a novel approach to spectral mri tiffany a. fetzner* advisor joseph p. hornak rochester institute of...

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.