MAGNETIC RESONANCE IMAGING APPARATUS AND METHOD

• US 20190324106A1
• Filed: 04/17/2019
• Published: 10/24/2019
• Est. Priority Date: 04/23/2018
• Status: Active Grant

• Current Assignee: Canon Medical Systems Corporation (Canon Inc.)
• Original Assignee: Canon Medical Systems Corporation (Canon Inc.)
• Inventors: TSURUYAMA, Tomoya, NITTA, Shuhei, TAKEGUCHI, Tomoyuki
• Granted Patent: US 10,775,469 B2
• Time in Patent Office: Days
• CPC Class Codes: G01R 33/246 Spatial mapping of the RF magnetic field B1
• CPC Class Codes: G01R 33/5607 by reducing the NMR signal of a particular spin species, e.g. of a chemical species for fat suppression, or of a moving spin species for black-blood imaging
• CPC Class Codes: G01R 33/5608 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 Class Codes: G01R 33/5659 caused by a distortion of the RF magnetic field, e.g. spatial inhomogeneities of the RF magnetic field G01R33/56509, G01R33/56518, G01R33/56536 take precedence
• CPC Class Codes: G01R 33/58 Calibration of imaging systems, e.g. using test probes , Phantoms; Calibration objects or fiducial markers such as active or passive RF coils surrounding an MR active material

Abstract

According to one embodiment, a magnetic resonance imaging apparatus includes processing circuitry. The processing circuitry estimates transmission inhomogeneity caused in a transmit RF magnetic field from a first image based on a first signal received by a whole-body coil, and estimates reception inhomogeneity caused in a receive RF magnetic field from the first image and a second image based on a second signal received by a surface coil. The processing circuitry generates a third image, having a resolution higher than a resolution of the first image and a resolution of the second image, based on a third signal received by the surface coil. The processing circuitry corrects the third image by using the estimated transmission inhomogeneity and reception inhomogeneity.

Claims

1. A magnetic resonance imaging apparatus comprising a processing circuitry configured to:

estimate transmission inhomogeneity caused in a transmit RF magnetic field from a first image based on a first reception signal received by a whole-body coil, and estimate reception inhomogeneity caused in a receive RF magnetic field from the first image and a second image based on a second reception signal received by a surface coil;

generate a third image, having a resolution higher than a resolution of the first image and a resolution of the second image, based on a third reception signal received by the surface coil; and

correct the third image by using the estimated transmission inhomogeneity and reception inhomogeneity.

2. The apparatus according to claim 1, wherein

the first image is a sensitivity map indicating a sensitivity distribution of the whole-body coil obtained by a calibration scan, the second image is a sensitivity map indicating a sensitivity distribution of the surface coil obtained by the calibration scan, and the third image is a diagnosis image obtained by a main scan.

3. The apparatus according to claim 1, wherein

the processing circuitry generates an inhomogeneity-estimation image by using a transmission inhomogeneity-estimation image and a reception inhomogeneity-estimation image, the transmission inhomogeneity-estimation image is generated based on the transmission inhomogeneity to which a weight is applied in accordance with an imaging sequence, and a reception inhomogeneity-estimation image is generated based on the reception inhomogeneity.

4. The apparatus according to claim 3, wherein

the processing circuitry generates a first mask image based on a background region extracted from the first image,

generates a correction factor for correcting the third image by using the inhomogeneity-estimation image and the first mask image.

5. The apparatus according to claim 4, wherein the processing circuitry generates a second mask image relating to a boundary between an air and living tissue by using the first image, and

applies the second mask image to the first image and the second image.

6. The apparatus according to claim 1, wherein

the first image and the second image have a same resolution obtained by a calibration scan.

7. The apparatus according to claim 1, wherein

the first image and the second image are a proton-density-weighted image.

8. The apparatus according to claim 1, wherein

the processing circuitry generates a transmission inhomogeneity-estimation image by applying, to the first image, at least one of a filtering process, a fitting process and machine learning based on neural network.

9. The apparatus according to claim 8, wherein

the processing circuitry generates a transmission/reception inhomogeneity-estimation image by applying, to the second image, at least one of a filtering process, a fitting process, or machine learning based on neural network.

10. The apparatus according to claim 9, wherein

the processing circuitry generates a reception inhomogeneity-estimation image by removing a component relating to the transmission inhomogeneity-estimation image from the transmission/reception inhomogeneity-estimation image.

11. A magnetic resonance imaging method comprising:

estimating transmission inhomogeneity caused in a transmit RF magnetic field from a first image based on a first reception signal received by a whole-body coil, and estimating reception inhomogeneity caused in a receive RF magnetic field from the first image and a second image based on a second reception signal received by a surface coil;

generating a third image, having a resolution higher than a resolution of the first image and a resolution of the second image, based on a third reception signal received by the surface coil; and

correcting the third image by using the estimated transmission inhomogeneity and reception inhomogeneity.

12. The method according to claim 11, wherein

the first image is a sensitivity map indicating a sensitivity distribution of the whole-body coil obtained by a calibration scan, the second image is a sensitivity map indicating a sensitivity distribution of the surface coil obtained by the calibration scan, and the third image is a diagnosis image obtained by a main scan.

13. The method according to claim 11, further comprising:

generating an inhomogeneity-estimation image by using a transmission inhomogeneity-estimation image and a reception inhomogeneity-estimation image, the transmission inhomogeneity-estimation image is generated based on the transmission inhomogeneity to which a weight is applied in accordance with an imaging sequence, and a reception inhomogeneity-estimation image is generated based on the reception inhomogeneity.

14. The method according to claim 13, further comprising:

generating a first mask image based on a background region extracted from the first image; and

generating a correction factor for correcting the third image by using the inhomogeneity-estimation image and the first mask image.

15. The method according to claim 14, further comprising:

generating a second mask image relating to a boundary between an air and living tissue by using the first image; and

applying the second mask image to the first image and the second image.

16. The method according to claim 11, wherein

the first image and the second image have a same resolution obtained by a calibration scan.

17. The method according to claim 11, wherein

the first image and the second image are a proton-density-weighted image.

18. The method according to claim 11, further comprising generating a transmission inhomogeneity-estimation image by applying, to the first image, at least one of a filtering process, a fitting process and machine learning based on neural network.

19. The method according to claim 18, further comprising generating a transmission/reception inhomogeneity-estimation image by applying, to the second image, at least one of a filtering process, a fitting process, or machine learning based on neural network.

20. The method according to claim 19, further comprising generating a reception inhomogeneity-estimation image by removing a component relating to the transmission inhomogeneity-estimation image from the transmission/reception inhomogeneity-estimation image.