LOW-FIELD MRI TEXTURE ANALYSIS

1 . A method of identifying a region of interest using a low-field magnetic resonance imaging (MRI) system, the method comprising:

obtaining a T2-weighted image from the low-field MRI system, wherein the T2-weighted image comprises a slice;

annotating a first region on the slice, wherein the first region corresponds to a suspicious region;

annotating a second region on the slice, wherein the second region corresponds to a non-suspicious region, and wherein the second region comprises the same size as the first region;

computing a first texture feature value for the first region;

computing a second texture feature value for the second region; and

comparing the first texture feature value to the second texture feature value.

2 . The method of claim 1 , wherein the first texture feature value and the second texture feature value correspond to a Haralick texture feature selected from a group consisting of Energy, Homogeneity, Contrast, and Correlation.

3 . The method of claim 1 , further comprising generating a graphical representation comparing the first texture feature value to the second texture feature value.

4 . The method of claim 1 , further comprising:

computing a plurality of first texture feature values for the first region; and

computing a plurality of second texture feature values for the second region, wherein the plurality of first texture feature values and second texture feature values correspond to Haralick texture features selected from a group consisting of Energy, Homogeneity, Contrast, and Correlation.

5 . The method of claim 1 , further comprising generating a gray level co-occurrence matrix for the slice.

6 . The method of claim 5 , wherein the gray level co-occurrence matrix is calculated with between 4 and 256 bins.

7 . The method of claim 5 , further comprising generating a texture map from the gray level co-occurrence matrix.

8 . The method of claim 7 , wherein computing the first texture feature value comprises calculating an average first value using a sliding window technique in the gray level co-occurrence matrix.

9 . The method of claim 8 , wherein the sliding window technique comprises a sliding window size between 5 by 5 pixels and 49 by 49 pixels, and wherein the sliding window technique further comprises a sliding window stride between one and ten pixels.

10 . A system, comprising:

a single-sided, low-field MRI system comprising an array of magnets configured to generate a permanent, non-uniform BO magnetic field in a region of interest offset from the array of magnets;

a control circuit configured to:

generate a T2-weighted image from the single-sided, low-field MRI;

identify a first region on the T2-weighted image, wherein the first region corresponds to a suspicious region;

identify a second region on the T2-weighted image, wherein the second region corresponds to a non-suspicious region, and wherein the second region comprises the same size as the first region;

compute a first texture feature value for the first region;

compute a second texture feature value for the second region; and

compare the first texture feature value to the second texture feature value; and

a display configured to convey the comparison of the first texture feature value to the second texture feature value.

11 . The system of claim 10 , wherein the single-sided, low-field MRI system further comprises a housing comprising a face, wherein a first axis extends through the face into the region of interest, and wherein the permanent, non-uniform B0 magnetic field extends from the array of permanent magnets relative to the first axis into the region of interest.

12 . The system of claim 10 , wherein the permanent, non-uniform B0 magnetic field comprises a magnetic field strength of less than 100 mT in the region of interest.

13 . The system of claim 10 , wherein the permanent, non-uniform B0 magnetic field comprises a magnetic field strength between 58 mT and 74 mT in the region of interest.

14 . The system of claim 10 , wherein the single-sided, low-field MRI system further comprises:

a gradient coil set;

at least one radio frequency coil;

a power circuit; and

a memory;

wherein the control circuit is in signal communication with the gradient coil set, the at least one radio frequency coil, the power circuit, and the memory.

15 . The system of claim 10 , wherein the first texture feature value and the second texture feature value correspond to a Haralick texture feature selected from a group consisting of Energy, Homogeneity, Contrast, and Correlation.

16 . The system of claim 10 , wherein the control circuit is further configured to:

compute a plurality of texture feature values for the first region; and

compute a plurality of texture feature values for the second region, wherein the plurality of texture feature values correspond to Haralick texture features selected from a group consisting of Energy, Homogeneity, Contrast, and Correlation.

17 . The system of claim 10 , wherein the comparison comprises a graphical representation.

18 . The system of claim 10 , wherein the control circuit is further configured to generate a gray level co-occurrence matrix, and wherein the gray level co-occurrence matrix is calculated with between 4 and 256 bins.

19 . The system of claim 18 , wherein the control circuit is further configured to:

generate a texture map from the gray level co-occurrence matrix; and

calculate an average first value using a sliding window technique in the gray level co-occurrence matrix, wherein the sliding window technique comprises a sliding window size between 5 by 5 pixels and 49 by 49 pixels, and wherein the sliding window technique further comprises a sliding window stride between one and ten pixels.