RTK/Examples/4DROOSTERReconstruction

RTK provides a tool to reconstruct a 3D + time image which does not require explicit motion estimation. Instead, it uses total variation regularization along both space and time. The implementation is based on a paper that we have published (article). You should read the article 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 a motion mask in which the region of the space where movement is expected is set to 1 and the rest is set to 0. Each piece of data is described in more details below and can be downloaded from MIDAS. It is assumed that the breathing motion is periodic, which implies that the mechanical state of the chest depends only on the respiratory phase.

Projection images

This example is illustrated with a set of projection images of the POPI patient. You can download the projections and the required tables of the Elekta database, FRAME.DBF and IMAGE.DBF. 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 a 3D volume from all projection images. It implicitly assumes that the patient's chest is static, which is wrong. Therefore the reconstruction will be blurry
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:

Motion mask

The next piece of data is a 3D motion mask: a volume that contains only zeros, where no movement is expected to occur, and ones, where movement is expected. Typically, during breathing, the whole chest moves, so it is safe to use the patient mask (red+green). However, restricting the movement to a smaller region, e.g. the rib cage, can help reconstruct this region more accurately and mitigate artifacts, so we might want to use the rib cage mask (red):

Respiratory signal

The 4D ROOSTER algorithm requires that we associate each projection image with the instant of the respiratory cycle at which it has been acquired. We used the Amsterdam shroud solution of Lambert Zijp (described here) which is implemented in RTK

rtkamsterdamshroud --path . \
                   --regexp '.*.his' \
                   --output shroud.mha \
                   --unsharp 650
rtkextractshroudsignal --input shroud.mha \
                       --output signal.txt

in combination with Matlab post-processing to get the phase signal. Note that the phase must go from 0 to 1 where 0.3 corresponds to 30% in the respiratory cycle, i.e., frame 3 if you have a 10-frames 4D reconstruction or frame 6 if you have a 20-frames 4D reconstruction. The resulting phase is in green on top of the blue respiratory signal and the detected end-exhale peaks:

4D ROOSTER for cone-beam CT reconstruction

We now have all the pieces to perform a 3D + time reconstruction. The algorithm will perform "niter" iterations of the main loop, and run three inner loops at each iteration:

- conjugate gradient reconstruction with "cgiter" iterations
- total-variation regularization in space, with parameter "gamma_space" (the higher, the more regularized) and "tviter" iterations
- total-variation regularization in time, with parameter "gamma_time" (the higher, the more regularized) and "tviter" iterations

The number of iterations suggested here should work in most situations. The parameters gamma_space and gamma_time, on the other hand, must be adjusted carefully for each type of datasets (and, unfortunately, for each resolution).

# Reconstruct from all projection images 4D ROOSTER
rtkfourdrooster \
  -p . \
  -r .*.his \
  -o rooster.mha \
  -g geometry.rtk \
  --signal sphase.txt \ 
  --motionmask MotionMask.mha \
  --gamma_time 0.01 \
  --gamma_space 0.01 \
  --niter 30 \
  --cgiter 4 \
  --tviter 10 \
  --spacing 2 \
  --dimension 128 \
  --frames 5

Depending on the resolution you choose, the reconstruction time can range from a few minutes (very low resolution, typically 32³ * 5) to many hours (standard resolution, typically 256³ * 10), even on powerful computers. For the latter, it is recommended to use the CUDA version of the forward and back projectors by running this command instead:

# Reconstruct from all projection images 4D ROOSTER
rtkfourdrooster \
  -p . \
  -r .*.his \
  -o rooster.mha \
  -g geometry.rtk \
  --signal sphase.txt \ 
  --motionmask MotionMask.mha \
  --fp CudaRayCast \
  --bp CudaVoxelBased \
  --gamma_time 0.01 \
  --gamma_space 0.01 \
  --niter 30 \
  --cgiter 4 \
  --tviter 10 \
  --spacing 2 \
  --dimension 128 \
  --frames 5

With a recent GPU, this should allow you to perform a standard resolution reconstruction in less than one hour.