ArticlePhysics in medicine and biology2017
Iterative reconstruction for dual energy CT with an average image-induced nonlocal means regularization.
Article in Physics in medicine and biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Accurate iodine quantification and residual error reduction with principal component analysis multimaterial decomposition using spectral CT.Medical physics · 2026Article
- A low-dose CT reconstruction method using sub-pixel anisotropic diffusion.Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2025Article
- Dual-energy computed tomography for improved visualization of internal jugular chain neck lymph node metastasis and nodal necrosis in head and neck squamous cell carcinoma.Japanese journal of radiology · 2023Article
- Motion-compensated scheme for sequential scanned statistical iterative dual-energy CT reconstruction.Physics in medicine and biology · 2023Article
- [A nonlocal spectral similarity-induced material decomposition method for noise reduction of dual-energy CT images].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2022Article
- Voxel-based partial volume correction of PET images via subtle MRI guided non-local means regularization.Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) · 2021Article
- Image-domain Material Decomposition for Spectral CT using a Generalized Dictionary Learning.IEEE transactions on radiation and plasma medical sciences · 2021Article
- Obtaining dual-energy computed tomography (CT) information from a single-energy CT image for quantitative imaging analysis of living subjects by using deep learning.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2020Article
- Image Quality and Dose Comparison of Single-Energy CT (SECT) and Dual-Energy CT (DECT).Radiology research and practice · 2020Review
- Iterative reconstruction for photon-counting CT using prior image constrained total generalized variation.Computers in biology and medicine · 2018Article
- Statistical CT reconstruction using region-aware texture preserving regularization learning from prior normal-dose CT image.Physics in medicine and biology · 2018Article
- [Redundancy information-induced image reconstruction for low-dose myocardial perfusion computed tomography].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2018Article
- Locally linear constraint based optimization model for material decomposition.Physics in medicine and biology · 2017Article
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13 authors.
Funding
Abstract
Reducing radiation dose in dual energy computed tomography (DECT) is highly desirable but it may lead to excessive noise in the filtered backprojection (FBP) reconstructed DECT images, which can inevitably increase the diagnostic uncertainty. To obtain clinically acceptable DECT images from low-mAs acquisitions, in this work we develop a novel scheme based on measurement of DECT data. In this scheme, inspired by the success of edge-preserving non-local means (NLM) filtering in CT imaging and the intrinsic characteristics underlying DECT images, i.e. global correlation and non-local similarity, an averaged image induced NLM-based (aviNLM) regularization is incorporated into the penalized weighted least-squares (PWLS) framework. Specifically, the presented NLM-based regularization is designed by averaging the acquired DECT images, which takes the image similarity within the two energies into consideration. In addition, the weighted least-squares term takes into account DECT data-dependent variance. For simplicity, the presented scheme was termed as 'PWLS-aviNLM'. The performance of the presented PWLS-aviNLM algorithm was validated and evaluated on digital phantom, physical phantom and patient data. The extensive experiments validated that the presented PWLS-aviNLM algorithm outperforms the FBP, PWLS-TV and PWLS-NLM algorithms quantitatively. More importantly, it delivers the best qualitative results with the finest details and the fewest noise-induced artifacts, due to the aviNLM regularization learned from DECT images. This study demonstrated the feasibility and efficacy of the presented PWLS-aviNLM algorithm to improve the DECT reconstruction and resulting material decomposition.
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