3,795 research outputs found
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
GPU-based Iterative Cone Beam CT Reconstruction Using Tight Frame Regularization
X-ray imaging dose from serial cone-beam CT (CBCT) scans raises a clinical
concern in most image guided radiation therapy procedures. It is the goal of
this paper to develop a fast GPU-based algorithm to reconstruct high quality
CBCT images from undersampled and noisy projection data so as to lower the
imaging dose. For this purpose, we have developed an iterative tight frame (TF)
based CBCT reconstruction algorithm. A condition that a real CBCT image has a
sparse representation under a TF basis is imposed in the iteration process as
regularization to the solution. To speed up the computation, a multi-grid
method is employed. Our GPU implementation has achieved high computational
efficiency and a CBCT image of resolution 512\times512\times70 can be
reconstructed in ~5 min. We have tested our algorithm on a digital NCAT phantom
and a physical Catphan phantom. It is found that our TF-based algorithm is able
to reconstrct CBCT in the context of undersampling and low mAs levels. We have
also quantitatively analyzed the reconstructed CBCT image quality in terms of
modulation-transfer-function and contrast-to-noise ratio under various scanning
conditions. The results confirm the high CBCT image quality obtained from our
TF algorithm. Moreover, our algorithm has also been validated in a real
clinical context using a head-and-neck patient case. Comparisons of the
developed TF algorithm and the current state-of-the-art TV algorithm have also
been made in various cases studied in terms of reconstructed image quality and
computation efficiency.Comment: 24 pages, 8 figures, accepted by Phys. Med. Bio
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