METHOD AND MAGNETIC RESONANCE APPARATUS FOR DYNAMIC MAGNETIC RESONANCE IMAGING

• US 20080021304A1
• Filed: 07/13/2007
• Published: 01/24/2008
• Est. Priority Date: 07/21/2006
• Status: Active Grant

• Current Assignee: Siemens Healthcare GMBH (Siemens AG)
• Original Assignee: Siemens AG
• Inventors: Stemmer, Alto
• Granted Patent: US 8,073,522 B2
• Time in Patent Office: Days
• US Class Current: 600/410
• CPC Class Codes: G01R 33/4824 using a non-Cartesian trajectory
• 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 33/5614 using a fully balanced steady-state free precession [bSSFP] pulse sequence, e.g. trueFISP
• CPC Class Codes: G01R 33/56325 Cine imaging
• CPC Class Codes: G01R 33/5673 Gating or triggering based on a physiological signal other than an MR signal, e.g. ECG gating or motion monitoring using optical systems for monitoring the motion of a fiducial marker

Abstract

The invention concerns a method and magnetic resonance apparatus for acquisition and generation of a time-resolved image series of an anatomical organ with a quasi-periodical movement in a subject, k-space is sampled in segments with a number of partial data sets, with the sampling points of each partial data set corresponding to grid points of a Cartesian sampling grid of a k-space segment, and the Cartesian sampling grids of the k-space segments being rotated relative to one another. A series of sub-data sets is incompletely acquired for each partial data set using alternating sampling schemes. Each incomplete sub-data set is associated with one of the individual images. For at least some of the partial data sets, complete sub-data sets are reconstructed from the incomplete sub-data sets. For the reconstruction of the individual images, in each of the individual images, at least some of the complete sub-data sets that are associated with this individual image are used.

Claims

1. A method for acquisition and generation of a time-resolved image series comprised of a plurality of individual magnetic resonance images, said method comprising the steps of:

acquiring magnetic resonance data from a region of an examination subject containing an anatomical organ exhibiting quasi-periodical movement and entering said data into k-space by sampling k-space in segments with a plurality of partial data sets, with sampling points of each of said partial data sets corresponding to grid points of a Cartesian sampling grid in each k-space segment, and rotating the respective Cartesian sampling grids of the k-space segments relative to each other;

acquiring said magnetic resonance data by acquiring a plurality of incomplete sub-data sets for each of said partial data sets, with each incomplete sub-data set being respectively associated with one of said individual images, with the acquisition of the incomplete sub-data sets for a respective partial data set ensuing along the sampling grid of that partial data set with different, alternating sampling schemes, and sampling other grid points of the sampling grid of that partial data set with said different sampling schemes;

from at least some of the partial data sets, reconstructing a plurality of complete sub-data sets from the incomplete sub-data sets; and

reconstructing said individual images by, for each of said individual images, using at least some of the complete sub-data sets that are associated with that individual image.

2. A method as claimed in claim 1 comprising acquiring the incomplete data sets of a respective partial data set in immediate succession.

3. A method as claimed in claim 1 comprising acquiring the incomplete sub-data sets of a partial data set after a trigger point in time that characterizes the quasi-periodic movement of the anatomical organ.

4. A method as claimed in claim 1 comprising for each partial data set, sampling all grid points of the sampling grid associated with that partial data set for the entirety of all sub-data sets of that partial data set.

5. A method as claimed in claim 1 comprising for each partial data set, sampling all grid points of the sampling grid of that partial data set multiple times for the entirety of all sub-data sets of that partial data set.

6. A method as claimed in claim 1 comprising cyclically alternating said different sampling schemes for acquiring incomplete sub-data sets.

7. A method as claimed in claim 1 wherein each partial data set comprises a plurality of k-space lines, and wherein the step of sampling the grid points of the sampling grid for the respective partial data sets with different sampling schemes comprises employing A different sampling schemes with only every A^th k-space line of the sampling grid of the respective partial data set being sampled in each of said A different sampling schemes, and shifting the sampled k-space lines from sampling scheme-to-sampling scheme.

8. A method as claimed in claim 1 comprising acquiring the respective incomplete data sets with a plurality of coil elements each having a different spatial sensitivity, and using information characterizing the respective sensitivities of the individual coils for reconstructing the complete sub-data sets from said incomplete sub-data sets.

9. A method as claimed in claim 1 comprising reconstructing the respective complete sub-data sets from the incomplete sub-data sets using further complete auxiliary data sets associated with a central region of k-space.

10. A method as claimed in claim 1 comprising rotating the individual k-space segments relative to one another around a central point of k-space.

11. A method as claimed in claim 10 comprising automatically computationally determining auxiliary data sets from the respective incomplete sub-data sets, and reconstructing the respective complete sub-data sets from the incomplete sub-data sets using said further auxiliary data sets.

12. A magnetic resonance apparatus for acquisition and generation of a time-resolved image series comprised of a plurality of individual magnetic resonance images, comprising:

a magnetic resonance scanner operated by a control unit to acquire magnetic resonance data from a region of an examination subject containing an anatomical organ exhibiting quasi-periodical movement, with said control unit entering said data into k-space by sampling k-space in segments with a plurality of partial data sets, with sampling points of each of said partial data sets corresponding to grid points of a Cartesian sampling grid in each k-space segment, and rotating the respective Cartesian sampling grids of the k-space segments relative to each other;

said control unit operating said magnetic resonance scanner to acquire said magnetic resonance data by acquiring a plurality of incomplete sub-data sets for each of said partial data sets, with each incomplete sub-data set being respectively associated with one of said individual images, with the acquisition of the incomplete sub-data sets for a respective partial data set ensuing along the sampling grid of that partial data set with different, alternating sampling schemes, and sampling other grid points of the sampling grid of that partial data set with said different sampling schemes;

a computer that, for at least some of the partial data sets, reconstructs a plurality of complete sub-data sets from the incomplete sub-data sets; and

said computer reconstructing said individual images by, for each of said individual images, using at least some of the complete sub-data sets that are associated with that individual image.

13. A computer-readable medium encoded with a data structure loadable into a computer of a magnetic resonance apparatus, said data structure, when said computer-readable medium is loaded into said computer, causing said computer to operate said magnetic resonance apparatus to acquire and generate a time-resolved image series comprised of a plurality of individual magnetic resonance images of an anatomical organ of an examination subject exhibiting a quasi-periodical anatomical organ movement, by causing said computer to:

acquire magnetic resonance data from a region of an examination subject containing an anatomical organ exhibiting quasi-periodical movement and enter said data into k-space by sampling k-space in segments with a plurality of partial data sets, with sampling points of each of said partial data sets corresponding to grid points of a Cartesian sampling grid in each k-space segment, and rotating the respective Cartesian sampling grids of the k-space segments relative to each other;

acquire said magnetic resonance data by acquiring a plurality of incomplete sub-data sets for each of said partial data sets, with each incomplete sub-data set being respectively associated with one of said individual images, with the acquisition of the incomplete sub-data sets for a respective partial data set ensuing along the sampling grid of that partial data set with different, alternating sampling schemes, and sampling other grid points of the sampling grid of that partial data set with said different sampling schemes;

for at least some of the partial data sets, reconstruct a plurality of complete sub-data sets from the incomplete sub-data sets; and

reconstruct said individual images by, for each of said individual images, using at least some of the complete sub-data sets that are associated with that individual image.