SYSTEMS AND METHODS FOR THE AUTOMATED DETECTION OF CEREBRAL MICROBLEEDS USING 3T MRI

Abstract

Automated cerebral microbleed detection is performed in extracted T2*-weighted image data, including gradient echo (GRE) image data and susceptibility-weighted imaging (SWI) image data. The image data is resampled and potential 2D regions of interest (ROI) having a circular or ellipsoidal shape are identified based in part on a respective intensity of associated resampled image pixels. The number of 2D ROIs are reduced by size, edge, and/or cerebrospinal fluid (CSF) mask exclusion, and then merged to form 3D ROIs. False positive 3D ROIs are removed and the remaining ROIs stored for review by a trained rater. The embodiments of the present disclosure outperform visual ratings of cerebral microbleeds, reducing the time to visually rate the scans while retaining sensitivity to the microbleeds themselves. These embodiments also exhibit higher sensitivity in longitudinal identification of microbleed locations, and are suited to longitudinal examination of cerebrovascular disease, e.g., Alzheimer’s in adults with Down syndrome.

Description

Claims

1. A method for cerebral microbleed detection, the method comprising: acquiring, by a preprocessor module, magnetic resonance imaging (MRI) image data, the MRI image data comprising T1-weighted MRI image data and acquired T2*-weighted image data;extracting, by the preprocessor module, extracted T2*-weighted image data from the acquired T2*-weighted image data, the extracted T2*-weighted image data corresponding to gradient echo (GRE) image data or susceptibility-weighted imaging (SWI) image data;resampling, by the preprocessor module, the extracted T2*-weighted image data to a relatively higher resolution to yield resampled image data, the resampled image data comprising a plurality of slices;identifying, by a detection module, each potential microbleed location in each slice of the resampled image data based, at least in part, on a respective intensity of each of a plurality of resampled image pixels, each potential microbleed location corresponding to a potential region of interest (ROI) and having a circular or ellipsoidal shape to within a shape tolerance;reducing, by the detection module, a number of potential ROIs based, at least in part on at least one reduction criterion to yield a reduced number of potential two-dimensional (2D) ROIs;merging, by the detection module, the reduced number of 2D ROIs into at least one merged potential three dimensional (3D) ROI, the merging performed between a plurality of adjacent slices;defining, by the detection module, a standardized potential 3D ROI for each merged potential 3D ROI, each standardized potential 3D ROI having a respective 3D center and a surrounding neighborhood having a common size;removing, by a 3D geometric filtering module, each potential false positive 3D ROI from the at least one standardized potential 3D ROI based, at least in part, on at least one 3D ROI characteristic of each standardized potential 3D ROI, to yield a number of final potential 3D ROIs; andstoring, by a final module, each of the final potential 3D ROIs comprising a location and a volume, associated with the extracted T2*-weighted image data, for review by a trained rater.

2. The method of claim 1, further comprising: co-registering, by the preprocessor module, the T1-weighted MRI image data and an atlas-based lobar mask to the resampled image data to generate co-registered image data; anddetermining, by the preprocessor module, a cerebrospinal fluid (CSF) mask based, at least in part, on a co-registered T1-weighted image data.

3. The method of claim 1, wherein the identifying each potential microbleed location comprises determining a 2D image gradient, detecting each edge pixel, applying hysteresis thresholding, and detecting each potential ROI having the circular or ellipsoidal shape.

4. The method of claim 3, wherein determining the 2D image gradient is performed using a Sobel filter, each edge pixel is detected using Canny edge detection, and each potential ROI having the circular or ellipsoidal shape is detected using a Hough transform.

5. The method of claim 1, wherein the at least one reduction criterion includes discarding each potential ROI positioned on an edge of an image, merging a plurality of overlapping ROIs, excluding each ROI having a size greater than a threshold size, excluding each singular ROI, and excluding each ROI that overlaps a cerebrospinal fluid (CSF) mask.

6. The method of claim 1, wherein removing each potential false positive 3D ROI from the at least one standardized potential 3D ROI comprises determining a vesselness of all voxels contained within each standardized potential 3D ROI.

7. The method of claim 1, wherein the 3D ROI characteristic includes a 3D image entropy of a selected standardized potential 3D ROI, a 2D image entropy of a maximum intensity projection of the selected standardized potential 3D ROI, a volume of a central blob of the selected standardized potential 3D ROI, a compactness of the central blob of the selected standardized potential 3D ROI, or combinations thereof.

8. The method of claim 7, wherein the volume and compactness of the central blob are determined based, at least in part, on Frangi filtering.

9. The method of claim 1, further comprising determining, by the final module, a number of identified microbleeds, and generating, by the final module, a distribution of locations using a co-registered lobar mask.

10. A system for cerebral microbleed detection, the system comprising: a computing device comprising a processor, a memory, input/output circuitry, and a data store;a preprocessor module configured to acquire magnetic resonance imaging (MRI) image data, the MRI image data comprising T1-weighted MRI image data and acquired T2*-weighted image data;the preprocessor module further configured to extract extracted T2*-weighted image data from the acquired T2*-weighted image data, the extracted T2*-weighted image data corresponding to gradient echo (GRE) image data or susceptibility-weighted imaging (SWI) image data;the preprocessor module further configured to resample the extracted T2*-weighted image data to a relatively higher resolution to yield resampled image data, the resampled image data comprising a plurality of slices;a detection module configured to identify each potential microbleed location in each slice of the resampled image data based, at least in part, on a respective intensity of each of a plurality of resampled image pixels, each potential microbleed location corresponding to a potential region of interest (ROI) and having a circular or ellipsoidal shape to within a shape tolerance;the detection module further configured to reduce a number of potential ROIs based, at least in part on at least one reduction criterion to yield a reduced number of potential two-dimensional (2D) ROIs;the detection module further configured to merge the reduced number of 2D ROIs into at least one merged potential three dimensional (3D) ROI, the merging performed between a plurality of adjacent slices;the detection module further configured to define a standardized potential 3D ROI for each merged potential 3D ROI, each standardized potential 3D ROI having a respective 3D center and a surrounding neighborhood having a common size;a 3D geometric filtering module configured to remove each potential false positive 3D ROI from the at least one standardized potential 3D ROI based, at least in part, on at least one 3D ROI characteristic of each standardized potential 3D ROI, to yield a number of final potential 3D ROIs; anda final module configured to store each of the final potential 3D ROIs comprising a location and a volume, associated with the extracted T2*-weighted image data, for review by a trained rater.

11. The system of claim 10, wherein the preprocessor module is configured to co-register the T1-weighted MRI image data and an atlas-based lobar mask to the resampled image data to generate co-registered image data; and to determine a cerebrospinal fluid (CSF) mask based, at least in part, on a co-registered T1-weighted image data.

12. The system of claim 10, wherein removing each potential false positive 3D ROI from the at least one standardized potential 3D ROI comprises determining a vesselness of all voxels contained within each standardized potential 3D ROI.

13. The system of claim 10, wherein the 3D ROI characteristic includes a 3D image entropy of a selected standardized potential 3D ROI, a 2D image entropy of a maximum intensity projection of the selected standardized potential 3D ROI, a volume of a central blob of the selected standardized potential 3D ROI, a compactness of the central blob of the selected standardized potential 3D ROI, or combinations thereof.

14. The system of claim 10, wherein the final module is configured to determine a number of identified microbleeds, and to generate a distribution of locations using a co-registered lobar mask.

15. A computer readable storage device having stored thereon instructions that when executed by one or more processors result in the following operations comprising: acquiring magnetic resonance imaging (MRI) image data, the MRI image data comprising T1-weighted MRI image data and acquired T2*-weighted image data;extracting extracted T2*-weighted image data from the acquired T2*-weighted image data, the extracted T2*-weighted image data corresponding to gradient echo (GRE) image data or susceptibility-weighted imaging (SWI) image data;resampling the extracted T2*-weighted image data to a relatively higher resolution to yield resampled image data, the resampled image data comprising a plurality of slices;identifying each potential microbleed location in each slice of the resampled image data based, at least in part, on a respective intensity of each of a plurality of resampled image pixels, each potential microbleed location corresponding to a potential region of interest (ROI) and having a circular or ellipsoidal shape to within a shape tolerance;reducing a number of potential ROIs based, at least in part on at least one reduction criterion to yield a reduced number of potential two-dimensional (2D) ROIs;merging the reduced number of 2D ROIs into at least one merged potential three dimensional (3D) ROI, the merging performed between a plurality of adjacent slices;defining a standardized potential 3D ROI for each merged potential 3D ROI, each standardized potential 3D ROI having a respective 3D center and a surrounding neighborhood having a common size;removing each potential false positive 3D ROI from the at least one standardized potential 3D ROI based, at least in part, on at least one 3D ROI characteristic of each standardized potential 3D ROI, to yield a number of final potential 3D ROIs; andstoring each of the final potential 3D ROIs comprising a location and a volume, associated with the extracted T2*-weighted image data, for review by a trained rater.

16. The device of claim 15, wherein the instructions stored thereon that when executed by one or more processors result in the following operations comprising: co-registering the T1-weighted MRI image data and an atlas-based lobar mask to the resampled image data to generate co-registered image data; anddetermining a cerebrospinal fluid (CSF) mask based, at least in part, on a co-registered T1-weighted image data.

17. The device of claim 15, wherein the at least one reduction criterion includes discarding each potential ROI positioned on an edge of an image, merging a plurality of overlapping ROIs, excluding each ROI having a size greater than a threshold size, excluding each singular ROI, and excluding each ROI that overlaps a cerebrospinal fluid (CSF) mask.

18. The device of claim 15, wherein removing each potential false positive 3D ROI from the at least one standardized potential 3D ROI comprises determining a vesselness of all voxels contained within each standardized potential 3D ROI.

19. The device of claim 15, wherein the 3D ROI characteristic includes a 3D image entropy of a selected standardized potential 3D ROI, a 2D image entropy of a maximum intensity projection of the selected standardized potential 3D ROI, a volume of a central blob of the selected standardized potential 3D ROI, a compactness of the central blob of the selected standardized potential 3D ROI, or combinations thereof.

20. The device of claim 15, wherein the instructions stored thereon that when executed by one or more processors result in the following operations comprising: determining a number of identified microbleeds; and generating a distribution of locations using a co-registered lobar mask.