Method and apparatus for acquiring a magnetic resonance imaging dataset

Patent metadata

Field	Value
Publication number	US-10058287-B2
Application number	US-201614994580-A
Country	US
Kind code	B2
Filing date	Jan 13, 2016
Priority date	Jan 13, 2015
Publication date	Aug 28, 2018
Grant date	Aug 28, 2018

Abstract

In a method and apparatus for acquiring a magnetic resonance imaging dataset of an area to be examined of a patient by magnetic resonance data are acquired over a prespecified acquisition period which has been fixed for the acquisition process. This acquisition period is divided into a number of sub-periods. For each sub-dataset of the magnetic resonance data acquired by undersampling in a sub-period, at least one motion value describing the motion status of the area to be examined is determined, and the data subsets to be used for the reconstruction of the magnetic resonance imaging dataset are selected in dependence on the motion values in order to minimize motion artifacts.

First claim

We claim as our invention:

1. A method for acquiring a magnetic resonance imaging data set of an examination region of a patient, comprising:
providing a control computer with a predetermined uninterrupted acquisition period for a data acquisition procedure for operating a magnetic resonance data acquisition scanner, in order to continuously acquire magnetic resonance raw data from an examination region of a patient and, in said control computer, dividing said uninterrupted acquisition period into a plurality of sub-periods;
using said control computer to operate said scanner to execute said data acquisition procedure so as to continuously acquire said raw magnetic resonance data from the examination region in each of said sub-periods individually, and entering the acquired raw data for each sub-period into an electronic memory representing k-space, thereby obtaining a plurality of data subsets in k-space respectively for the sub-periods, with each data subset being undersampled in k-space;
for each sub-period, determining a motion value that represents a motion status, during the respective sub-period, of said examination region;
in an image reconstruction computer, executing an image reconstruction algorithm to reconstruct an image of the examination region from said magnetic resonance raw data in k-space and, in said reconstruction algorithm, selecting data subsets from said electronic memory, based on said motion value of each sub-period, according to a selection criterion that minimizes motion artifacts in said image, and using only the selected data subsets in said reconstruction algorithm to reconstruct said image; and
making the reconstructed image of the examination region available in electronic form from said reconstruction computer as a data file.

2. A method as claimed in claim 1 comprising operating said scanner to acquire said magnetic resonance raw data from said examination region in each of said sub-periods as three-dimensional magnetic resonance raw data.

3. A method as claimed in claim 1 comprising dividing said uninterrupted predetermined acquisition period into a plurality of equally long sub-periods.

4. A method as claimed in claim 1 comprising determining said motion value for each data subset from the respective data subset itself.

5. A method as claimed in claim 4 comprising, in each sub-period, acquiring a magnetic resonance navigator and thereby including navigator data in each data subset in k-space, and determining the motion value for each data subset from the navigator data thereof.

6. A method as claimed in claim 4 comprising, from each individual data subset, determining a quality measure from the k-space data thereof and using said quality measure as said motion value for that respective data subset.

7. A method as claimed in claim 1 comprising determining the motion value for each data subset by implementing a measurement that is separate from the acquisition of the magnetic resonance raw data for that respective data subset.

8. A method as claimed in claim 7 comprising implementing said separate measurement as a measurement from the group consisting of using a camera to track a camera-detectable marker on the patient, obtaining an electronic signal representing respiratory movement from a breathing belt worn by the patient, and using a field camera to detect patient movement-induced changes in a magnetic field in said scanner.

9. A method as claimed in claim 1 comprising, for each sub-period, entering the magnetic resonance raw data acquired during that respective sub-period into k-space according to a data entry technique selected from the group consisting of stochastic data entry, pseudo-random data entry, and data entry according to a regular pattern.

10. A method as claimed in claim 1 comprising, for each sub-period, entering the magnetic resonance raw data thereof into k-space according to an incoherent sampling technique, and reconstructing said image of said examination region with an iterative reconstruction algorithm as said reconstruction algorithm.

11. A method as claimed in claim 1 comprising, for each sub-period, entering the raw magnetic resonance data thereof into k-space according to a coherent sampling technique, and reconstructing said image of said examination region with a linear reconstruction algorithm, as said reconstruction algorithm.

12. A method as claimed in claim 1 comprising using said criterion that minimizes motion artifacts in said reconstructed image to sort said data subsets in k-space into at least two motion status classes dependent on the respective motion values thereof.

13. A method as claimed in claim 12 wherein said sorting of said data subsets into at least two motion status classes produces a motion status class in which a majority of said magnetic resonance raw data are present, and using only the data subsets in said motion status class having said majority of said magnetic resonance raw data in said reconstruction algorithm.

14. A method as claimed in claim 12 comprising sorting said data subsets into said at least two motion status classes dependent on a quality criterion for said motion value, and selecting said data subsets for reconstructing said image in said reconstruction algorithm only from motion status classes that satisfy said quality criterion.

15. A method as claimed in claim 12 comprising selecting data subsets for use in reconstructing said image in said reconstruction algorithm from a motion status class, among said at least two motion status classes, dependent on said criterion that minimizes motion artifacts in said image, and from at least one motion status class, among said at least two motion status classes, that is within a predetermined permissible deviation from said criterion that minimizes motion artifacts in said image.

16. A method as claimed in claim 15 comprising, in said reconstruction algorithm, weighting magnetic resonance raw data in data subsets in said motion class that satisfies said criterion for reducing motion artifacts in said image more heavily than magnetic resonance raw data in data subsets in said motion status class having said predetermined permissible deviation from said motion criterion that minimizes motion artifacts in said image.

17. A method as claimed in claim 12 comprising selecting data subsets for use in reconstructing said image in said reconstruction algorithm from a motion status class, among said at least two motion status classes, dependent on said criterion that minimizes motion artifacts in said image, and from at least one motion status class, among said at least two motion status classes, that satisfies a correctability criterion representing a degree to which an effect of motion on the magnetic resonance raw data can be corrected.

18. A method as claimed in claim 17 comprising using, as said correctability criterion, a measure representing a degree to which a registration of a first intermediate image data set, derived from magnetic resonance raw data in data subsets in said motion status class that satisfies said criterion for reducing motion artifacts in said image, can be brought into registration, with a registration deviation that does not exceed a predetermined threshold, with a second intermediate image data set derived from magnetic resonance raw data in data subsets in said motion status class that satisfies said correctability criterion.

19. A method as claimed in claim 17 comprising, in said reconstruction algorithm, performing a motion correction at least on magnetic resonance

Assignees

• Siemens Ag

Inventors

• Meyer Heiko
• Raithel Esther

Classifications

• E04B1/38

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• E04B1/1903

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• E04B1/19

  Three-dimensional [3D] framework structures · CPC title

• E04H15/34

  Supporting means, e.g. frames · CPC title

• E04H15/00 Primary

  Tents or canopies, in general · CPC title