Hierrarchical mapping framework for coil compression in magnetic resonance image reconstruction

US10310042B2 · US · B2

Patent metadata
FieldValue
Publication numberUS-10310042-B2
Application numberUS-201515305436-A
CountryUS
Kind codeB2
Filing dateApr 23, 2015
Priority dateApr 24, 2014
Publication dateJun 4, 2019
Grant dateJun 4, 2019

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Abstract

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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.

First claim

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The invention claimed is: 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.

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Classifications

  • involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging · CPC title

  • using a non-Cartesian trajectory · CPC title

  • Parallel RF transmission, i.e. RF pulse transmission using a plurality of independent transmission channels · CPC title

  • 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 title

  • using correlation, e.g. template matching or determination of similarity · CPC title

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What does patent US10310042B2 cover?
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 pr…
Who is the assignee on this patent?
Massachusetts Gen Hospital
What technology area does this patent fall under?
Primary CPC classification G01R33/5612. Mapped technology areas include Physics.
When was this patent published?
Publication date Tue Jun 04 2019 00:00:00 GMT+0000 (Coordinated Universal Time) (B2). Legal status and post-grant events are not shown on this page.
What related patents are in patentsdb?
We list 8 related publications on this page (citations in our corpus or others sharing the same primary CPC).