ArticlePhysics in medicine and biology2016
Using edge-preserving algorithm with non-local mean for significantly improved image-domain material decomposition in dual-energy CT.
Article in Physics in medicine and biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01825213 (Stratification of Patients With Chronic Liver Disease Using Multi Spectral CT), which is not on this map. Cited by 14 papers.
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Stratification of Patients With Chronic Liver Disease Using Multi Spectral CT
Who cites it
14 citing papers in PubMed.
- Revealing hidden material composition via physics-induced deep learning for precision clinical diagnosis.Patterns (New York, N.Y.) · 2026Article
- Dual-Head Pix2Pix Network for Material Decomposition of Conventional CT Projections with Photon-Counting Guidance.Sensors (Basel, Switzerland) · 2025Article
- Multi-Material Decomposition Using Spectral Diffusion Posterior Sampling.IEEE transactions on bio-medical engineering · 2025Article
- [A semi-supervised material quantitative intelligent imaging algorithm for spectral CT based on prior information perception learning].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2023Article
- Deep learning-based virtual noncalcium imaging in multiple myeloma using dual-energy CT.Medical physics · 2022Article
- Image-domain Material Decomposition for Spectral CT using a Generalized Dictionary Learning.IEEE transactions on radiation and plasma medical sciences · 2021Article
- Quantitative analysis of therapeutic response in psoriatic arthritis of digital joints with Dual-energy CT iodine maps.Scientific reports · 2020Article
- 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
- Impact of prior information on material decomposition in dual- and multienergy computed tomography.Journal of medical imaging (Bellingham, Wash.) · 2019Article
- Tensor decomposition and non-local means based spectral CT image denoising.Journal of X-ray science and technology · 2019Article
- Optimization of Energy Combination for Gold-based Contrast Agents below K-edges in Dual-energy Micro-CT.IEEE transactions on radiation and plasma medical sciences · 2018Article
- Segmentation-free x-ray energy spectrum estimation for computed tomography using dual-energy material decomposition.Journal of medical imaging (Bellingham, Wash.) · 2017Article
- Applications of nonlocal means algorithm in low-dose X-ray CT image processing and reconstruction: A review.Medical physics · 2017Review
- Adaptive Nonlocal Means Method for Denoising Basis Material Images From Dual-Energy Computed Tomography.Journal of computer assisted tomographyArticle
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9 authors.
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Abstract
Increased noise is a general concern for dual-energy material decomposition. Here, we develop an image-domain material decomposition algorithm for dual-energy CT (DECT) by incorporating an edge-preserving filter into the Local HighlY constrained backPRojection reconstruction (HYPR-LR) framework. With effective use of the non-local mean, the proposed algorithm, which is referred to as HYPR-NLM, reduces the noise in dual-energy decomposition while preserving the accuracy of quantitative measurement and spatial resolution of the material-specific dual-energy images. We demonstrate the noise reduction and resolution preservation of the algorithm with an iodine concentrate numerical phantom by comparing the HYPR-NLM algorithm to the direct matrix inversion, HYPR-LR and iterative image-domain material decomposition (Iter-DECT). We also show the superior performance of the HYPR-NLM over the existing methods by using two sets of cardiac perfusing imaging data. The DECT material decomposition comparison study shows that all four algorithms yield acceptable quantitative measurements of iodine concentrate. Direct matrix inversion yields the highest noise level, followed by HYPR-LR and Iter-DECT. HYPR-NLM in an iterative formulation significantly reduces image noise and the image noise is comparable to or even lower than that generated using Iter-DECT. For the HYPR-NLM method, there are marginal edge effects in the difference image, suggesting the high-frequency details are well preserved. In addition, when the search window size increases from to , there are no significant changes or marginal edge effects in the HYPR-NLM difference images. The reference drawn from the comparison study includes: (1) HYPR-NLM significantly reduces the DECT material decomposition noise while preserving quantitative measurements and high-frequency edge information, and (2) HYPR-NLM is robust with respect to parameter selection.
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