Concurrent fat and iron estimation in magnetic resonance signal data

• US 9,157,975 B2
• Filed: 10/24/2013
• Issued: 10/13/2015
• Est. Priority Date: 10/24/2012
• Status: Active Grant

• Current Assignee: Siemens Healthineers AG (Siemens AG)
• Original Assignee: Siemens Medical Solutions USA Incorporated (Siemens AG)
• Inventors: Dale, Brian
• Primary Examiner(s): Luong, Peter
• Application Number:
  US14/061,798
• Publication Number:
  US 20140114178A1
• Time in Patent Office: 719 Days
• Field of Search: 600/407, 600/410
• US Class Current: 1/1
• CPC Class Codes: A61B 5/055 involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
• CPC Class Codes: G01R 33/4828 Resolving the MR signals of different chemical species, e.g. water-fat imaging
• CPC Class Codes: G01R 33/485 based on chemical shift information [CSI] or spectroscopic imaging, e.g. to acquire the spatial distributions of metabolites
• CPC Class Codes: G01R 33/50 based on the determination of relaxation times , e.g. T1 measurement by IR sequences; T2 measurement by multiple-echo sequences
• 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

Abstract

A computer-implemented method for concurrently estimating the amount of fat and iron in anatomical tissue from magnetic resonance (MR) signal data includes receiving a test signal representative of the anatomical tissue acquired using a MR pulse sequence type. A repository of reference signal data is generated. The repository comprises a plurality of reference signals derived by an MR signal simulation for a plurality of different transverse relaxation rates, a plurality of different fat fractions, and the MR pulse sequence type. A first reference signal is identified in the plurality of reference signals. The first reference signal provides a best match to the test signal based on one or more matching criteria. The repository is searched to determine a first transverse relaxation rate and a first fat fraction associated with the first reference signal. Then, the amount of fat and iron in the anatomical tissue is estimated based on the first transverse relaxation rate and the first fat fraction.

Claims

1. A computer-implemented method for concurrently estimating an amount of fat and iron in anatomical tissue from magnetic resonance (MR) signal data, the method comprising:

receiving a test signal representative of the anatomical tissue acquired using a MR pulse sequence type;

generating a repository of reference signal data comprising a plurality of reference signals derived by an MR signal simulation for a plurality of different transverse relaxation rates, a plurality of different fat fractions, and the MR pulse sequence type;

identifying a first reference signal in the plurality of reference signals, the first reference signal providing a best match to the test signal based on one or more matching criteria;

searching the repository to determine a first transverse relaxation rate and a first fat fraction associated with the first reference signal; and

estimating the amount of fat and iron in the anatomical tissue based on the first transverse relaxation rate and the first fat fraction.

2. The method of claim 1, wherein the MR signal simulation includes application of a Bloch function for magnetic resonance.

3. The method of claim 1, wherein the one or more matching criteria comprise a measure of signal similarity determined by a mathematical product calculation.

4. The method of claim 3, wherein identifying the first reference signal in the plurality of reference signals, the first reference signal providing the best match to the test signal based on one or more matching criteria, comprises:

for each of the plurality of reference signals, calculating a mathematical product value of the test signal with the respective reference signal; and

determining that the first reference signal has a maximum mathematical product value among the plurality of reference signals.

5. The method of claim 1, further comprising:

generating a plurality of magnetic field offset values corresponding to the different transverse relaxation rates; and

storing the plurality of magnetic field offset values in the repository of reference signal data.

6. The method of claim 1, further comprising:

generating an incoherent set of echo times for use in acquiring the reference signals; and

deriving the reference signals by the MR signal simulation at the incoherent set of echo times.

7. The method of claim 6, wherein receiving the test signal representative of the anatomical tissue acquired using the pulse sequence type comprises:

acquiring the test signal at the set of echo times.

8. A system for concurrently estimating an amount of fat and iron in anatomical tissue from magnetic resonance (MR) signal data, the system comprising:

an input processor configured to receive a test signal representative of the anatomical tissue acquired using a MR pulse sequence type;

a repository of reference signal data comprising a plurality of reference signals derived by MR signal simulation for a plurality of different transverse relaxation rates, a plurality of different fat fractions, and the MR pulse sequence type;

an image data processor configured to;

identify a first reference signal in the plurality of reference signals providing a best match to the test signal based on one or more matching criteria,

search the repository to determine a first transverse relaxation rate and a first fat fraction associated with the first reference signal, and

estimate the amount of fat and iron in the anatomical tissue based on the first transverse relaxation rate and the first fat fraction.

9. The system of claim 8, the image data processor identifies the first reference signal in the plurality of reference signals providing the best match to the test signal based on one or more matching criteria by a matching process comprising:

for each of the plurality of reference signals, calculating a mathematical product value of the test signal with the respective reference signal; and

determining that the first reference signal has a maximum mathematical product value among the plurality of reference signals.

10. The system of claim 8, further comprising:

a simulation processor configured to generate the plurality of reference signals.

11. The system of claim 10, wherein the simulation processor generates the plurality of reference signals using a Bloch function.

12. A system according to claim 10, wherein the simulation processor is further configured to generate a range of fat fraction values for corresponding different transverse relaxation rates.

13. A system according to claim 12, wherein the simulation processor is further configured to generate a plurality of magnetic field offset values for corresponding different transverse relaxation rates.

14. The system of claim 10, wherein the simulation processor is further configured to:

generate an incoherent set of echo times for use in acquiring the reference signals; and

generate the repository of reference signal data at the incoherent set of echo times using a signal model.

15. A computer-implemented method for concurrently estimating fat and iron in anatomical tissue using magnetic resonance (MR) signal data, the method comprising:

receiving, by an image processing computer, MR signal data representative of a portion of patient anatomy;

generating, by the image processing computer, a library of reference signal entries, each reference signal entry comprising a reference signal, an associated transverse relaxation value, and an associated fat fraction value;

receiving, by the image processing computer, a test signal;

for each reference signal entry in the library of reference signal entries, calculating an inner product value of the test signal and the respective reference signal entry'"'"'s corresponding reference signal;

identifying, by the image processing computer, a first reference signal entry associated with a maximum inner product value; and

estimating, by the image processing computer, a test transverse relaxation rate value and a test fat fraction value based on a first associated transverse relaxation value and a first associated fat fraction value corresponding to the first reference signal entry.

16. The method of claim 15, wherein generating the library of reference signals comprises:

receiving, by the image processing computer, an MR pulse sequence;

selecting, by the image processing computer, a range of transverse relaxation rate values; and

for each transverse relaxation value in the range of transverse relaxation rate values, performing an iterative process comprising;

selecting, by the image processing computer, a plurality of fat fraction values,

performing a MR simulation to generate a reference signal for the respective

transverse relaxation value and each fat fraction value, and

creating a plurality of entries in the library of reference signals, each entry comprising the generated reference signal, the respective transverse relaxation value, and one of the plurality of fat fraction values.

17. The method of claim 16, wherein the range of transverse relaxation rate values comprises a plurality of transverse relaxation rate values ranging from 0 Hz to 600 Hz in increments of 5 Hz.

18. The method of claim 15, wherein generating the library of reference signals comprises:

receiving, by the image processing computer, an MR pulse sequence;

selecting, by the image processing computer, a range of fat fraction values; and

for each fat fraction value in the range of fat fraction values, performing an iterative process comprising;

selecting, by the image processing computer, a plurality of transverse relaxation values,

performing a MR simulation to generate a reference signal for each transverse relaxation value and the respective fat fraction value, and

creating plurality of entries in the library of reference signals, each entry comprising the generated reference signal, one of the transverse relaxation values, and the respective fat fraction value.

19. The method of claim 18, wherein the range of fat fraction values comprises a plurality of fat fraction values ranging from 0% to 90% in increments of 1%.

20. The method of claim 15, wherein each reference signal entry in the library of reference signal entries further comprises one or more magnetic field offset values.