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Stent Artefact Reduction for Vascular Imaging
in Micro Computed Tomography
Lydia Jim | Supervised by Dr. John Fenner and Dr. Andrew Narracott | Department of Cardiovascular Science
1. Background and Motivation
Stents are metal cylindrical structures used to
prevent localized blood flow constriction within
blood vessels.
Micro computed tomography (CT), a
non-destructive imaging technique, is
used to image isolated vessels embedded with stents.lll
The process involves the attenuation of
poly-energetic x-ray photons by an object from
multiple viewing angles. This data is used to
reconstruct an image.lll
In micro CT of stents, the highly attenuating
metal stent struts significantly modifies the
x-ray spectrum.lll
Streak artefacts are formed when spectral
modifications are not taken into account for image
reconstruction.lll
This project is concerned with reducing the effects of
artefacts in the reconstructed image. Computationally
corrective methods are used to improve the
reconstruction of the biological stent specimens.
This can improve biological interpretation.
A deployed stent.
Cropped from original by
BruceBlaus/CC-BY-3.
CT image of a stent in
a wax phantom
compromised by streak
artefacts
1. Assign energy
dependent
attenuation
co-efficients to
each pixel in the
test image
containing
artefacts.
2. Pass
poly-energetic
x-ray photon
spectrum
through test
image.
4. Convert the
poly-energetic
sinogram from
photon flux to
an attenuation
sinogram.
5. Apply a radon
transform to the
test image
containing artefacts
(1) to obtain a
mono-energetic
sinogram of the
test image.
7. Mutiply the
mono-energetic
sinogram from step 5
by the correction
array from step 6 to
obtain a corrected
sinogram.
8. Back-project the
corrected sinogram
to reconstruct the
corrected image.
Iterate steps 1 to 8
as necessary to
further minimize
artefacts.
2. Methods
& Results
3. Create a
poly-energetic
sinogram with
mutiple viewing
angles.
÷
6. Divide the
mono-energetic
sinogram from
step 5 by the
poly-energetic
sinogram from
step 4 to obtain
the correction
array.
= Correction
Array
Artefactual
Test Image
X-ray
Spectrum
Example
Intensity
profile
from a
projection
Calculated poly-
energetic sinogram
Empirical poly-
energetic sinogram
Corrected sinogram
3. Critique and Future Work
The emission spectrum used is only an approximation of the filtered tungsten-
target x-ray tube used by the department’s micro CT. The correction technique
could be improved by using a higher fidelity representation of the spectrum.
The source beam of a µCT system is generally a cone beam whereas the
computational model has a parallel source beam. Although a correction for real
data has not been attempted, this issue can be resolved by appropriate
modification of the reconstruction mathematics.
The mechanism used to amend the poly-energetic sinogram has been shown to
deliver significant reduction in streak artefacts within the computer model and is
ready to be applied to µCT.
The work of this SURE project invites further development, and therefore a grant
has been applied for to support this aspect of the work. It is also being pursued
under the auspices of a final year undergraduate student project.
Corrected
Test Image

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sure poster

  • 1. Stent Artefact Reduction for Vascular Imaging in Micro Computed Tomography Lydia Jim | Supervised by Dr. John Fenner and Dr. Andrew Narracott | Department of Cardiovascular Science 1. Background and Motivation Stents are metal cylindrical structures used to prevent localized blood flow constriction within blood vessels. Micro computed tomography (CT), a non-destructive imaging technique, is used to image isolated vessels embedded with stents.lll The process involves the attenuation of poly-energetic x-ray photons by an object from multiple viewing angles. This data is used to reconstruct an image.lll In micro CT of stents, the highly attenuating metal stent struts significantly modifies the x-ray spectrum.lll Streak artefacts are formed when spectral modifications are not taken into account for image reconstruction.lll This project is concerned with reducing the effects of artefacts in the reconstructed image. Computationally corrective methods are used to improve the reconstruction of the biological stent specimens. This can improve biological interpretation. A deployed stent. Cropped from original by BruceBlaus/CC-BY-3. CT image of a stent in a wax phantom compromised by streak artefacts 1. Assign energy dependent attenuation co-efficients to each pixel in the test image containing artefacts. 2. Pass poly-energetic x-ray photon spectrum through test image. 4. Convert the poly-energetic sinogram from photon flux to an attenuation sinogram. 5. Apply a radon transform to the test image containing artefacts (1) to obtain a mono-energetic sinogram of the test image. 7. Mutiply the mono-energetic sinogram from step 5 by the correction array from step 6 to obtain a corrected sinogram. 8. Back-project the corrected sinogram to reconstruct the corrected image. Iterate steps 1 to 8 as necessary to further minimize artefacts. 2. Methods & Results 3. Create a poly-energetic sinogram with mutiple viewing angles. ÷ 6. Divide the mono-energetic sinogram from step 5 by the poly-energetic sinogram from step 4 to obtain the correction array. = Correction Array Artefactual Test Image X-ray Spectrum Example Intensity profile from a projection Calculated poly- energetic sinogram Empirical poly- energetic sinogram Corrected sinogram 3. Critique and Future Work The emission spectrum used is only an approximation of the filtered tungsten- target x-ray tube used by the department’s micro CT. The correction technique could be improved by using a higher fidelity representation of the spectrum. The source beam of a µCT system is generally a cone beam whereas the computational model has a parallel source beam. Although a correction for real data has not been attempted, this issue can be resolved by appropriate modification of the reconstruction mathematics. The mechanism used to amend the poly-energetic sinogram has been shown to deliver significant reduction in streak artefacts within the computer model and is ready to be applied to µCT. The work of this SURE project invites further development, and therefore a grant has been applied for to support this aspect of the work. It is also being pursued under the auspices of a final year undergraduate student project. Corrected Test Image