System and method for magnetic resonance imaging parametric mapping using confidence maps

• US 8,995,738 B2
• Filed: 02/18/2013
• Issued: 03/31/2015
• Est. Priority Date: 02/18/2013
• Status: Active Grant

• Current Assignee: Wisconsin Alumni Research Foundation (University of Wisconsin System)
• Original Assignee: Wisconsin Alumni Research Foundation (University of Wisconsin System)
• Inventors: Hernando, Diego, Reeder, Scott B.
• Primary Examiner(s): Carter, Aaron W
• Assistant Examiner(s): LEMIEUX, IAN L
• Application Number:
  US13/769,682
• Publication Number:
  US 20140233817A1
• Time in Patent Office: 771 Days
• US Class Current: 382/131
• CPC Class Codes: G01R 33/4828 Resolving the MR signals of different chemical species, e.g. water-fat imaging
• 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 33/565 Correction of image distortions, e.g. due to magnetic field inhomogeneities
• CPC Class Codes: G01R 33/56536 due to magnetic susceptibility variations
• CPC Class Codes: G06T 11/003 Reconstruction from projections, e.g. tomography

Abstract

A method for producing parametric maps using a magnetic resonance imaging (MRI) system is provided. The MRI system is used to acquire k-space data from a field-of-view. A series of images is reconstructed from the acquired k-space data, and a confidence map is produced using the k-space data. The confidence map depicts regions in the field-of-view that are affected by error sources. A parametric map is produced using the reconstructed series of images and the produced confidence map. Values in the parametric map associated with regions in the field-of-view depicted in the confidence map as being affected by error sources are not computed, thereby reducing errors in the parametric map.

Claims

1. A method for producing a parametric map using a magnetic resonance imaging (MRI) system, the steps of the method comprising:

a) acquiring k-space data from a subject positioned in a field-of-view using an MRI system;

b) reconstructing a series of images from the k-space data acquired in step a);

c) producing a parametric map using the series of images reconstructed in step b);

d) producing a confidence map from the k-space data, the confidence map depicting regions in the field-of-view that are affected by error sources; and

e) modifying the parametric map using the confidence map produced in step d) to identify regions in the parametric map that are affected by error sources.

2. The method as recited in claim 1 in which step c) includes producing the confidence map from the series of images reconstructed in step b) from the k-space data acquired in step a).

3. The method as recited in claim 2 in which the confidence map is produced in step c) by:

i) fitting the series of images reconstructed in step b) to a signal model to produce residual values indicative of a goodness of fit between the series of images and the signal model;

ii) identifying regions in the imaging field-of-view associated with residual values that exceed a defined threshold value; and

iii) assigning binary values in the confidence map to the regions identified in step c)ii).

4. The method as recited in claim 1 in which step e) includes producing an image mask using the confidence map and applying the image mask to the parametric map to mask regions that are affected by error sources.

5. The method as recited in claim 1 in which step e) includes displaying the confidence map produced in step c) as overlaid on the parametric map produced in step e).

6. The method as recited in claim 5 in which step e) includes color-coding the confidence map based values in the confidence map.

7. The method as recited in claim 1 in which the confidence map is produced in step c) by:

i) generating a field map indicative of magnetic field values in the field-of-view associated with a main magnetic field of the MRI system;

ii) identifying regions in the field map depicting magnetic field variations that are rapid relative to a spatial resolution of the acquired k-space data; and

iii) assigning binary values in the confidence map to the regions identified in step c)ii).

8. The method as recited in claim 1 in which the confidence map is produced in step c) by:

i) calculating a transverse relaxation rate, R*₂, in the field-of-view from the acquired k-space data;

ii) estimating at least one of a bias and a variance of the transverse relaxation rate for acquisition parameters used in step a) to acquire the k-space data; and

iii) assigning binary values in the confidence map to regions associated with at least one of estimated bias and estimated variance above a predetermined bound value.

9. The method as recited in claim 8 in which step c)ii) includes estimating a variance using a Cramer-Rao bound analysis.

10. A method for producing a parametric map using a magnetic resonance imaging (MRI) system, the steps of the method comprising:

a) acquiring k-space data from a subject positioned in a field-of-view using an MRI system;

b) reconstructing a series of images from the k-space data acquired in step a);

c) producing a confidence map from the k-space data, the confidence map depicting regions in the field-of-view that are affected by error sources;

d) producing a parametric map using the series of images reconstructed in step b) and the confidence map produced in step c);

wherein values in the parametric map associated with regions in the field-of-view depicted in the confidence map as being affected by error sources are not computed.

11. The method as recited in claim 10 in which step c) includes producing the confidence map from the series of images reconstructed in step b) from the k-space data acquired in step a).

12. The method as recited in claim 11 in which the confidence map is produced in step c) by:

i) fitting the series of images reconstructed in step b) to a signal model to produce residual values indicative of a goodness of fit between the series of images and the signal model;

ii) identifying regions in the imaging field-of-view associated with residual values that exceed a defined threshold value; and

iii) assigning binary values in the confidence map to the regions identified in step c)ii).