MRI METHOD OF FASTER CHANNEL-BY-CHANNEL RECONSTRUCTION WITHOUT IMAGE DEGRADATION

• US 20140070804A1
• Filed: 03/16/2012
• Published: 03/13/2014
• Est. Priority Date: 03/17/2011
• Status: Abandoned Application

• Current Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Original Assignee: Koninklijke Philips N.V.
• Inventors: Huang, Feng, Lin, Wei
• Application Number:
  US14/005,300
• Publication Number:
  US 20140070804A1
• Time in Patent Office: Days
• US Class Current: 324/309
• CPC Class Codes: G01R 33/34 Constructional details, e.g. resonators , specially adapted to MR
• CPC Class Codes: G01R 33/56 Image enhancement or correction, e.g. subtraction or averaging techniques , e.g. improvement of signal-to-noise ratio and resolution
• CPC Class Codes: G01R 33/5611 Parallel magnetic resonance imaging, e.g. sensitivity encoding [SENSE], simultaneous acquisition of spatial harmonics [SMASH], unaliasing by Fourier encoding of the overlaps using the temporal dimension [UNFOLD], k-t-broad-use linear acquisition speed-up technique [k-t-BLAST], k-t-SENSE structural details of arrays of sub-coils G01R33/3415
• CPC Class Codes: G01R 33/5612 Parallel RF transmission, i.e. RF pulse transmission using a plurality of independent transmission channels
• CPC Class Codes: G01R 33/56509 due to motion, displacement or flow, e.g. gradient moment nulling G01R33/567 takes precedence

Abstract

A plurality of coil elements (18, 18′) and corresponding receivers (26) define a plurality of channels, each carrying a corresponding partial k-space data set (60, 64). One or more processors (30) generate (80) a first image representation (76) based on the plurality of partial k-space data sets, generate a relative sensitivity map (82) for each of the channels, project (90) the first image representation (76) with each of the relative sensitivity maps (82) to generate a plurality of recreated k-space data sets (92), and each partial k-space data and the corresponding recreated k-space data set are combined to generate substituted k-space data sets (96). The substituted k-space data sets are reconstructed (100) into a plurality of images (102) which are combined (104) to create a final image (106).

Claims

1. A method for magnetic resonance imaging, comprising:

generating a first image representation of an examination region based on a plurality of partial k-space data sets, each data set being associated with at least one channel;

generating a relative sensitivity map for each of a plurality of coil elements, each coil element being associated with at least one channel;

projecting the first image representation and each relative sensitivity map to generate a plurality of recreated k-space data sets, each generated recreated k-space data set corresponding to one partial k-space dataset; and

combining each partial k-space data set and the corresponding recreated k-space data set to generate substituted k-space data sets.

3. The method according to claim 1, wherein the step of generating the first image representation includes:

compressing the plurality of partial data sets into at least one compressed partial data set;

reconstructing the at least one compressed partial data set the first image representation.

4. The method according to claim 3, wherein the plurality of partial data sets is compressed into the at least one partial data set based on a principal component analysis.

5. The method according to claim 3, wherein the plurality of partial data sets is compressed into more than one partial data set and the reconstruction is based on a channel-by-channel partially parallel imaging algorithm.

6. The method according to claim 1, wherein the relative sensitivity maps are calculated sensitivity maps for each coil element and from an auto-calibration signal generated during the reconstruction of the first image representation or a pre-scan to generate the sensitivity maps.

7. The method according to claim 1, wherein combining the partial k-space data sets and the recreated k-space data set includes substituting actually collected data from the partial k-space data sets for corresponding or missing data in the recreated data set.

8. The method according to claim 1, wherein generating the first image representation includes:

performing principle component analysis on the partial k-space data sets to generate combined partial k-space data sets;

selecting a subset of the combined partial k-space data sets with the largest eigenvalues;

reconstructing the subset of combined partial k-space data sets into the first image representation using a partial parallel image reconstruction technique.

9. The method according to claim 8, wherein the step of selecting a subset of combined partial k-space data sets with the largest eigenvalues, includes:

selecting a subset of the combined partial k-space data sets with the smallest eigenvalues; and

performing motion correction on the selected subset of combined partial k-space data sets with the largest eigenvalues; and

combining the motion corrected subset of combined partial k-space data sets with the subset of the combined partial k-space data sets with the smallest eigenvalues.

10. A magnetic resonance imaging system comprising:

a plurality of RF coil elements;

a plurality of RF transmitters;

a plurality of RF receivers each connected with one of the coil elements;

gradient magnetic field coils;

a gradient coil controller;

a magnetic resonance scan controller which controls the RF transmitters, the RF receivers, and the gradient controller to generate a plurality of partial k-space data sets, each data set being associated with a least one channel defined by a corresponding pair of RF coil elements and RF receiver; and

a computer processor programmed the method according to Claim 1.

2. The method according to claim 2, further including:

reconstructing each substitute k-space data set into a partial image representation of the examination region; and

combining the partial image representations into a volume representation.

11. A magnetic resonance imaging system comprising:

a plurality of coil elements and corresponding receivers which define a plurality of channels, data from the channels during the magnetic resonance imaging sequence constituting a plurality of k-space data sets;

one or more processors programmed to;

generate a first image representation of an examination region based on the plurality of k-space data sets,

generate a relative sensitivity map for each of the channels,

project the first image representation with each of the relative sensitivity maps to generate a corresponding plurality of recreated k-space data sets, and

combine each partial k-space data set and the corresponding recreated k-space data set to generate substituted k-space data sets.

12. The apparatus according to claim 11, further including:

reconstructing each substitute k-space data set into a partial image representation of the examination region; and

combining the partial image representations into a volume representation.

13. The apparatus according to claim 11, wherein the step of generating the first image representation includes:

compressing the plurality of partial data sets into at least one compressed partial data set;

reconstructing the at least one compressed partial data set into the first image representation.

14. The apparatus according to claim 13 wherein the plurality of partial data sets is compressed into the at least one partial data set based on a principal component analysis.

15. The apparatus according to claim 13, wherein the plurality of partial data sets is compressed into more than one partial data set and the reconstruction is based on a channel-by-channel partially parallel imaging algorithm.

16. The apparatus according to claim 11, wherein the relative sensitivity maps are calculated sensitivity maps for each coil element and from an auto-calibration signal generated during the reconstruction of the first image representation or a pre-scan to generate the sensitivity maps.

17. The apparatus according to claim 11, wherein combining the partial k-space data sets and the recreated k-space data set includes substituting actually collected data from the partial k-space data sets for corresponding or missing data in the recreated data set.

18. The apparatus according to claim 11, wherein generating the first image representation includes:

performing principle component analysis on the partial k-space data sets to generate combined partial k-space data sets;

selecting a subset of the combined partial k-space data sets with the largest eigenvalues;

reconstructing the subset of combined partial k-space data sets into the first image representation using a partial parallel image reconstruction technique.

19. The method according to claim 18, wherein the step selecting a subset of combined partial k-space data sets with the largest eigenvalues, includes:

selecting a subset of the combined partial k-space data sets with the smallest eigenvalues; and

performing motion correction on the selected subset of combined partial k-space data sets with the largest eigenvalues; and

combining the motion corrected subset of combined partial k-space data sets with the subset of the combined partial k-space data sets with the smallest eigenvalues.

20. In an image reconstruction system which generates a plurality of channels of partial k-space data, one or more processors programmed to:

compress the partial k-space data sets into a fewer number of channels;

generating a virtual coil image from one or more of the compressed channels;

applying a channel-by-channel reconstruction algorithm to the k-space data values of the compressed channels to generate a virtual coil image;

calculating a relative sensitivity map of the original channels;

projecting the virtual composite coil image using the relative sensitivity maps to generate a recreated sensitivity map corresponding to each original channel;

inserting acquired data from the partial k-space data sets into each of the corresponding recreated k-space data sets to generate substituted k-space data sets; and

reconstruct and combine the plurality of recreated k-space data sets.