Systems and methods for strategically acquired gradient echo imaging

• US 10,451,697 B2
• Filed: 07/25/2017
• Issued: 10/22/2019
• Est. Priority Date: 07/25/2017
• Status: Active Grant

• Current Assignee: Spintech, Inc.
• Original Assignee: Spintech, Inc.
• Inventors: Haacke, E. Mark
• Primary Examiner(s): Lindsay, Jr., Walter L
• Assistant Examiner(s): Wenderoth, Frederick
• Application Number:
  US15/659,353
• Publication Number:
  US 20190033418A1
• Time in Patent Office: 819 Days
• Field of Search: 324309
• CPC Class Codes: A61B 2576/026 for the brain
• CPC Class Codes: A61B 5/055 involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: G01R 33/246 Spatial mapping of the RF magnetic field B1
• CPC Class Codes: G01R 33/4818 MR characterised by data acquisition along a specific k-space trajectory or by the temporal order of k-space coverage, e.g. centric or segmented coverage of k-space
• CPC Class Codes: G01R 33/50 based on the determination of relaxation times , e.g. T1 measurement by IR sequences; T2 measurement by multiple-echo sequences
• CPC Class Codes: G01R 33/5613 Generating steady state signals, e.g. low flip angle sequences [FLASH]
• CPC Class Codes: G01R 33/5615 Echo train techniques involving acquiring plural, differently encoded, echo signals after one RF excitation, e.g. using gradient refocusing in echo planar imaging [EPI], RF refocusing in rapid acquisition with relaxation enhancement [RARE] or using both RF and gradient refocusing in gradient and spin echo imaging [GRASE]
• CPC Class Codes: G01R 33/56554 caused by acquiring plural, differently encoded echo signals after one RF excitation, e.g. correction for readout gradients of alternating polarity in EPI
• CPC Class Codes: G01R 33/5676 Gating or triggering based on an MR signal, e.g. involving one or more navigator echoes for motion monitoring and correction

Abstract

Variable flip angle techniques with constraints for reconstructing MR images include a processor generating a T_1app map representing a spatial distribution of T_1app within an anatomical region using a first MR dataset corresponding to a first flip angle (FA) and a second MR dataset corresponding to a second FA. The processor can estimate a first and second transmit RF field maps by scaling the T_1app map by a first constant value of T₁ associated with a first tissue type and a second constant value of T₁ associated with a second tissue type, respectively. The processor can generate a third transmit RF field map using the first and second transmit RF field maps, and use the third transmit RF field map to construct MR images of the anatomical region. Weighted subtraction images can be created with improved contrast-to-noise ratio compared to images of the first and second MR datasets.

Claims

1. A magnetic resonance imaging (MRI) system comprising:

a MRI scanner configured to acquire a first magnetic resonance (MR) dataset corresponding to a first flip angle and a second MR dataset corresponding to a second flip angle by imaging an anatomical region using at least one echo time;

at least one processor; and

a memory, with computer code instructions stored thereon, the computer code instructions, when executed by the at least one processor, cause the at least one processor to;

generate an apparent longitudinal relaxation time (T_1app) map, representing a spatial distribution of T1app within the anatomical region using the first MR dataset, the second MR dataset and a constant value for a transmit radio frequency (RF) field within the anatomical region;

estimate a first transmit RF field map by scaling the T_1app map by a first constant value of longitudinal relaxation time (T₁), the first constant value of T₁ associated with a first tissue type within the anatomical region;

estimate a second transmit RF field map by scaling the T_1app map by a second constant value of T₁, the second constant value of T₁ associated with a second tissue type within the anatomical region; and

generate a third transmit RF field map using the estimated first transmit RF field map and the estimated second transmit RF field map, the third transmit RF field map representing a spatial distribution of the transmit RF field within the anatomical region.

2. The MRI system of claim 1, wherein the anatomical region is a human brain, the first tissue type is white matter, and the second tissue type is gray matter.

3. The MRI system of claim 1, wherein the computer code instructions, when executed by the at least one processor, further cause the at least one processor to:

estimate a fourth transmit RF field map by scaling the T_1app map by a third constant value of T₁, the third constant value of T₁ associated with a third tissue type within the anatomical region,

wherein generating the third transmit RF field map includes using the estimated first transmit RF field map, the estimated second transmit RF field map, and the estimated fourth transmit RF field map.

4. The MRI system of claim 3, wherein the anatomical region is a human brain, the first tissue type is white matter, the second tissue type is gray matter and the third tissue type is cerebro-spinal fluid.

5. The MRI system of claim 1, wherein the computer code instructions, when executed by the at least one processor, further cause the at least one processor to:

generate a first longitudinal relaxation time (T₁) map by dividing the T_1app map by the square of the third transmit RF field map, the first T₁ map representing the spatial distribution of T₁ within the anatomical region.

6. The MRI system of claim 1, wherein the computer code instructions, when executed by the at least one processor, further cause the at least one processor to:

generate an apparent spin density (SD_app) map using the first MR dataset, the second MR dataset, and the constant value for the transmit RF field, the SD_app map representing a spatial distribution of SD_app within the anatomical region;

generate a first spin density (SD) map by scaling the SD_app map by the third transmit RF field map;

synthesize, using the third transmit RF field map, the first T₁ map, and the first SD map, a third MR dataset corresponding to a third flip angle such that at least a first sub-region of the anatomical region corresponding to the first tissue type and a second sub-region of the anatomical region corresponding to the second tissue type are isointense;

estimate a receive RF field map using the synthesized third MR dataset, the receive RF field map representing spatial distribution of a receive RF field within the anatomical region; and

generate a second SD map by scaling the first SD map by the estimated receive RF field map.

7. The MRI system of claims 1, wherein the computer code instructions, when executed by the at least one processor, cause the at least one processor to:

estimate a plurality of transmit RF field maps for a plurality of subjects; and

generate a transmit RF field template using an averaging of the estimated plurality of transmit RF field maps for the plurality of subjects.

8. The MRI system of claim 7, wherein the computer code instructions, when executed by the at least one processor, cause the at least one processor to generate a second T₁ map using the T_1app map and the transmit RF field template.

9. The MRI system of claim 7, wherein the computer code instructions, when executed by the at least one processor, cause the at least one processor to generate a spin density (SD) map using an estimated SD_app map, the transmit RF field template, and an estimated receive RF field map.

10. The MRI system of claim 6, wherein the computer code instructions, when executed by the at least one processor, cause the at least one processor to:

scale the first MR dataset by the receive RF field map;

scale the second MR dataset by the receive RF field map; and

generate an image representing a weighted subtraction of the scaled first MR dataset from the scaled second MR dataset.

11. The MRI system of claim 1, wherein the computer code instructions, when executed by the at least one processor, cause the at least one processor to generate an image representing a weighted subtraction of the first MR dataset from the second MR dataset.

12. A method for magnetic resonance imaging (MRI), comprising:

receiving, by at least one processor, a first magnetic resonance (MR) dataset corresponding to a first flip angle and a second MR dataset corresponding to a second flip angle, the first MR dataset and the second MR dataset acquired by imaging an anatomical region using at least one echo time;

generating, by at least one processor, an apparent longitudinal relaxation time (T_1app) map, representing a spatial distribution of T_1app within the anatomical region using the first MR dataset, the second MR dataset and a constant value for a transmit radio frequency (RF) field within the anatomical region;

estimating, by the at least one processor, a first transmit RF field map by scaling the T_1app map by a first constant value of longitudinal relaxation time (T₁), the first constant value of T₁ associated with a first tissue type within the anatomical region;

estimating, by the at least one processor, a second transmit RF field map by scaling the T_1app map by a second constant value of T₁, the second constant value of T₁ associated with a second tissue type within the anatomical region; and

generating, by the at least one processor, a third transmit RF field map using the estimated first transmit RF field map and the estimated second transmit RF field map, the third transmit RF field map representing a spatial distribution of the transmit RF field within the anatomical region.

13. The method of claim 12 further comprising:

estimating, by the at least one processor, a fourth transmit RF field map by scaling the T_1app map by a third constant value of T₁, the third constant value of T₁ associated with a third tissue type within the anatomical region,

wherein generating the third transmit RF field map includes using the estimated first transmit RF field map, the estimated second transmit RF field map, and the estimated fourth transmit RF field map.

14. The method of claim 12 further comprising:

generating, by the at least one processor, a first longitudinal relaxation time (T₁) map by dividing the T_1app map by the square of the third transmit RF field map, the first T₁ map representing spatial distribution of T₁ within the anatomical region.

15. The method of claim 14 further comprising:

generating, by the at least one processor, an apparent spin density (SD_app) map using the first MR dataset, the second MR dataset, and the constant value for the transmit RF field, the SD_app map representing spatial distribution of SD_app within the anatomical region; and

generating, by the at least one processor, a first spin density (SD) map by scaling the SDapp map by the third transmit RF field map;

synthesizing, by the at least one processor, using the third RF field map, the first T₁ map, and the first SD map, a third MR dataset corresponding to a third flip angle such that at least a first sub-region of the anatomical region corresponding to the first tissue type and a second sub-region of the anatomical region corresponding to the second tissue type are isointense;

estimating, by the at least one processor, a receive RF field map using the synthesized third MR dataset, the receive RF field map representing spatial distribution of a receive RF field within the anatomical region; and

generating, by the at least one processor, a second SD map by scaling the first SD map by the estimated receive RF field map.

16. The method of claim 12 further comprising:

estimating, by the at least one processor, a plurality of transmit RF field maps for a plurality of subjects; and

generating, by the at least one processor, a transmit RF field template using an averaging of the estimated plurality of transmit RF field maps for the plurality of subjects.

17. The method of claim 16 further comprising:

generating, by the at least one processor, a spin density (SD) map using an estimated SD_app map, the transmit RF field template, and an estimated receive RF field map.

18. The method of claim 16 further comprising:

generating, by the at least one processor, a second T₁ map using the T_1app map and the transmit RF field template.

19. The method of claim 15 further comprising:

scaling, by the at least one processor, the first MR dataset by the receive RF field map;

scaling, by the at least one processor, the second MR dataset by the receive RF field map; and

generating, by the at least one processor, an image representing a weighted subtraction of the scaled first MR dataset from the scaled second MR dataset.

20. A non-transitory computer-readable medium comprising computer code instructions stored thereon, the computer code instructions when executed by a at least one processor cause the at least one processor to:

receive a first magnetic resonance (MR) dataset corresponding to a first flip angle and a second MR dataset corresponding to a second flip angle, the first MR dataset and the second MR dataset acquired by imaging an anatomical region using at least one echo time;

generate an apparent longitudinal relaxation time (T_1app) map, representing a spatial distribution of T_1app within the anatomical region using the first MR dataset, the second MR dataset and a constant value for a transmit radio frequency (RF) field within the anatomical region;

estimate a first transmit RF field map by scaling the T_1app map by a first constant value of longitudinal relaxation time (T₁), the first constant value of T₁ associated with a first tissue type within the anatomical region;

estimate a second transmit RF field map by scaling the T_1app map by a second constant value of T₁, the second constant value of T₁ associated with a second tissue type within the anatomical region; and

generate a third transmit RF field map using the estimated first transmit RF field map and the estimated second transmit RF field map, the third transmit RF field map representing a spatial distribution of the transmit RF field within the anatomical region.