Method and system for “push-button” comprehensive cardiac MR examination using continuous self-gated 3D radial imaging

Patent metadata

Field	Value
Publication number	US-9655522-B2
Application number	US-201514878937-A
Country	US
Kind code	B2
Filing date	Oct 8, 2015
Priority date	Oct 10, 2014
Publication date	May 23, 2017
Grant date	May 23, 2017

Abstract

The present invention teaches systems and methods for a simple cardiac MRI approach that (1) continuously acquires data; (2) covers the entire heart with high isotropic resolution within a few minutes; and (3) requires no physiological gating and minimal user intervention. Applications of the inventive systems and methods include, but are in no way limited to cardiac cine, myocardial perfusion, coronary MRA, delayed enhancement imaging, myocardial T1-weighted imaging for fibrosis imaging, and myocardial T2-weighted imaging for edema imaging.

First claim

What is claimed is:

1. A method for performing magnetic resonance imaging (MRI) on a subject, comprising performing one or more of the following scans using an MRI machine:
(a) a scout scan to determine the position of the subject's heart;
(b) a stress perfusion MRI scan on the subject's heart;
(c) a cine MRI scan on the subject's heart;
(d) a rest perfusion MRI scan on the subject's heart;
(e) a coronary MRA scan on the subject's heart; and
(f) a delayed enhancement MRI scan on the subject's heart; wherein (a) one or more scan is performed by using a continuous three dimensional radial acquisition scheme that results in the acquisition of a free-breathing k-space dataset, and (b) image reconstruction for one or more scan is performed using a constrained or compressed sensing scheme, and wherein the method does not require (1) ECG triggering, (2) breath-holding by the subject, or (3) the use of a diaphragm navigator.

2. The method of claim 1 , further comprising performing T2-weighted imaging for edema imaging of the subject's heart and/or performing T1-weighted imaging for fibrosis imaging of the subject's heart.

3. The method of claim 1 , wherein the image reconstruction for one or more scans comprises conjugate-gradient sensitivity encoding (CG-SENSE) reconstruction.

4. The method of claim 1 , further comprising correcting for the subject's motion during one or more scans by a method comprising:
(1) segmenting an acquired free-breathing k-space data set into different respiratory bins using self-navigation;
(2) using a single bin as a reference, estimating the respiratory motion of all other bins using image-based 3D affine registration; and
(3) using estimated translation vectors and affine transform matrices to modify the k-space data and trajectory, thereby resulting in motion-corrected k-space data and trajectory.

5. The method of claim 4 , further comprising incorporating the resulting motion-corrected k-space data and trajectory into a CG-SENSE reconstruction framework.

6. The method of claim 5 , further comprising performing sensitivity self-calibration by a method comprising:
(1) reconstructing motion-corrected individual coil images by gridding;
(2) calculating coil sensitivity maps by using the eigenvector of local signal covariance matrices as the estimate of the respective sensitivity values at a specific spatial location; and
(3) averaging the local image covariance matrices over blocks of a predetermined size to suppress streaking artifacts.

7. The method of claim 6 , wherein the averaging operation is implemented in MATLAB using a graphical processing unit (GPU).

8. The method of claim 7 , wherein the sensitivity encoding operation is performed using a gridding/regridding approach with a density compensation function (DCF) iteratively calculated from the k-space trajectory to compensate for sampling nonuniformity.

9. The method of claim 8 , further comprising preconditioning by density compensation to accelerate convergence of the CG iterations.

10. The method of claim 9 , further comprising introducing a contrast agent into the subject's vascular system prior to or during any of one or more of scans a-f.

11. The method of claim 10 , further comprising diagnosing the subject with the presence or absence of a cardiovascular disease or condition based upon one or more resulting images.

12. The method of claim 11 , wherein the cardiovascular disease is atherosclerosis and/or cardiomyopathy.

13. The method of claim 11 , wherein the MRI machine is a 1.5T scanner or a 3T scanner.

14. A magnetic resonance imaging system, comprising:
(1) a magnet operable to provide a magnetic field;
(2) a transmitter operable to transmit to a region within the magnetic field;
(3) a receiver operable to receive a magnetic resonance signal from the region; and
(4) a processor operable to control the transmitter and the receiver; wherein the processor is configured to direct the transmitter and receiver to execute a sequence, comprising (a) acquiring magnetic resonance data from a volume of interest (VOI) comprising all or a portion of the subject's heart according to the method of claim 1 ; and (b) generating one or more images using the image reconstruction scheme of claim 1 , wherein a processor of the MRI machine and/or a subsystem configured to function therewith are configured to generate one or more images based on the magnetic resonance data acquired.

15. A non-transitory machine-readable medium having machine executable instructions for causing one or more processors of a magnetic resonance imaging (MRI) machine, and/or a subsystem configured to function therewith, to execute a method, comprising:
performing one or more of the following scans:
(a) a scout scan to determine the position of a subject's heart;
(g) a stress perfusion MRI scan on the subject's heart;
(h) a cine MRI scan on the subject's heart;
(i) a rest perfusion MRI scan on the subject's heart;
(j) a coronary MRA scan on the subject's heart; and
(k) a delayed enhancement MRI scan on the subject's heart; wherein (a) one or more scan is performed by using a continuous three dimensional radial acquisition scheme that results in the acquisition of a free-breathing k-space dataset, and (b) image reconstruction for one or more scan is performed using a constrained or compressed sensing scheme, and wherein the method does not require (1) ECG triggering, (2) breath-holding by the subject, or (3) the use of a diaphragm navigator.

16. The non-transitory machine-readable medium of claim 15 , wherein the method executed further comprises performing T2-weighted imaging for edema imaging of the subject's heart and/or performing T1-weighted imaging for fibrosis imaging of the subject's heart.

17. The non-transitory machine-readable medium of claim 15 , wherein the image reconstruction for one or more scans comprises conjugate-gradient sensitivity encoding (CG-SENSE) reconstruction.

18. The non-transitory machine-readable medium of claim 17 , wherein the method executed further comprises correcting for the subject's motion during one or more scans by a method comprising:
(1) segmenting an acquired free-breathing k-space data set into different respiratory bins using self-navigation;
(2) using a single bin as a reference, estimating the respiratory motion of all other bins using image-based 3D affine registration; and
(3) using estimated translation vectors and affine transform matrices to modify the k-space data and trajectory, thereby resulting in motion-corrected k-space data and trajectory.

19. The non-transitory machine-readable medium of claim 18 , wherein the executed method further comprises incorporating the resulting motion-corrected k-space data and trajectory into a CG-SENSE reconstruction framework.

20. The non-transitory machine-readable medium of claim 19 , wherein the method executed further comprises performing sensitivity self-calibration by a method comprising:
(1) reconstructing motion-corrected individual coil images by gridding;
(2) calculating coil sensitivity maps by using the eigenvector of local signal covariance matrices as the estimate of the respective sensitivity values at a specific spatial location; and
(3) averaging the local image covariance matrices over blocks of a predetermined size to suppress streaking artifacts.

Assignees

• Cedars Sinai Medical Center

Inventors

• Li Debiao
• Sharif Behzad
• Berman Daniel S
• Pang Jianing

Classifications

• G01R33/56509 Primary

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

• A61B5/0044 Primary

  for the heart · CPC title

• G06T7/337

  involving reference images or patches · CPC title

• G01R33/5602

  by filtering or weighting based on different relaxation times within the sample, e.g. T1 weighting using an inversion pulse · 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