Correction of MR images for motion artifacts using navigator echoes and autocorrection

• US 6,184,682 B1
• Filed: 04/13/1999
• Issued: 02/06/2001
• Est. Priority Date: 04/15/1998
• Status: Expired due to Term

• Current Assignee: Mayo Foundation For Medical Education And Research (Mayo Clinic)
• Original Assignee: Mayo Foundation For Medical Education And Research (Mayo Clinic)
• Inventors: Manduca, Armando, McGee, Kiaran P., Ehman, Richard L., Felmlee, Joel P.
• Primary Examiner(s): Oda, Christine K.
• Assistant Examiner(s): Shrivastav, Brij B.
• Application Number:
  US09/290,817
• Time in Patent Office: 665 Days
• Field of Search: 324/309, 324/300, 324/307, 128/653
• US Class Current: 324/309
• CPC Class Codes: G06T 2207/10016 Video; Image sequence
• CPC Class Codes: G06T 2207/10088 Magnetic resonance imaging [MRI]
• CPC Class Codes: G06T 2207/20056 Discrete and fast Fourier transform, [DFT, FFT]
• CPC Class Codes: G06T 2207/30004 Biomedical image processing
• CPC Class Codes: G06T 2207/30168 Image quality inspection
• CPC Class Codes: G06T 5/10 using non-spatial domain filtering
• CPC Class Codes: G06T 5/50 using two or more images, e.g. averaging or subtraction
• CPC Class Codes: G06T 5/73 Deblurring; Sharpening

Abstract

Navigator echo signals are acquired during an MRI scan in which image data is acquired. The navigator echo signals are used to estimate an initial correction of the acquired image data for patient motion during the scan. The initially corrected image data is then autocorrected in an iterative process which measures image quality and makes further corrections to the image data until the measured image quality is within tolerance.

Claims

1. A method for correcting a medical image for artifacts, the steps comprising:

a) acquiring a navigator signal;

b) acquiring a series of views to form an image data set;

c) comparing the navigator signal with views in the image data set;

d) correcting the views in the image data set based on the comparison in step c);

e) reconstructing an image from the acquired image data set;

f) evaluating the quality of the reconstructed image by calculating a metric based on the image;

g) iteratively minimizing the metric by making further corrections to views in the image data set and repeating steps e) and f).

2. The method as recited in claim 1 in which the artifacts are caused by patient motion.

3. A method for correcting an MRI image for artifacts caused by patient motion, the steps comprising:

a) acquiring an NMR navigator signal which has phase information indicative of patient positioning;

b) acquiring a k-space image data set as a series of views;

c) comparing phase information in the NMR navigator signal with phase information in acquired views;

d) correcting views in the k-space image data set based on the comparison in step c);

e) reconstructing an MR image from the k-space image data set;

f) evaluating the quality of the MR image by calculating a metric based on the MR image;

g) iteratively minimizing the metric by making further corrections to views in the k-space image data set and repeating steps e) and f).

4. The method as recited in claim 3 in which each view is acquired with a pulse sequence which includes producing a phase encoding gradient directed along one axis and producing a readout gradient directed along an orthogonal axis, each of said views differs by the amount of the phase encoding gradient, and the NMR navigator signal is acquired with a pulse sequence which includes producing a readout gradient directed along said one axis.

5. The method as recited in claim 4 in which a plurality of NMR navigator signals are acquired and the steps include:

calculating the average of the acquired NMR navigator signals for use in step c).

6. The method as recited in claim 3 in which the phase of a signal sample in the NMR navigator signal is compared with the phase of a signal sample in each of said views.

7. The method as recited in claim 6 in which the correction of each view in step d) is based on the difference in phase of said signal samples.

8. The method as recited in claim 3 in which all the views in the k-space data set are compared in step c) and corrected in step d).

9. The method as recited in claim 3 in which calculating the metric in step f) includes calculating the MR image gradient.