SYSTEM AND METHOD FOR MULTISTATION IMAGE PASTING FOR WHOLE BODY DIFFUSION-WEIGHTED IMAGING

• US 20130265045A1
• Filed: 04/06/2012
• Published: 10/10/2013
• Est. Priority Date: 04/06/2012
• Status: Abandoned Application

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Xu, Dan
• Application Number:
  US13/441,047
• Publication Number:
  US 20130265045A1
• Time in Patent Office: Days
• US Class Current: 324/309
• CPC Class Codes: G01R 33/56341 Diffusion imaging
• CPC Class Codes: G01R 33/56383 involving motion of the sample as a whole, e.g. multistation MR or MR with continuous table motion
• CPC Class Codes: G01R 33/56518 due to eddy currents, e.g. caused by switching of the gradient magnetic field

Abstract

An MRI system includes a plurality of gradient coils positioned about a bore of a magnet, an RF transceiver system and an RF switch controlled by a pulse module to transmit RF signals to an RF coil assembly to acquire MR images, and a computer programmed to execute a diffusion-weighted imaging pulse sequence to acquire MR data from a subject over two or more stations, acquire imaging data of the subject over the two or more stations, reconstruct images that correspond to each of the two or more stations, and calculate an average intensity signal per slice within each of the reconstructed images. The computer is further programmed to adjust intensity within at least one of the reconstructed images based on the calculated average intensity signal within each of the reconstructed images and form a pasted image using a reconstructed image having its intensity adjusted and another reconstructed image.

Claims

1. An MRI apparatus comprising:

a magnetic resonance imaging (MRI) system having a plurality of gradient coils positioned about a bore of a magnet, and an RF transceiver system and an RF switch controlled by a pulse module to transmit RF signals to an RF coil assembly to acquire MR images; and

a computer programmed to;

execute a diffusion-weighted imaging pulse sequence to acquire MR data from a subject over two or more stations;

acquire imaging data of the subject over the two or more stations;

reconstruct images that correspond to each of the two or more stations;

calculate an average intensity signal per slice within each of the reconstructed images;

adjust intensity within at least one of the reconstructed images based on the calculated average intensity signal within each of the reconstructed images; and

form a pasted image using the at least one of the reconstructed images having its intensity adjusted and another reconstructed image, having a boundary formed therebetween.

2. The MRI apparatus of claim 1 wherein the computer is further programmed to perform a station-wise correction by being programmed to:

calculate a difference in magnitude of intensity proximate a border between two stations of reconstructed image data;

adjust image intensity data within one of the two stations of reconstructed images, and subsequent stations, based on the difference;

calculate another difference in magnitude of intensity proximate another border between two subsequent stations of reconstructed data; and

adjust image intensity data within one of the two subsequent stations, based on the another difference.

3. The MRI apparatus of claim 1 wherein the computer is further programmed to perform a slice-wise correction by being programmed to curvefit the calculated average intensity, determine a ratio per-slice between image data of the reconstructed images and the curvefit, and adjust the intensity per-slice based on a corresponding ratio.

4. The MRI apparatus of claim 1 wherein the computer is programmed to:

determine whether an overlap has occurred at the boundary between two of the two or more stations; and

curvefit average intensity per slice within each of the reconstructed images to determine a difference in magnitude of intensity between the two stations.

5. The MRI apparatus of claim 4 wherein:

if overlap has occurred between two of the stations, then the computer is programmed to use at least average slice data from each of the two stations within an overlap region at the boundary; and

if overlap has not occurred between two of the stations, then the computer is programmed to use average slice data proximate the boundary from each of the two stations.

6. The MRI apparatus of claim 1 wherein if there is mis-registration at the boundary between the reconstructed images, the computer is programmed to adjust registration of the reconstructed images in one of a coronal and a sagittal plane by applying an inter-station per-slice shift.

7. The MRI apparatus of claim 6 wherein, if the computer adjusts the registration between the reconstructed images, the computer is further programmed to adjust the registration by being programmed to:

apply a correlation-based shift if there is an image overlap at the boundary;

or

apply a mutual information-based shift if there is not an image overlap at the boundary.

8. The MRI apparatus of claim 1 wherein the computer is programmed to:

prior to executing the diffusion-weighted imaging scan, obtain a reference scan at each station and estimate a frequency offset to correct, within each slice of each station, at least one of a tissue susceptibility and a B₀ field inhomogeneity when executing the diffusion-weighted imaging pulse sequence; and

apply an intra-station per-slice shift to the reconstructed images based on the reference scan.

9. The MRI apparatus of claim 1 wherein the computer is programmed to smooth data in the formed image by being programmed to recalculate image slice information in the pasted image and at the boundary based on neighboring slice data in both a positive and a negative slice direction.

10. A method of MR imaging comprising:

obtaining diffusion-weighted MR imaging data of a subject from at least two stations;

reconstructing images of the subject using the MR imaging data that correspond to the at least two stations, the images having a border formed therebetween;

calculating an average intensity signal per slice within the reconstructed images to determine a net intensity offset between the reconstructed images;

offsetting intensity of pixels within at least one of the reconstructed images based on the determined net intensity offset; and

forming a pasted image using the reconstructed images, wherein at least one of the reconstructed images includes the at least one reconstructed image having had its pixel intensity offset.

11. The method of claim 10 comprising:

determining whether the border is formed of images that overlap one another to form the boundary, or whether the border is formed of images that abut one another to form the boundary;

curvefitting the average intensity within each of the images;

determining the net intensity offset based on a curvefit of the average intensity within each of the images;

calculating a station-wise correction coefficient based on the net intensity offset and based on a magnitude of the calculated intensity proximate where the net intensity offset is calculated; and

applying the station-wise correction coefficient to one of the images that forms the border and to subsequent images.

12. The method of claim 10 further comprising calculating a per-slice correction based on a curvefit of the average intensity, wherein offsetting the intensity of the pixels comprises offsetting each slice based on a respective per-slice correction.

13. The method of claim 11 comprising:

if the images overlap to form the boundary, then the step of curvefitting comprises curvefitting using imaging data in each of the images that overlaps to form the boundary;

or

if the images abut to form the boundary, then the step of curvefitting comprises curvefitting using imaging data in each of the images that abut one another.

14. The method of claim 10 comprising:

determining whether image mis-registration has occurred between the images at the boundary in at least one of a coronal and a sagittal plane;

adjusting image registration between the images using one of;

applying a correlation-based shift if there is an image overlap at the boundary; and

applying a mutual-information based shift if images that form the boundary abut one another.

15. The method of claim 10 comprising:

obtaining a reference scan of each of the at least two stations;

estimating a frequency offset within each reconstructed image to correct, within each station, at least one of a tissue susceptibility and a B₀ field inhomogeneity when executing the diffusion-weighted MR imaging scan; and

applying an intra-station per-slice shift to the reconstructed images based on the reference scan.

16. The method of claim 10 comprising:

recalculating image slice information in the pasted image proximate a boundary between two of the reconstructed images based at least on neighboring pixel data in at least one of a positive and a negative slice direction; and

reforming the pasted image using the recalculated image slice information.

17. A computer readable storage medium having stored thereon a computer program comprising instructions, which, when executed by a computer, cause the computer to:

execute a diffusion-weighted imaging pulse sequence to acquire MR data from a subject over two or more stations;

acquire imaging data of the subject over the two or more stations;

reconstruct images of each of the two or more stations using the acquired imaging data;

calculate an average intensity signal per slice within each of the two or more stations using the reconstructed images;

determine an amount of pixel intensity offset based on the calculated average intensity per slice;

adjust intensity within at least one of the reconstructed images based on the pixel intensity offset; and

form a pasted image using the at least one of the reconstructed images having its intensity adjusted and another reconstructed image, having a boundary formed therebetween.

18. The computer readable storage medium of claim 17 wherein the computer is further programmed to curvefit the calculated average intensity, and adjust the intensity per-slice based on a corresponding ratio derived from a ratio determined per-slice between the reconstructed image data and the curvefit intensity.

19. The computer readable storage medium of claim 17 wherein the computer is further caused to:

determine whether an overlap has occurred at the boundary between two of the two or more stations; and

curvefit average intensity per slice within each of the reconstructed images.

20. The computer readable storage medium of claim 17 wherein:

if image overlap has occurred between two of the stations, then the computer is programmed to use at least average slice data from each of the two stations within an overlap region at the boundary; and

if overlap has not occurred between two of the stations, then the computer is programmed to use average slice data proximate the boundary from each of the two stations.

21. The computer readable storage medium of claim 17 wherein if there is mis-registration at the boundary between the reconstructed images, the computer is programmed to adjust registration of the reconstructed images in one of a coronal and a sagittal plane.

22. The computer readable storage medium claim 21 wherein, if the computer is programmed to adjust the registration between the reconstructed images, the computer is further programmed to:

apply a correlation-based shift if there is an image overlap at the boundary;

or

apply a mutual information-based shift if there is not an image overlap at the boundary.

23. The computer readable storage medium of claim 17 wherein the

computer is programmed to:

prior to executing the diffusion-weighted imaging scan, obtain a reference scan at each station and estimate a frequency offset to correct, within each station, at least one of a tissue susceptibility and a B₀ field inhomogeneity when executing the diffusion-weighted imaging pulse sequence; and

apply an intra-station per-slice shift to the reconstructed images based on the reference scan.

24. The computer readable storage medium of claim 17 wherein the computer is programmed to smooth data in the formed image by being programmed to recalculate image slice information in the pasted image and at the boundary based on neighboring slice data in both a positive and a negative slice direction.