a novel approach to spectral mri tiffany a. fetzner* advisor joseph p. hornak rochester institute of...
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A Novel Approach To Spectral MRI
Tiffany A. Fetzner* Advisor Joseph P. Hornak
Rochester Institute of Technology
May 8, 1998
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.
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
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 Imaging2) Use of Maximum Chemical Shift Artifacts3) Use of a filtered Inverse Radon Transform
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 .
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
Chemical Shift Artifact (CSA)
High BW Med BW Low BW
CSANo CSA Low CSA Hi
Overview: Project AlgorithmOverview: Project Algorithm
1 Original Input Data1 Original Input Data2 Column Extraction2 Column Extraction3 Back Projection (IRT)3 Back Projection (IRT)4 Recomposition of Slices4 Recomposition of Slices5 Remapping of Data (x, y)5 Remapping of Data (x, y)6 End Result of Data6 End Result of Data
Preliminary Research Tests Single images:
1) Comparing Filtered Inverse Radon Transform Results, for ideal situations
A) Only possible angles used, withRepetition as needed
B) Only possible angles, No repetition
Preliminary Research Tests Single images:
2) Comparing Filtered Inverse Radon Transform Results, for possible experimental situations
A) Only possible angles used, withRepetition as needed
B) Only possible angles, No repetition
Theo. Radon Transform (I2)
Difference Image (I1- I3)= (I4)
Theo. RT-1 Reconstruction (I3)
Original Test Image (I1)
Theo. Radon Transform (I2)
Theo. RT-1 Reconstruction (I3)
Difference Image (I1- I3)= (I4)
Original Test Image (I1)
Theoretical Tests
Difference Image (I1- I6)= (I7)
Radon Transformation (I5)
. RT-1 Reconstruction (I6)
Difference Image (I4- I7)= (I8)
Exp.. Radon Transformation (I5)
Difference Image (I4- I7)= (I8)
Exp... RT-1 Reconstruction (I6)
Difference Image (I1- I6)= (I7)
Experimental Tests
The total squared difference of image: (I4) = 2.26898e+007(I7) = 3.12087e+008(I8) = 2.91537e+008
Test Image Statistics
Example of Test Results
A) Using Joe Hornak’s Brain
B) Using a single center pixel
A B
A) Brain_images Repetition of possible angles
Ideal Radon Reconstruction Difference
Experimental Radon Reconstruction Difference
Ideal Radon Reconstruction Difference
B) Brain_images NO Repetition of possible angles
Experimental Radon Reconstruction Difference
Brain_images Repetition
BBBRRRAAAIIINNN___000RRRIIIGGG
DDDIIIFFFFFFEEERRREEENNNCCCEEE IIIMMMAAAGGGEEE III777SSSTTTAAANNNDDDAAARRRDDD DDDEEEVVVIIIAAATTTIIIOOONNN III777::: 444...888777555444222EEE+++000000777DDDIIIFFFFFFEEERRREEENNNCCCEEE IIIMMMAAAGGGEEE III888SSSTTTAAANNNDDDAAARRRDDD DDDEEEVVVIIIAAATTTIIIOOONNN III888::: 444...000444000888777EEE+++000000666DDDIIIFFFFFFEEERRREEENNNCCCEEE IIIMMMAAAGGGEEE III444:::SSSTTTAAANNNDDDAAARRRDDD DDDEEEVVVIIIAAATTTIIIOOONNN::: 444...444666333111444EEE+++000000777
A) Difference ImageMissing_brain_images No Repetition
B)Difference Image
MMMiiissssssiiinnnggg___BBBrrraaaiiinnn___ooorrriiigggDDDiiiffffffeeerrreeennnccceee IIImmmaaagggeee III777SSStttaaannndddaaarrrddd dddeeevvviiiaaatttiiiooonnn III777::: 888...000555111444777eee+++000000777DDDiiiffffffeeerrreeennnccceee IIImmmaaagggeee III888SSStttaaannndddaaarrrddd dddeeevvviiiaaatttiiiooonnn III888::: 111...333000777111000eee+++000000777DDDiiiffffffeeerrreeennnccceee IIImmmaaagggeee III444:::SSStttaaannndddaaarrrddd dddeeevvviiiaaatttiiiooonnn::: 444...444666333111444eee+++000000777
A) PSF of central Pixel With Repetition
Ideal Radon Reconstruction Difference
Experimental Radon Reconstruction Difference
B) Missing PSF of central Pixel No Repetition
Ideal Radon Reconstruction Difference
Experimental Radon Reconstruction Difference
PSF with Repetition
A) Difference
Psf_Repeat_possibleDifference Image I7Standard deviation I7: 5.49078e+007Difference Image I8Standard deviation I8: 12261.5Difference Image I4:Standard deviation: 5.48986e+007
Missing PSF No Repetition
Missing_PsfDifference Image I7Standard deviation I7: 5.49029e+007Difference Image I8Standard deviation I8: 6209.17Difference Image I4:Standard deviation: 5.48986e+007
B) Difference
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 useableexperimental data without repetition produced a lower standard deviation across the image.
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.
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.
Conclusions3) 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.