ArticlePhysics in medicine and biology2018
Iterative spectral CT reconstruction based on low rank and average-image-incorporated BM3D.
Article in Physics in medicine and biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Image-domain Material Decomposition for Spectral CT using a Generalized Dictionary Learning.IEEE transactions on radiation and plasma medical sciences · 2021Article
- Refined Locally Linear Transform-Based Spectral-Domain Gradient Sparsity and Its Applications in Spectral CT Reconstruction.IEEE access : practical innovations, open solutions · 2021Article
- A Robust Regularizer for Multiphase CT.IEEE transactions on medical imaging · 2020Article
- Non-Local Low-Rank Cube-Based Tensor Factorization for Spectral CT Reconstruction.IEEE transactions on medical imaging · 2019Article
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3 authors.
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Abstract
In a photon counting detector integrated spectral CT scanner, the received photons are counted in several energy channels to generate the corresponding projections. Since the projection in each energy channel is generated using part of the received photons, the reconstructed channel image suffers from severe noise. Therefore, image reconstruction in spectral CT is considered to be a big challenge. Because the inter-channel images are all from the same object but in different energy bins, there exists a strong correlation among these images. Moreover, it is suggested that there are similarities among various patches of CT images in the spatial domain. In this work, we propose average-image-incorporated block-matching and 3D (aiiBM3D) filtering along with low rank regularization for iterative spectral CT reconstruction. The aiiBM3D method is based on filtered 3D data arrays formed by similar 2D blocks using the mapped version of the average image obtained from linear regression. The reconstruction procedure consists of two main steps. First, the alternating direction method of multipliers is employed to solve the problem with low rank regularization where the goal is to exploit the correlation in inter-channel images. Second, our proposed BM3D-based algorithm is applied to all the channel images to make use of the redundant information in the spatial domain and inter-channel. The two steps repeat until the stopping criteria are satisfied. The proposed method is validated on numerically simulated and preclinical datasets. Our results confirm its high performance in terms of signal to noise ratio and structural preservation.
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