@article{Vidal2011MedPhys,
author = {F. P. Vidal and M. Folkerts and N. Freud and S. Jiang},
title = {{GPU} Accelerated {DRR} Computation with Scatter},
journal = {Medical Physics},
year = 2011,
volume = 38,
pages = {3455-3456},
number = 6,
month = jul,
address = {Vancouver, Canada},
annotation = {AAPM Annual Meeting, Jul~31--Aug~4, 2011},
abstract = {Purpose: We propose a fast software library implemented on
graphics processing unit (GPU) to compute digitally reconstructed
radiographs (DRRs). It takes into account first order Compton
scattering.
Methods: The simulation is based on the evaluation of
the Beer-Lambert law and of the Klein-Nishina equation. The
algorithm is fully determinist and has been fully implemented on
GPU to achieve clinically acceptable efficiency. A full resolution
simulation is performed for primary radiation. A much lower image
resolution is used for Compton scattering as it adds a low frequency
pattern over the projection image. Each voxel of the CT dataset is
considered as a secondary source. The number of photons that reach
each voxel is evaluated. Then, for each secondary source, a projection
image is computed and integrated in the final image. The photon energy
between each secondary source and each pixel is also computed.
An interlaced sampling mode is also proposed to further reduce
the computation time without sacrificing numerical accuracy. Finally,
the speed and accuracy are assessed.
Results: We show that the computations can be fully implemented on
the GPU with an original under-sampling method to produce clinically
acceptable results. For example, a simulation can be achieved in less
than 7 seconds whilst the maximum relative error remains below 5\% and
the average relative error below 1.4\%. At full resolution, a speed-up
by factor ∼12X is achieved for the GPU implementation with our
interlaced-mode by comparison with our multi-threaded CPU implementation
using 8 threads in parallel.
Conclusions: DRR calculation with scatter is
computationally intensive. The use of GPU can achieve clinically
acceptable efficiency. A Compton fluence map can be computed in a few
seconds using under-sampling, whilst keeping numerical inaccuracies
relatively low. This work can be used for CBCT reconstruction to reduce
scatter artifacts.},
doi = {10.1118/1.3611828},
publisher = {American Association of Physicists in Medicine}
}
@article{Vidal2010MedPhys-A,
author = {F. P. Vidal and J. Louchet and {J.-M.} Rocchisani and \'E. Lutton},
title = {Flies for {PET}: An Artificial Evolution Strategy for Image Reconstruction in Nuclear Medicine},
journal = {Medical Physics},
year = 2010,
volume = 37,
pages = {3139},
number = 6,
month = jul,
address = {Philadelphia, Pensilvania, USA},
annotation = {AAPM Annual Meeting, Jul~18--22, 2010},
abstract = {Purpose: We propose an evolutionary approach for image
reconstruction in nuclear medicine. Our method is based on
a cooperative coevolution strategy (also called Parisian evolution):
the ``fly algorithm''.
Method and Materials: Each individual, or fly,
corresponds to a 3D point that mimics a radioactive emitter, i.e.
a stochastic simulation of annihilation events is performed to compute
the fly's illumination pattern. For each annihilation, a photon is
emitted in a random direction, and a second photon is emitted in
the opposite direction. The line between two detected photons is
called line of response (LOR). If both photons are detected by
the scanner, the fly's illumination pattern is updated.
The LORs of every fly are aggregated to form the population total
illumination pattern. Using genetic operations to optimize the position
of positrons, the population of flies evolves so that the population
total pattern matches measured data. The final population of flies
approximates the radioactivity concentration.
Results: We have developed numerical phantom models to assess
the reconstruction algorithm. To date, no scattering and no tissue attenuation
have been considered. Whilst this is not physically correct, it allows us
to test and validate our approach in the simplest cases.
Preliminary results show the validity of this approach in both 2D and
fully-3D modes. In particular, the size of objects, and
their relative concentrations can be retrieved in the 2D mode.
In fully-3D, complex shapes can be reconstructed.
Conclusions: An evolutionary approach for PET reconstruction has been proposed
and validated using simple test cases. Further work will therefore include
the use of more realistic input data (including random events and scattering),
which will finally lead to implement the correction of scattering within our algorithm.
A comparison study against ML-EM and/or OS-EM methods will also need to be conducted.},
doi = {10.1118/1.3468200},
publisher = {American Association of Physicists in Medicine},
pdf = {pdf/Vidal2010MedPhys-A.pdf}
}
@article{Vidal2010MedPhys-B,
author = {F. P. Vidal and P. F. Villard and M. Garnier and N. Freud and J. M. L\'etang and N. W. John and F. Bello},
title = {Joint Simulation of Transmission X-ray Imaging on {GPU} and Patient's Respiration on {CPU}},
journal = {Medical Physics},
year = 2010,
volume = 37,
pages = {3129},
number = 6,
month = jul,
address = {Philadelphia, Pensilvania, USA},
annotation = {AAPM Annual Meeting, Jul~18--22, 2010},
abstract = {Purpose: We previously proposed to compute the X-ray attenuation
from polygons directly on the GPU, using OpenGL, to significantly
increase performance without loss of accuracy. The method has been
deployed into a training simulator for percutaneous transhepatic
cholangiography. The simulations were however restricted to
monochromatic X-rays using a point source. They now take into account
both the geometrical blur and polychromatic X-rays.
Method and Materials: To implement the Beer-Lambert law with a
polychromatic beam, additional loops have been included in the
simulation pipeline. It is split into rendering passes and uses frame
buffer objects to store intermediate results. The source shape is
modeled using a variable number of point sources and the incident beam
is split into discrete energy channels. The respiration model is composed
of ribs, spine, lungs, liver, diaphragm and the external skin. The organ
motion simulation is based on anatomical and physiological studies:
the model is monitored by two independent active components:
the ribs with a kinematics law and the diaphragm tendon with an up and
down translation. Other soft-tissue components are passively deformed
using a 3D extension of the ChainMail algorithm. The respiration rate
is also tunable to modify the respiratory profile.
Results: We have extended the simulation pipeline to take into account
focal spots that cause geometric unsharpness and polychromatic X-rays,
and dynamic polygon meshes of a breathing patient can be used as input data.
Conclusions: X-ray transmission images can be fully simulated on the GPU,
by using the Beer-Lambert law with polychromatism and taking into account
the shape of the source. The respiration of the patient can be
modeled to produce dynamic meshes. This is a useful development to improve
the level of realism in simulations, when it is needed to retain both speed
and accuracy.},
doi = {10.1118/1.3468154},
publisher = {American Association of Physicists in Medicine},
pdf = {pdf/Vidal2010MedPhys-B.pdf}
}
@inproceedings{Sinha2009RSNA,
author = {A. Sinha and K. Flood and D. Kessel and S. Johnson and C. Hunt and H. Woolnough and F. P. Vidal and {P.-F.} Villard and R. Holbray and C. M. Crawshaw and A. Bulpitt and N. W. John and F. Bello and R. Phillips and D. A. Gould},
title = {The Role of Simulation in Medical Training and Assessment},
booktitle = {Radiological Society of North America 2009 (RSNA 2009)},
year = 2009,
month = nov,
address = {Chicago, Illinois},
annotation = {Nov~29--Dec~4, 2009}
}
@inproceedings{Villard2009CARS,
author = {{P.-F.} Villard and F. P. Vidal and C. Hunt and F. Bello and N. W. John and S. Johnson and D. A. Gould},
title = {Percutaneous Transhepatic Cholangiography Training Simulator with Real-time Breathing Motion},
booktitle = {Proceeding of the 23rd International Congress of Computer Assisted Radiology and Surgery},
year = 2009,
series = {International Journal of Computer Assisted Radiology and Surgery},
volume = {4 (Suppl 1)},
pages = {S66-S67},
month = jun,
address = {Berlin, Germany},
annotation = {Jun~23--27, 2009},
keywords = {Interventional radiology, Virtual environments, Respiration simulation, X-ray simulation, Needle puncture, Haptics, Task analysis},
doi = {10.1007/s11548-009-0326-x},
publisher = {Springer}
}
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