RTK/Examples/MCCBCTReconstruction: Difference between revisions

From Openrtk
Jump to navigation Jump to search
No edit summary
No edit summary
Line 1: Line 1:
RTK provides the necessary tools to reconstruct an image with motion compensation. The implementation is based on our previous work although it is currently very slow. You should read the following articles to understand the basics of the algorithm:
RTK provides the necessary tools to reconstruct an image with motion compensation. The implementation is based on two articles that we have published ([http://www.creatis.insa-lyon.fr/site/fr/publications/RIT-09 article 1] and [http://www.creatis.insa-lyon.fr/site/fr/publications/RIT-09b article 2]) but without optimization (very slow reconstruction) and only the FDK-based motion-compensated CBCT reconstruction. You should read the articles to understand the basics of the algorithm before trying to use the software.


<dd>Rit, S., Sarrut, D. and Desbat, L.
The algorithm requires a set of projection images with the associated RTK geometry, the respiratory phase of each projection image and the 4D motion vector field over a respiratory cycle in the cone-beam coordinate system. It is assumed that we have a breathing motion that is cyclic and similar to that described by the vector field. Note that you could modify the code and create your own motion model if you want to. [http://www.openrtk.org/RTK/project/contactus.html Contact us] if you want to.
<dd><i>Comparison of analytic and algebraic methods for motion-compensated cone-beam CT reconstruction of the thorax</i>
<dd>IEEE Trans Med Imag, <b>2009</b>, Vol. 28(10), pp. 1513-1525


and
= Projection images =


<dd>Rit, S., Wolthaus, J., van Herk, M. and Sonke, J.-J.
This example is illustrated with a set of projection images of the [http://www.creatis.insa-lyon.fr/rio/popi-model_original_page POPI patient]. This dataset has been used in the first previously-mentioned article. You can [http://midas3.kitware.com/midas/download/item/208648/projections.tgz download the projections] and the required tables of the Elekta database, [http://midas3.kitware.com/midas/download/item/208650/FRAME.DBF FRAME.DBF] and [http://midas3.kitware.com/midas/download/item/208649/IMAGE.DBF IMAGE.DBF], from MIDAS. The dataset is first used to reconstruct a blurry image:
<dd><i>On-the-fly motion-compensated cone-beam CT using an a priori model of the respiratory motion</i>
 
<dd>Med Phys, <b>2009</b>, Vol. 36(6), pp. 2283-2296
<source lang="bash">
# Convert Elekta database to RTK geometry
rtkelektasynergygeometry \
  -o geometry.rtk \
  -f FRAME.DBF \
  -i IMAGE.DBF \
  -u 1.3.46.423632.141000.1169042526.68
 
# Reconstruct from all projection images without any motion compensation
rtkfdk \
  -p . \
  -r .*.his \
  -o fdk.mha \
  -g geometry.rtk \
  --hann 0.5 \
  --pad 1.0
 
# Keep only the field-of-view of the image
rtkfieldofview \
  --reconstruction fdk.mha \
  --output fdk.mha \
  --geometry geometry.rtk \
  --path . \
  --regexp '.*.his'
</source>
 
You should obtain something like that with [http://vv.creatis.insa-lyon.fr/ VV]:
[[File:blurred.jpg]]

Revision as of 06:52, 25 July 2013

RTK provides the necessary tools to reconstruct an image with motion compensation. The implementation is based on two articles that we have published (article 1 and article 2) but without optimization (very slow reconstruction) and only the FDK-based motion-compensated CBCT reconstruction. You should read the articles to understand the basics of the algorithm before trying to use the software.

The algorithm requires a set of projection images with the associated RTK geometry, the respiratory phase of each projection image and the 4D motion vector field over a respiratory cycle in the cone-beam coordinate system. It is assumed that we have a breathing motion that is cyclic and similar to that described by the vector field. Note that you could modify the code and create your own motion model if you want to. Contact us if you want to.

Projection images

This example is illustrated with a set of projection images of the POPI patient. This dataset has been used in the first previously-mentioned article. You can download the projections and the required tables of the Elekta database, FRAME.DBF and IMAGE.DBF, from MIDAS. The dataset is first used to reconstruct a blurry image:

# Convert Elekta database to RTK geometry
rtkelektasynergygeometry \
  -o geometry.rtk \
  -f FRAME.DBF \
  -i IMAGE.DBF \
  -u 1.3.46.423632.141000.1169042526.68

# Reconstruct from all projection images without any motion compensation
rtkfdk \
  -p . \
  -r .*.his \
  -o fdk.mha \
  -g geometry.rtk \
  --hann 0.5 \
  --pad 1.0

# Keep only the field-of-view of the image
rtkfieldofview \
  --reconstruction fdk.mha \
  --output fdk.mha \
  --geometry geometry.rtk \
  --path . \
  --regexp '.*.his'

You should obtain something like that with VV: Blurred.jpg