31,466 research outputs found
Extended object reconstruction in adaptive-optics imaging: the multiresolution approach
We propose the application of multiresolution transforms, such as wavelets
(WT) and curvelets (CT), to the reconstruction of images of extended objects
that have been acquired with adaptive optics (AO) systems. Such multichannel
approaches normally make use of probabilistic tools in order to distinguish
significant structures from noise and reconstruction residuals. Furthermore, we
aim to check the historical assumption that image-reconstruction algorithms
using static PSFs are not suitable for AO imaging. We convolve an image of
Saturn taken with the Hubble Space Telescope (HST) with AO PSFs from the 5-m
Hale telescope at the Palomar Observatory and add both shot and readout noise.
Subsequently, we apply different approaches to the blurred and noisy data in
order to recover the original object. The approaches include multi-frame blind
deconvolution (with the algorithm IDAC), myopic deconvolution with
regularization (with MISTRAL) and wavelets- or curvelets-based static PSF
deconvolution (AWMLE and ACMLE algorithms). We used the mean squared error
(MSE) and the structural similarity index (SSIM) to compare the results. We
discuss the strengths and weaknesses of the two metrics. We found that CT
produces better results than WT, as measured in terms of MSE and SSIM.
Multichannel deconvolution with a static PSF produces results which are
generally better than the results obtained with the myopic/blind approaches
(for the images we tested) thus showing that the ability of a method to
suppress the noise and to track the underlying iterative process is just as
critical as the capability of the myopic/blind approaches to update the PSF.Comment: In revision in Astronomy & Astrophysics. 19 pages, 13 figure
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
High resolution image reconstruction with constrained, total-variation minimization
This work is concerned with applying iterative image reconstruction, based on
constrained total-variation minimization, to low-intensity X-ray CT systems
that have a high sampling rate. Such systems pose a challenge for iterative
image reconstruction, because a very fine image grid is needed to realize the
resolution inherent in such scanners. These image arrays lead to
under-determined imaging models whose inversion is unstable and can result in
undesirable artifacts and noise patterns. There are many possibilities to
stabilize the imaging model, and this work proposes a method which may have an
advantage in terms of algorithm efficiency. The proposed method introduces
additional constraints in the optimization problem; these constraints set to
zero high spatial frequency components which are beyond the sensing capability
of the detector. The method is demonstrated with an actual CT data set and
compared with another method based on projection up-sampling.Comment: This manuscript appears in the proceedings of the 2010 IEEE medical
imaging conferenc
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