ArticleIEEE transactions on medical imaging2016
Extracting Information From Previous Full-Dose CT Scan for Knowledge-Based Bayesian Reconstruction of Current Low-Dose CT Images.
Article in IEEE transactions on medical imaging, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed.
- Learned Tensor Neural Network Texture Prior for Photon-Counting CT Reconstruction.IEEE transactions on medical imaging · 2024Article
- Machine Learned Texture Prior From Full-Dose CT Database via Multi-Modality Feature Selection for Bayesian Reconstruction of Low-Dose CT.IEEE transactions on medical imaging · 2023Article
- A Joint-Parameter Estimation and Bayesian Reconstruction Approach to Low-Dose CT.Sensors (Basel, Switzerland) · 2023Article
- Role of CT Images in the Diagnosis of Common Acute Abdominal Diseases in General Surgery.Journal of healthcare engineering · 2022Article
- Prior-image-based CT reconstruction using attenuation-mismatched priors.Physics in medicine and biology · 2021Article
- Characterization of tissue-specific pre-log Bayesian CT reconstruction by texture-dose relationship.Medical physics · 2020Article
- Spectral CT Reconstruction via Low-Rank Representation and Region-Specific Texture Preserving Markov Random Field Regularization.IEEE transactions on medical imaging · 2020Article
- A Task-dependent Investigation on Dose and Texture in CT Image Reconstruction.IEEE transactions on radiation and plasma medical sciences · 2020Article
- Constructing a tissue-specific texture prior by machine learning from previous full-dose scan for Bayesian reconstruction of current ultralow-dose CT images.Journal of medical imaging (Bellingham, Wash.) · 2020Article
- Energy enhanced tissue texture in spectral computed tomography for lesion classification.Visual computing for industry, biomedicine, and art · 2019Article
- A Feasibility Study of Extracting Tissue Textures From a Previous Full-Dose CT Database as Prior Knowledge for Bayesian Reconstruction of Current Low-Dose CT Images.IEEE transactions on medical imaging · 2019Article
- Automatic Detection and Classification of Lung Nodules in CT Image Using Optimized Neuro Fuzzy Classifier with Cuckoo Search Algorithm.Journal of medical systems · 2019Article
- Regularization Analysis and Design for Prior-Image-Based X-Ray CT Reconstruction.IEEE transactions on medical imaging · 2018Article
- Statistical CT reconstruction using region-aware texture preserving regularization learning from prior normal-dose CT image.Physics in medicine and biology · 2018Article
- Regularization strategies in statistical image reconstruction of low-dose x-ray CT: A review.Medical physics · 2018Review
- Prospective Image Quality Analysis and Control for Prior-Image-Based Reconstruction of Low-Dose CT.Proceedings of SPIE--the International Society for Optical Engineering · 2018Article
- Iterative Low-Dose CT Reconstruction With Priors Trained by Artificial Neural Network.IEEE transactions on medical imaging · 2017Article
- Assessment of prior image induced nonlocal means regularization for low-dose CT reconstruction: Change in anatomy.Medical physics · 2017Article
- Iterative reconstruction for dual energy CT with an average image-induced nonlocal means regularization.Physics in medicine and biology · 2017Article
- Applications of nonlocal means algorithm in low-dose X-ray CT image processing and reconstruction: A review.Medical physics · 2017Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Markov random field (MRF) model has been widely employed in edge-preserving regional noise smoothing penalty to reconstruct piece-wise smooth images in the presence of noise, such as in low-dose computed tomography (LdCT). While it preserves edge sharpness, its regional smoothing may sacrifice tissue image textures, which have been recognized as useful imaging biomarkers, and thus it may compromise clinical tasks such as differentiating malignant vs. benign lesions, e.g., lung nodules or colon polyps. This study aims to shift the edge-preserving regional noise smoothing paradigm to texture-preserving framework for LdCT image reconstruction while retaining the advantage of MRF's neighborhood system on edge preservation. Specifically, we adapted the MRF model to incorporate the image textures of muscle, fat, bone, lung, etc. from previous full-dose CT (FdCT) scan as a priori knowledge for texture-preserving Bayesian reconstruction of current LdCT images. To show the feasibility of the proposed reconstruction framework, experiments using clinical patient scans were conducted. The experimental outcomes showed a dramatic gain by the a priori knowledge for LdCT image reconstruction using the commonly-used Haralick texture measures. Thus, it is conjectured that the texture-preserving LdCT reconstruction has advantages over the edge-preserving regional smoothing paradigm for texture-specific clinical applications.
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.