Hierrarchical mapping framework for coil compression in magnetic resonance image reconstruction

• US 10,310,042 B2
• Filed: 04/23/2015
• Issued: 06/04/2019
• Est. Priority Date: 04/24/2014
• Status: Active Grant

• Current Assignee: The General Hospital Corporation (Mass General Brigham, Inc.)
• Original Assignee: The General Hospital Corporation (Mass General Brigham, Inc.)
• Inventors: Cauley, Stephen, Polimeni, Jonathan, Setsompop, Kawin, Wald, Lawrence
• Primary Examiner(s): Bonnette, Rodney A
• Application Number:
  US15/305,436
• Publication Number:
  US 20170045599A1
• Time in Patent Office: 1,503 Days
• CPC Class Codes: A61B 5/055 involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
• CPC Class Codes: A61B 5/7246 using correlation, e.g. template matching or determination of similarity
• CPC Class Codes: A61B 5/7285 for synchronising or triggering a physiological measurement or image acquisition with a physiological event or waveform, e.g. an ECG signal A61B6/5205, A61B6/541, A61B8/5207, A61B8/543 take precedence
• CPC Class Codes: G01R 33/3664 Switching for purposes other than coil coupling or decoupling, e.g. switching between a phased array mode and a quadrature mode, switching between surface coil modes of different geometrical shapes, switching from a whole body reception coil to a local reception coil or switching for automatic coil selection in moving table MR or for changing the field-of-view G01R33/3671 takes precedence
• CPC Class Codes: G01R 33/482 using a Cartesian trajectory
• CPC Class Codes: G01R 33/4824 using a non-Cartesian trajectory
• 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 33/5611 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

Abstract

Systems and methods for a hierarchical mapping framework (“HMF”} for coil compression are provided. The HMF-based coil compression can be applied to existing coil compression algorithms to improve their performance. The receive channels associated with a coil array are divided into subgroups based on the strength of their mutual correlation. In each subgroup, one or more virtual channels are produced based on the channels not in the subgroup. The virtual channels are produced using a coil compression algorithm subject to a hierarchically semiseparable channel mixing across the subgroups. Images are reconstructed for the subgroups and then combined to produce the final image of the subject.

Claims

1. A method for reconstructing 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 the MRI system, wherein the data is acquired using a plurality of receive channels associated with a radio frequency (RF) coil array;

(b) determining a strength of correlation between the plurality of receive channels;

(c) creating subgroups of the receive channels based on the determined strength of correlation between the plurality of receive channels;

(d) producing at least one virtual channel for each subgroup using a hierarchical semiseparable channel mixing across the subgroups;

(e) reconstructing an image for each subgroup from the acquired data and using the receive channels and virtual channels in each respective subgroup; and

(f) producing an image of the subject by combining the reconstructed images for each subgroup.

2. The method as recited in claim 1, wherein step (c) includes creating the subgroups such that each subgroup contains at least two of the plurality of receive channels, wherein the at least two receive channels are more strongly correlated to each other than other receive channels.

3. The method as recited in claim 1, wherein step (d) includes producing the at least one virtual channel using a coil compression algorithm subject to the hierarchical semiseparable channel mixing.

4. The method as recited in claim 3, wherein the coil compression algorithm is a singular value decomposition-based coil compression algorithm.

5. The method as recited in claim 3, wherein the coil compression algorithm is a geometric-decomposition coil compression algorithm.

6. The method as recited in claim 3, wherein the coil compression algorithm uses a global mapping framework to produce a virtual coil for a particular subgroup by compressing channels not in the particular subgroup into the virtual coil.

7. The method as recited in claim 1, wherein step (c) further includes producing at least one pseudo-channel for a particular subgroup based on the receive channels selected to be in that particular subgroup.

8. The method as recited in claim 7, wherein producing the at least one pseudo-channel includes solving a non-linear least squares problem to compute a magnitude of the at least one pseudo-channel as a sum-of-squares combination of the receive channels in the particular subgroup.