1. A Novel Approach To Spectral MRI Tiffany A. Fetzner* Advisor Joseph P. Hornak Rochester Institute of Technology May 8, 1998

2. What is Spectral MRI? Magnetic Resonance Imaging (MRI), is a diagnostic medical imaging technique based on the phenomenon of nuclear magnetic resonance (NMR), however the spectral information NMR provides, is usually lost in conventional MRI procedures. Spectral MRI attempts to recover and use this information.

3. 1) The spectral information from MRI is averaged, and presented as a single image. 2) MRI images, like most images are two-dimensional, but people are three- dimensional Problems with conventional MRI

4. Reason for Research Spectral data could provide a third dimension for tissue identification, but is traditionally difficult to obtain. What makes this project different? 1) Use of Variable Bandwidth Imaging 2) Use of Maximum Chemical Shift Artifacts 3) Use of a filtered Inverse Radon Transform

5. Research Goal: To determine the effectiveness of using an inverse Radon transformation, on a set of variable bandwidth (VBW), Magnetic Resonance (MR) images to obtain spectral tissue information.

6. Variable Bandwidth Imaging Allows a series of projections to be obtained at different angles Provides a 2nd Spatial Component Equivalent to obtaining projections through a spatial-spatial-spectral domain

7. Chemical Shift Artifact (CSA) High BW Med BW Low BW CSANo CSA Low CSA Hi

8. Overview: Project Algorithm 1 Original Input Data 2 Column Extraction 3 Back Projection (IRT) 4 Recomposition of Slices 5 Remapping of Data (x, y) 6 End Result of Data

9. Preliminary Research Tests Single images: 1) Comparing Filtered Inverse Radon Transform Results, for ideal situations A) Only possible angles used, with Repetition as needed B) Only possible angles, No repetition

10. Preliminary Research Tests Single images: 2) Comparing Filtered Inverse Radon Transform Results, for possible experimental situations A) Only possible angles used, with Repetition as needed B) Only possible angles, No repetition

11. Theo. Radon Transform (I 2 ) Difference Image (I 1 - I 3 )= (I 4 ) Theo. RT -1 Reconstruction (I 3 ) Original Test Image (I 1 ) Theo. Radon Transform (I 2 ) Theo. RT -1 Reconstruction (I 3 ) Difference Image (I 1 - I 3 )= (I 4 ) Original Test Image (I 1 ) Theoretical Tests Difference Image (I 1 - I 6 )= (I 7 ) Radon Transformation (I 5 ). RT -1 Reconstruction (I 6 ) Difference Image (I 4 - I 7 )= (I 8 ) Exp.. Radon Transformation (I 5 ) Difference Image (I 4 - I 7 )= (I 8 ) Exp... RT -1 Reconstruction (I 6 ) Difference Image (I 1 - I 6 )= (I 7 ) Experimental Tests

12. The total squared difference of image: (I 4 ) = 2.26898e+007 (I 7 ) = 3.12087e+008 (I 8 ) = 2.91537e+008 Test Image Statistics

13. Example of Test Results A)Using Joe Hornak’s Brain B)Using a single center pixel A B

14. A) Brain_images Repetition of possible angles Ideal RadonReconstruction Difference Experimental Radon ReconstructionDifference

15. Ideal RadonReconstruction Difference B) Brain_images NO Repetition of possible angles Experimental Radon ReconstructionDifference

16. Brain_images Repetition A) Difference Image Missing_brain_images No Repetition B)Difference Image

17. A) PSF of central Pixel With Repetition Ideal RadonReconstruction Difference Experimental Radon ReconstructionDifference

18. B) Missing PSF of central Pixel No Repetition Ideal RadonReconstruction Difference Experimental Radon ReconstructionDifference

19. PSF with Repetition A) Difference Psf_Repeat_possible Difference Image I7 Standard deviation I7: 5.49078e+007 Difference Image I8 Standard deviation I8: 12261.5 Difference Image I4: Standard deviation: 5.48986e+007 Missing PSF No Repetition Missing_Psf Difference Image I7 Standard deviation I7: 5.49029e+007 Difference Image I8 Standard deviation I8: 6209.17 Difference Image I4: Standard deviation: 5.48986e+007 B) Difference

20. Some Results Obtained A) For the brain images use of the useable experimental data with repetition produced a lower standard deviation across the image. B) For one pixel case, use of the useable experimental data without repetition produced a lower standard deviation across the image.

21. Conclusions 1) The feasibility of using this algorithm to extract spectral information from MRI images depends on how accurately images can be reconstructed, since the inverse radon transform is lossy.

22. Conclusions 2) The most effective reconstruction method applied to this inverse Radon Transform technique, depends on the Geometry of the object being imaged. Repetition seems to work best for complex objects.

23. Conclusions 3) Synthetic Variable bandwidth images have been generated, with CSA and final tests are still being concluded. 4) More work is still needed to verify the overall magnitude of how much spectral information can be extracted with this technique.