844 research outputs found
Four-dimensional Cone Beam CT Reconstruction and Enhancement using a Temporal Non-Local Means Method
Four-dimensional Cone Beam Computed Tomography (4D-CBCT) has been developed
to provide respiratory phase resolved volumetric imaging in image guided
radiation therapy (IGRT). Inadequate number of projections in each phase bin
results in low quality 4D-CBCT images with obvious streaking artifacts. In this
work, we propose two novel 4D-CBCT algorithms: an iterative reconstruction
algorithm and an enhancement algorithm, utilizing a temporal nonlocal means
(TNLM) method. We define a TNLM energy term for a given set of 4D-CBCT images.
Minimization of this term favors those 4D-CBCT images such that any anatomical
features at one spatial point at one phase can be found in a nearby spatial
point at neighboring phases. 4D-CBCT reconstruction is achieved by minimizing a
total energy containing a data fidelity term and the TNLM energy term. As for
the image enhancement, 4D-CBCT images generated by the FDK algorithm are
enhanced by minimizing the TNLM function while keeping the enhanced images
close to the FDK results. A forward-backward splitting algorithm and a
Gauss-Jacobi iteration method are employed to solve the problems. The
algorithms are implemented on GPU to achieve a high computational efficiency.
The reconstruction algorithm and the enhancement algorithm generate visually
similar 4D-CBCT images, both better than the FDK results. Quantitative
evaluations indicate that, compared with the FDK results, our reconstruction
method improves contrast-to-noise-ratio (CNR) by a factor of 2.56~3.13 and our
enhancement method increases the CNR by 2.75~3.33 times. The enhancement method
also removes over 80% of the streak artifacts from the FDK results. The total
computation time is ~460 sec for the reconstruction algorithm and ~610 sec for
the enhancement algorithm on an NVIDIA Tesla C1060 GPU card.Comment: 20 pages, 3 figures, 2 table
PyHST2: an hybrid distributed code for high speed tomographic reconstruction with iterative reconstruction and a priori knowledge capabilities
We present the PyHST2 code which is in service at ESRF for phase-contrast and
absorption tomography. This code has been engineered to sustain the high data
flow typical of the third generation synchrotron facilities (10 terabytes per
experiment) by adopting a distributed and pipelined architecture. The code
implements, beside a default filtered backprojection reconstruction, iterative
reconstruction techniques with a-priori knowledge. These latter are used to
improve the reconstruction quality or in order to reduce the required data
volume and reach a given quality goal. The implemented a-priori knowledge
techniques are based on the total variation penalisation and a new recently
found convex functional which is based on overlapping patches.
We give details of the different methods and their implementations while the
code is distributed under free license.
We provide methods for estimating, in the absence of ground-truth data, the
optimal parameters values for a-priori techniques
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