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Since in newer PET systems, data processing is generally based on iterative reconstruction differences between centres which may become smaller in the future.
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In this paper, a 2D CS sparse image reconstruction algorithm based on iterative gradient projection is proposed.
Also, a 2D CS image reconstruction algorithm based on iterative gradient projection is derived in [25].
We review several image reconstruction strategies based on iterative, statistical, and gradient sparsity regularization, demonstrating that high image quality is achievable with low radiation dose given ever more powerful computational resources.
The design is based on iterative window spectrum zero placing.
The solution method is based on iterative defect correction.
Clustering based on iterative partitioning.
It features an iterative implementation of the GBA method which is based on iterative Group Analysis algorithm (iGA) [ 26].
Reconstruction settings for the PET scan were based on an iterative reconstruction algorithm with attenuation and scatter correction (OSEM, four iterations, eight subsets, using a 5-mm Gaussian filter) on a 168 × 168 (4 × 4 mm) grid and a slice thickness of 3 mm.
Studies on iterative reconstruction techniques on computed tomographic (CT) scanners show reduced noise and changed image texture.
Iterative detection is subsequently performed based on two stopping criteria of dynamic iterative decoding.
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