ArticleJournal of computer assisted tomography
Adaptive Nonlocal Means Method for Denoising Basis Material Images From Dual-Energy Computed Tomography.
Article in Journal of computer assisted tomography. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- [Low-dose CT reconstruction based on high-dimensional partial differential equation projection recovery].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2024Article
- A review on Deep Learning approaches for low-dose Computed Tomography restoration.Complex & intelligent systems · 2023Article
- InNetGAN: Inception Network-Based Generative Adversarial Network for Denoising Low-Dose Computed Tomography.Journal of healthcare engineering · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
We propose an adaptive nonlocal means approach for image-domain material decomposition in low-dose dual-energy micro-computed tomography. The key idea is to create a distribution map for decomposition error and assign a smooth weight for a given pixel. This method is applied to the decomposed images of 3 basis materials: bone, soft tissue, and gold in our applications. We assume that bone and gold cannot coexist in the same pixel and regroup these basis materials into 2 categories. For soft tissue, the proposed algorithm is implemented in a noniterative mode. For bone and gold, an iterative mode is used and followed by a postiteration process. Both our numerical simulation and in vivo preclinical experiment results show that the proposed adaptive nonlocal means outperforms other state-of-the-art denoising algorithms, such as the original nonlocal means and total variation minimization methods.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.