IP Library › Granted Patent US 11,467,238
Granted Patent B2
US 11,467,238 · App. 17/059,924 · Granted Oct 11, 2022

Method for uniform reconstruction of multi-channel surface-coil magnetic resonance data without use of a reference scan

Inventors: John Grinstead (Portland, OH); Matthew A. Frick (Rochester, MN); Vibhas Deshpande (Austin, TX); Kimberly K. Amrami (St. Paul, MN); Venkata Veerendranadh Chebrolu (Rochester, MN); Joel P. Felmlee (Rochester, MN); Peter Kollasch (Minnetonka, MN)
Assignees: Mayo Foundation for Medical Education and Research; Siemens Healthcare GmbH
G01R33/4828G01R33/565G01R33/5608
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Quick Facts
Patent No.
US 11,467,238
App. No.
17/059,924
Granted
Oct 11, 2022
Kind
B2
Abstract

Systems and methods for generating images with a magnetic resonance imaging (“MRI”) system, in which the images have been corrected for receive coil nonuniformities are described. Improved data acquisition schemes for fat saturation are also described.

Claims (41)

1. A method for generating an image of a subject using a magnetic resonance imaging (MRI) system, the steps of the method comprising:

(a) acquiring data from a subject using an MRI system that includes a multichannel receive coil having a plurality of different receive coils;

(b) reformatting the acquired data as a four-dimensional (4D) data set having three spatial dimensions and one dimension associated with a channel number of the multichannel receive coil;

(c) estimating cumulative coil sensitivity data from the 4D data set based on optimally combining data from the plurality of different receive coils and estimating coil sensitivity data for the plurality of different receive coils; and

(d) reconstructing an image of the subject from the acquired data using the cumulative coil sensitivity data, wherein the image has been corrected for non-uniformities in the multichannel receive coil based on the cumulative coil sensitivity data.

2. The method as recited in claim 1 , wherein step (c) implements a data-driven approach in which the data from the plurality of different receive coils is optimally combined and the coil sensitivity data are estimated from the combined data.

3. The method as recited in claim 1 , wherein step (c) implements a model-driven approach in which the coil sensitivity data are individually estimated from the plurality of different receive coils before optimally combining the estimated coil sensitivity data for the plurality of different receive coils.

4. The method as recited in claim 1 , wherein the optimally combining the data in step (c) can be performed using a singular-value decomposition (SVD).

5. The method as recited in claim 1 , wherein the cumulative coil sensitivity data can be computed based on modeling the multichannel receive coil non-uniformity using a polynomial dependent on spatial location indices.

6. The method as recited in claim 1 , wherein the cumulative coil sensitivity data can be iteratively estimated from the acquired data and the estimated coil sensitivity data.

7. The method as recited in claim 1 , wherein the cumulative coil sensitivity data can be analytically estimated from the acquired data and the estimated coil sensitivity data based on computing logarithms of the estimated coil sensitivity data.

8. The method as recited in claim 1 , wherein the data are acquired using a pulse sequence that has been optimized to improve fat saturation uniformity.

9. The method as recited in claim 8 , wherein the pulse sequence is optimized based on a selection of one or more RF parameters comprising RF pulse type, RF pulse duration, RF frequency off-set of fat with respect to water (“RFO”), bandwidth (“BW”), and time-bandwidth-product (“TBWP”).

10. A system for generating an image of a subject with improved uniformity, the system comprising:

a magnetic resonance imaging (MRI) system configured to acquire data from a subject using a multichannel receive coil having a plurality of different receive coils;

a computer system configured to:

(i) reformat the acquired data as a four-dimensional (4D) data set having three spatial dimensions and one dimension associated with a channel number of the multichannel receive coil;

(ii) estimate cumulative coil sensitivity data from the 4D data set based on optimally combining data from the plurality of different receive coils and estimating coil sensitivity data for the plurality of different receive coils; and

(iii) reconstruct an image of the subject from the acquired data using the cumulative coil sensitivity data, wherein the image has been corrected for non-uniformities in the multichannel receive coil based on the cumulative coil sensitivity data.

11. The system as recited in claim 10 , wherein the computer system implements a data-driven approach in which the data from the plurality of different receive coils is optimally combined and the coil sensitivity data are estimated from the combined data.

12. The system as recited in claim 10 , wherein the computer system implements a model-driven approach in which the coil sensitivity data are individually estimated from the plurality of different receive coils before optimally combining the estimated coil sensitivity data for the plurality of different receive coils.

13. The system as recited in claim 10 , wherein the optimally combining the data can be performed using a singular-value decomposition (SVD).

14. The system as recited in claim 10 , wherein the cumulative coil sensitivity data can be computed based on modeling the multichannel receive coil non-uniformity using a polynomial dependent on spatial location indices.

15. The system as recited in claim 10 , wherein the cumulative coil sensitivity data can be iteratively estimated from the acquired data and the estimated coil sensitivity data.

16. The system as recited in claim 10 , wherein the cumulative coil sensitivity data can be analytically estimated from the acquired data and the estimated coil sensitivity data based on computing logarithms of the estimated coil sensitivity data.

17. The system as recited in claim 10 , wherein the data are acquired using the magnetic resonance imaging system with a pulse sequence that has been optimized to improve fat saturation uniformity.

18. The system as recited in claim 17 , wherein the pulse sequence is optimized based on a selection of one or more RF parameters comprising RF pulse type, RF pulse duration, RF frequency off-set of fat with respect to water (“RFO”), bandwidth (“BW”), and time-bandwidth-product (“TBWP”).

19. A method for generating an image of a subject with improved uniformity, the method comprising:

a) determining a set of acquisition parameters for a magnetic resonance imaging (MRI) system;

b) acquiring data using the MRI system with the determined acquisition parameters;

c) generating a spectrum depicting fat and water based on the acquired data;

d) assessing a level of uniformity based on the generated spectrum;

e) updating the acquisition parameters until a threshold level of uniformity has been achieved and acquiring updated data using the updated acquisition parameters; and

f) reconstructing the image of the subject with improved uniformity from the updated data.

20. The method of claim 19 , wherein the acquisition parameters are radio frequency (RF) parameters including at least one of: RF pulse type, RF pulse duration, RF frequency off-set of fat with respect to water (“RFO”), bandwidth (“BW”), and time-bandwidth-product (“TBWP”).

21. The method of claim 19 , wherein determining acquisition parameters includes determining a symmetry of an RF pulse profile, and wherein acquiring data includes using the determined RF pulse profile.

22. The method of claim 21 , wherein the RF pulse profile is asymmetric.

23. The method of claim 22 , wherein the asymmetric RF pulse profile includes an edge between a water peak and a fat peak in the generated spectrum depicting fat and water.

24. The method of claim 21 , further comprising updating the acquisition parameters with the determined RF pulse profile when a threshold level of uniformity has not been achieved and acquiring updated data using the updated acquisition parameters.

25. The method of claim 24 , wherein updating the acquisition parameters includes updating a symmetric RF pulse profile with an asymmetric RF pulse profile.

26. The method of claim 19 , wherein assessing a level of uniformity includes determining if a threshold level of uniformity has been achieved in the reconstructed image of the subject.

Assignments (5)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 20, 2023
From: SIEMENS HEALTHCARE GMBH
To: SIEMENS HEALTHINEERS AG
Reel/Frame 066267/0346 →
CORRECTIVE ASSIGNMENT TO CORRECT THE ASSIGNEE PREVIOUSLY RECORDED ON REEL 054577 FRAME 0648. ASSIGNOR(S) HEREBY CONFIRMS THE ASSIGNMENT OF ASSIGNORS INTEREST. Recorded Dec 11, 2020
From: SIEMENS MEDICAL SOLUTIONS USA, INC.
To: SIEMENS HEALTHCARE GMBH
Reel/Frame 054700/0413 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 8, 2020
From: CHEBROLU, VENKATA VEERANDRANADH; KOLLASCH, PETER; GRINSTEAD, JOHN WILLIAM; DESHPANDE, VIBHAS S.
To: SIEMENS MEDICAL SOLUTIONS USA, INC.
Reel/Frame 054575/0779 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 8, 2020
From: PAUL, DOMINIK
To: SIEMENS HEALTHCARE GMBH
Reel/Frame 054577/0648 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 4, 2020
From: AMRAMI, KIMBERLY K.; FELMLEE, JOEL P.; FRICK, MATTHEW A.
To: MAYO FOUNDATION FOR MEDICAL EDUCATION AND RESEARCH
Reel/Frame 054549/0044 →
Continuity (2)
Provisional Application 62679638 · Jun 1, 2018
Related Publication 20210208226A1 · Jul 8, 2021
Cited By (1)
US 12,681,118