The mathematics of computerized tomography
The mathematics of computerized tomography
Efficient computation of sum-products on GPUs through software-managed cache
Proceedings of the 22nd annual international conference on Supercomputing
Multi GPU implementation of iterative tomographic reconstruction algorithms
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Accelerating regularized iterative CT reconstruction on commodity graphics hardware (GPU)
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Accelerating the EMML algorithm and related iterative algorithms by rescaled block-iterative methods
IEEE Transactions on Image Processing
Adaptive wavelet graph model for Bayesian tomographic reconstruction
IEEE Transactions on Image Processing
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In computed tomography, the application of iterative reconstruction methods in practical situations is impeded by their high computational demands. Especially in high resolution X-ray computed tomography, where reconstruction volumes contain a high number of volume elements (several giga voxels), this computational burden prevents their actual breakthrough. Besides the large amount of calculations, iterative algorithms require the entire volume to be kept in memory during reconstruction, which quickly becomes cumbersome for large data sets. To overcome this obstacle, we present a novel multiresolution reconstruction, which greatly reduces the required amount of memory without significantly affecting the reconstructed image quality. It is shown that, combined with an efficient implementation on a graphical processing unit, the multiresolution approach enables the application of iterative algorithms in the reconstruction of large volumes at an acceptable speed using only limited resources.