Automatic detection and correction of magnetic resonance imaging data

1. A method for processing magnetic resonance imaging (MRI) data, comprising:

receiving MRI data comprising a plurality of k-space points for an image data set;

deriving a plurality of different versions of the image data set based on the MRI data, each of the plurality of different versions of the image set obtained by zeroing a different one of the plurality of k-space points;

computing image space metric values for each of the plurality of different versions of the image data set;

identifying zeroed ones of the plurality of k-space points for each of the plurality of different versions of the image data set for which the at least one image space metric value fails to meet a threshold value; and

adjusting a portion of the MRI data associated with the identified zeroed ones of the plurality of k-space points to yield adjusted MRI data.

2. The method of claim 1 , further comprising:

generating a final image from the adjusted MRI data.

3. The method of claim 1 , wherein the adjusting comprises setting values of each of the k-space points in the portion of the MRI data to zero.

4. The method of claim 1 , wherein the adjusting comprises computing new values of each of the k-space points in the portion of the MRI data using a constrained reconstruction.

5. The method of claim 1 , wherein the image space metric values comprise total variation values, and wherein the adjusting further comprises identifying the portion of the MRI data by:

sorting the image space metric values to yield sorted total variation values;

defining a function of the sorted total variation values as a function of a logarithm of sorted position;

selecting a critical image space metric value associated with the shoulder of the function closest to the median as the threshold; and

selecting as the portion of the MRI data, at least a portion of the sorted image space metric below the threshold.

6. The method of claim 5 , wherein defining the function further comprises:

reversing the sorted total variation values; and

discarding values greater than a median of the sorted total variation values.

7. The method of claim 1 , wherein the adjusting further comprises defining the threshold value by:

discarding a portion of the image space metric values greater than a median of the image space metric values to yield remaining image space metric values;

computing a class threshold for the remaining image space metric values;

selecting as the portion of the MRI data, at least a portion of the sorted image space metric below the class threshold.

8. A system for processing magnetic resonance imaging (MRI) data, comprising:

a processor;

a computer-readable medium comprising a plurality of instructions for causing the processor to perform the method comprising:

receiving MRI data comprising a plurality of k-space points for an image data set;

deriving a plurality of different versions of the image data set based on the MRI data, each of the plurality of different versions of the image set obtained by zeroing a different one of the plurality of k-space points;

computing image space metric values for each of the plurality of different versions of the image data sets;

identifying zeroed ones of the plurality of k-space points for each of the plurality of different versions of the image data set for which the at least one image space metric value fails to meet a threshold value; and

adjusting a portion of the MRI data associated with ones of the image space metric values that fail to meet a threshold value to yield adjusted MRI data.

9. The system of claim 8 , the computer readable medium further comprising instruction for causing the processor to generate a final image from the adjusted MRI data.

10. The system of claim 8 , wherein the adjusting comprises setting values of each of the k-space points in the portion of the MRI data to zero.

11. The system of claim 8 , wherein the adjusting comprises computing new values of each of the k-space points in the portion of the MRI data using a constrained reconstruction.

12. The system of claim 8 , wherein the adjusting further comprises identifying the portion of the MRI data by:

sorting the image space metric values to yield sorted image space metric values;

defining a function of the sorted image space metric values as a function of the logarithm of sorted position;

selecting a critical image space metric value associated with the shoulder of the function closest to the median as the threshold; and

selecting as the portion of the MRI data, at least a portion of the sorted image space metric below the threshold.

13. The system of claim 12 , wherein defining the function further comprises:

reversing sorted image space metric values; and

discarding values greater than a median of the sorted image space metric values.

14. The system of claim 8 , wherein the adjusting further comprises defining the threshold value by:

discarding a portion of the image space metric values greater than a median of the image space metric values to yield remaining image space metric values;

computing a class threshold for the remaining image space metric values;

selecting as the portion of the MRI data, at least a portion of the sorted image space metric below the class threshold.

15. A non-transitory computer-readable medium having stored thereon a plurality of instructions for causing a computing device to perform a method for processing magnetic resonance imaging (MRI) data, the plurality of instructions comprising code sections for:

receiving MRI data comprising a plurality of k-space points for an image data set;

deriving a plurality of different versions of the image data set based on the MRI data, each of the plurality of different versions of the image set obtained by zeroing a different one of the plurality of k-space points;

computing image space metric values for each of the plurality of different versions of the image data set;

adjusting a portion of the MRI data associated with the identified zeroed ones of the plurality of k-space points to yield adjusted MRI data.

16. The non-transitory computer-readable medium of claim 15 , further comprising code sections for:

generating a final image from the adjusted MRI data.

17. The non-transitory computer-readable medium of claim 15 , wherein the adjusting comprises setting values of each of the k-space points in the portion of the MRI data to zero.

18. The non-transitory computer-readable medium of claim 15 , wherein the adjusting comprises computing new values of each of the k-space points in the portion of the MRI data using a constrained reconstruction.

19. The non-transitory computer-readable medium of claim 15 , wherein the adjusting further comprises identifying the portion of the MRI data by:

sorting the image space metric values to yield sorted image space metric values;

defining a function of the sorted image space metric values as a function of the logarithm of sorted position;

selecting a critical image space metric value associated with the shoulder of the function closest to the median as the threshold; and

selecting as the portion of the MRI data, at least a portion of the sorted image space metric below the threshold.

20. The non-transitory computer-readable medium of claim 15 , wherein the adjusting further comprises defining the threshold value by:

discarding a portion of the image space metric values greater than a median of the image space metric values to yield remaining image space metric values;

computing a class threshold for the remaining image space metric values;

selecting as the portion of the MRI data, at least a portion of the sorted image space metric below the class threshold.