Method and magnetic resonance apparatus for determining diffusion parameters

Patent metadata

Field	Value
Publication number	US-10613183-B2
Application number	US-201815958325-A
Country	US
Kind code	B2
Filing date	Apr 20, 2018
Priority date	Apr 21, 2017
Publication date	Apr 7, 2020
Grant date	Apr 7, 2020

Abstract

In a magnetic resonance (MR) method and apparatus, MR signals are acquired in multiple diffusion measurements using a defined set of parameters in the respective measurements that differ in terms of at least one MR parameter from measurement-to-measurement, thereby producing a measured MR signal dataset. Multiple calculated datasets are calculated using a model, with a defined number of model parameters in each calculated dataset, but in different combinations, with each calculated dataset having an MR signal intensity. The measured MR signal having a closest match to the measured MR signal dataset, using a quality criterion based on the MR signal intensity, is identified, and the diffusion parameter is obtained from the calculated dataset having the closest match.

First claim

The invention claimed is:

1. A method for determining at least one diffusion parameter for an examination subject using magnetic resonance (MR) signals, comprising:
operating an MR data acquisition scanner in order to execute a scan of a subject to acquire MR signals in each of a plurality of diffusion measurements, wherein each diffusion measurement is implemented using a defined set of MR parameters, with the MR parameters in the respective defined sets differing from each other in terms of at least one MR parameter, thereby creating a measured MR signal dataset comprising the MR signals respectively acquired with the different sets of MR parameters;
in a computer, determining a plurality of calculated datasets using a model, said model describing a change in the MR signals for said different diffusion measurements, with a defined number of diffusion model parameters being used, but in respectively different combinations, with the respective combinations producing a calculated MR signal intensity for the respective diffusion measurements;
in said computer, comparing the measured MR signal dataset with the plurality of the calculated datasets in order to identify a calculated dataset that has a closest match to the measured MR signal dataset based on a quality criterion that is dependent on said MR signal intensity; and
in said computer, determining at least one diffusion parameter from the calculated dataset having the closest match to the measured MR signal dataset, from the model parameters used to determine the calculated dataset having said closest match.

2. The method as claimed in claim 1 , comprising generating a plurality N of MR diffusion images, each comprised of pixels, from the MR signals, for which images the signal intensity over time in the individual pixels of the MR diffusion images is used in order to create a measured MR signal data vector for each pixel,
wherein the measured MR signal data vector comprises the measured signal intensities for the different MR parameters used, which were employed in the various diffusion measurements.

3. The method as claimed in claim 2 , comprising performing said N diffusion measurements using N different sets of the MR parameters,
wherein for a defined combination of the model parameters the signal intensity is calculated for the N sets of the MR parameters in order to create a calculated data vector for each defined combination of the model parameters and the N values used for the MR parameters,
wherein the calculated data vector is determined for all possible combinations of the values of the model parameters in order to determine a plurality of different calculated data vectors, and
wherein each calculated data vectors comprises the MR signal intensities for the N diffusion measurements when using the defined combination of the model parameters.

4. The method as claimed in claim 3 , comprising identifying the calculated dataset that has the closest match to the measured MR signal dataset, by comparing the measured MR signal data vector with the plurality of different calculated data vectors, and the calculated data vector is determined that has the closest match to the measured MR signal data vector.

5. The method as claimed in claim 4 , comprising normalizing both the measured MR signal dataset and the multiplicity of the calculated datasets before the comparison.

6. The method as claimed in claim 1 , comprising, during the acquisition of the plurality of diffusion measurements, at least different diffusion weightings are used as the different MR parameters,
wherein the MR signal intensity is calculated for each diffusion weighting and the possible combinations of the different model parameters and of the different values.

7. The method as claimed in claim 1 , wherein the different model parameters are at least one model parameter from the group consisting of a magnetization that is produced without diffusion weighting, an apparent diffusion coefficient, and a diffusion tensor.

8. The method as claimed in claim 7 , wherein the different model parameters comprise said diffusion tensor, and
wherein for the diffusion measurements at least three different diffusion measurements are performed using different mutually orthogonal diffusion directions.

9. The method as claimed in claim 8 , wherein the magnetization that is produced without diffusion weighting and six components of the diffusion tensor are used as the model parameters, and
wherein for the calculated dataset having the greatest similarity, the trace of the diffusion tensor is used to determine a mean isotropic diffusion as the diffusion parameter.

10. The method as claimed in claim 8 , wherein the magnetization that is produced without diffusion weighting and six components of the diffusion tensor are used as the model parameters,
wherein diffusion measurements are performed using at least two different diffusion weightings and at least 6 different diffusion directions, and
wherein, for the calculated dataset having the greatest similarity, all the tensor components of the diffusion tensor are determined in order to determine an anisotropic diffusion tensor as the diffusion parameter.

11. The method as claimed in claim 1 , comprising, in the plurality of diffusion measurements, an associated raw data space is fully sampled in accordance with the Nyquist condition.

12. A magnetic resonance (MR) apparatus comprising:
an MR data acquisition scanner;
a computer configured to operate said MR data acquisition scanner in order to acquire MR signals in each of a plurality of diffusion measurements,
wherein each diffusion measurement is implemented using a defined set of MR parameters, with the MR parameters in the respective defined sets differing from each other in terms of at least one MR parameter, thereby creating a measured MR signal dataset comprising the MR signals respectively acquired with the different sets of MR parameters;
said computer being configured to determine a plurality of calculated datasets using a model, said model describing a change in the MR signals for said different diffusion measurements, with a defined number of diffusion model parameters being used, but in respectively different combinations, with the respective combinations producing a calculated MR signal intensity for the respective diffusion measurements;
said computer being configured to compare the measured MR signal dataset with the plurality of the calculated datasets in order to identify a calculated dataset that has a closest match to the measured MR signal dataset based on a quality criterion that is dependent on said MR signal intensity; and
said computer being configured to determine at least one diffusion parameter from the calculated dataset having the closest match to the measured MR signal dataset, from the model parameters used to determine the calculated dataset having said closest match.

13. A non-transitory, computer-readable data storage medium encoded with programming instructions, said storage medium being loaded into a computer of a magnetic resonance (MR) apparatus, and said programming instructions causing said computer to:
operate an MR data acquisition scanner in order to acquire MR signals in each of a plurality of diffusion measurements, wherein each diffusion measurement is implemented using a defined set of MR parameters, with the MR parameters in the respective defined sets differing from each other in terms of at least one MR parameter, thereby creating a measured MR signal dataset comprising the MR signals respectively acquired with the different sets of MR parameters;
determine a plurality of calculated datasets usi

Assignees

• Siemens Healthcare Gmbh

Inventors

• Huwer Stefan
• Feiweier Thorsten

Classifications

• G01R33/5608

  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 title

• G01R33/56341 Primary

  Diffusion imaging · CPC title