echo time (TE) that is less than a TE associated with one or more imaging echoes.

19. The non-transitory computer readable medium of claim 18 , wherein the non-imaging

Assignees

• Siemens Healthcare Gmbh
• Massachusetts Gen Hospital

Inventors

• Polak Daniel
• Setsompop Kawin
• Cauley Stephen Farman

Classifications

• 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 Primary

  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

• G01R33/56509 Primary

  due to motion, displacement or flow, e.g. gradient moment nulling (G01R33/567 takes precedence) · CPC title

• G01R33/5676

  Gating or triggering based on an MR signal, e.g. involving one or more navigator echoes for motion monitoring and correction · CPC title