Field-mapping and artifact correction in multispectral imaging

1. A method for generating a magnetic resonance image of an object in a magnetic resonance imaging (MRI) system, wherein the object contains at least one metallic implant, comprising:

providing from the MRI system multiple excitations of at least part of the object;

reading out through the MRI system image signals from the object;

saving in the MRI system the readout image signals as image data;

generating a field-map from the image data using a goodness-of-fit process which uses a goodness-of-fit metric, matched-filter, and/or similar fitting techniques to fit expected signals using bin-specific displacements from each excitation to the image data;

generating a blurry or low-resolution image from the image data;

generating a deblurred or high-resolution image from the image data, using the field-map to deblur the image by using a spectral image combination that weights each image on a voxel by voxel basis based on a weighted noise reduction function, wherein the high-resolution image has a higher resolution than the low-resolution image;

generating a weighted image from the blurry image and deblurred image based on a goodness-of-fit; and

displaying the weighted image.

2. The method, as recited in claim 1 , wherein the goodness-of-fit process uses both a goodness-of-fit metric and a matched-filter to fit expected signals from each excitation to the image data.

3. The method, as recited in claim 2 , wherein the goodness-of-fit process comprises:

generating a matched-filter field-map; and

using the matched-filter field-map to generate a goodness-of-fit field-map.

4. The method, as recited in claim 3 , wherein the goodness-of-fit metric is used with a weighted-combination deblurring algorithm.

5. The method, as recited in claim 4 , wherein the weighted-combination deblurring algorithm is a weighted average deblurring algorithm.

6. The method, as recited in claim 5 , wherein the providing from the MRI system multiple excitations of at least part of the object provides multiple excitations of multiple spectral bins.

7. The method, as recited in claim 6 , wherein the weighted- image is a multispectral image.

8. The method, as recited in claim 1 , wherein the weighted noise reduction function provides a weighting based on expected contribution of each image voxel from the field-map.

9. The method, as recited in claim 8 , wherein the weighted image is a weighted average image.

10. The method, as recited in claim 1 , wherein the goodness-of-fit process uses both a goodness-of-fit metric and a matched-filter to fit expected signals from each excitation to the image data.

11. The method, as recited in claim 1 , wherein the goodness-of-fit process comprises:

generating a matched-filter field-map; and

using the matched-filter field-map to generate a goodness-of-fit field-map.

12. The method, as recited in claim 1 , wherein the goodness-of-fit metric is used with a weighted-combination deblurring algorithm.

13. The method, as recited in claim 12 , wherein the weighted-combination deblurring algorithm is a weighted average deblurring algorithm.

14. A method for generating a magnetic resonance image of an object in a magnetic resonance imaging (MRI) system, wherein the object contains at least one metallic implant, comprising:

providing from the MRI system multiple excitations of at least part of the object;

reading out through the MRI system image signals from the object;

saving in the MRI system the readout image signals as image data;

generating a field-map from the image data using a goodness-of-fit process which uses a goodness-of-fit metric and/or a matched-filter to fit expected signals using bin-specific displacements from each excitation to the image data;

generating a blurry image from the image data;

generating a deblurred image from the image data, using the field-map to deblur the image by using a spectral image combination that weights each image on a voxel by voxel basis based on an expected contribution of each image voxel from the field-map;

generating a weighted average image from the blurry image and deblurred image based on a goodness-of-fit; and

displaying the weighted average image.

15. The method, as recited in claim 14 , wherein the goodness-of-fit process uses both a goodness-of-fit metric and a matched-filter to fit expected signals from each excitation to the image data.

16. The method, as recited in claim 15 , wherein the goodness-of-fit process comprises:

generating a matched-filter field-map; and

using the matched-filter field-map to generate a goodness-of-fit field-map.

17. The method, as recited in claim 15 , wherein the goodness-of-fit metric is used with a weighted-average deblurring algorithm.