1. Brain segmentation and Phase unwrapping in MRI data ECE 738 Project JongHoon Lee

2. Outline Nature of fast MRI: EPI & Field Inhomogeneity Nature of fast MRI: EPI & Field Inhomogeneity Background problem – Image Distortion Background problem – Image Distortion Specific problems Specific problems a) Brain Segmentation b) Phase Unwrapping Goal Goal Approach Approach Result Result Conclusion and future work Conclusion and future work

3. Nature of fast MRI: EPI EPI(Echo Planar Imaging) - Most common technique for fast MRI Magnetic Field - B(r) - Homogeneous w/o object : B(r) = const. Inhomogeneous - Inhomogeneous w/ object (e.g.head: bone, brain, air,…) > Due to magnetic susceptibility difference > EPI is sensitive to this  Geometrical Distortion in EPI image  x,  y : amounts of shift (misplacement) in x, y directions in mm FOVx, FOVy : field of view in x, y direction in mm  tx,  ty : sampling interval in x, y direction (  1/sampling rate)

4. Nature of fast MRI: EPI Geometric Distortion Example Significance - Misregistration of EPI and Anatomical image  Incorrect mapping of region of interest(ROI)  Need to be corrected using Fieldmap

5. Specific Problems MRI data = complex image data from Fourier transform Magnitude image Real image Phase image Imaginary image Fieldmap = Field inhomogeneity map = TE : Echo time - Peak signal time

6. Specific Problems Brain Segmentation Brain Segmentation x= Phase Unwrapping Phase Unwrapping I II Fieldmap Correction Scanner

7. Specific Problems Phase Unwrapping Phase Unwrapping True phase Wrapped phase in original phase data Wrapped phase with noise Unwrapped phase: True phase Unwrapped phase: Fooled by noise

8. Specific Problems Problem. 1 – Imperfect segmentation  Noise at boundary  Erroneous unwrapping by 1D conventional Unwrapping Phase Unwrapping Phase Unwrapping Problem. 2 – Islands  Erroneous unwrapping by slice by slice 2D based seed growing method Solution! - Solution! - Unwrapping from inside to outside: Seed growing Solution! – Solution! – 3D volume based Unwrapping: 3D Seed growing

9. Specific Problems Brain Segmentation Brain Segmentation Manual Method - time cost - time cost - requirement for sufficient training - requirement for sufficient training Automated Method - combination of image-processing techniques - combination of image-processing techniques thresholding | clustering | region growing thresholding | clustering | region growing edge detection | morphological | surface modeling edge detection | morphological | surface modeling - Seeded region growing algorithm based method - Seeded region growing algorithm based method - Histogram - Morphology based method - Histogram - Morphology based method - Deformable surface modeling - Deformable surface modeling Problem – All the segmentation method is intensity dependent  May cause problem with phase map data Solution? – Segmentation using phase map data

10. Goal Simultaneous Phase unwrapping & Segmentation of brain area by assuming smoothly varying phase in brain for Fieldmap correction of fast MRI(EPI)

11. Approach 3D seed growing unwrapping guided by noise-pole field. Based on papers by R. Cusack et.al. & Sofia et.al. 1)Generate pole field 2)Modify the pole field with initial thresholded mask  Noise-pole field 3)Find purest point in the center of 3D brain data  Seed 4)Merge or unwrap adjacent pixels  Seed growing 5)Repeat with new, increased threshold  Iteration Unwrapped phase map 6)Stop at the final threshold  Unwrapped phase map Mask data (Segmentation) 7)Set nonzero brain area to ‘1’  Mask data (Segmentation) Geometric correction of EPI 8)Make fieldmap from two phase maps  Geometric correction of EPI

12. Approach Details Generate: Noise-Pole field A A’ A A’ A = A’ A  A’ A  A’  Pole Phase Map Pole Field Initial Mask Noise-Pole Field Unique point! Noise-pole field Computationally expensive segmentation is unnecessary

13. Approach Details Iterative seed growing: recursive algorithm Implemented in Matlab Easy to visualize/control image data Poor to deal with recursive algorithm!  ‘Out of Memory!’ Problem Converting to ‘C’ didn’t work Repeating ‘for loops’ in a recursive function  Increased speed and solved memory problem ex) 20 repetition in a function reduced time 1/3 Unwrapping from less noisy area to more noisy area

14. Results.1 Phase Unwrapping Phase Unwrapping

15. Results.2 Mask from new wayMask from High complexity segmentation (BET, Stephen M. Smith) MRI intensity images Brain Segmentation Brain Segmentation

16. Results.3 Undistortion

17. Conclusion & Future Work First work of brain segmentation using phase data  Phase only segmentation is possible research area Reduce complexity of whole procedure of fieldmap generation  Unwrapping and segmentation are executed at the same time Has not applied to other applications Smoothing and threshold parameters are to be chosen carefully Narrow areas tends to be eroded by smoothing Implementation in other language for faster operation