ArticlePhysics in medicine and biology2020
Noise reduction in CT image using prior knowledge aware iterative denoising.
Article in Physics in medicine and biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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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.
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Who cites it
8 citing papers in PubMed, 15 citations in OpenAlex.
- Data-centric strategies enable cross-center generalization of convolutional neural networks for CT brain extraction.Frontiers in neurology · 2026Article
- ErisNet: A Deep Learning Model for Noise Reduction in CT Images.Bioengineering (Basel, Switzerland) · 2025Article
- CT image denoising methods for image quality improvement and radiation dose reduction.Journal of applied clinical medical physics · 2024Review
- Article
- Diagnostic usefulness of cone-beam computed tomography versus multi-detector computed tomography for sinonasal structure evaluation.Laryngoscope investigative otolaryngology · 2022Article
- Diagnostic Accuracy and Failure Mode Analysis of a Deep Learning Algorithm for the Detection of Intracranial Hemorrhage.Journal of the American College of Radiology : JACR · 2021Article
- Quantitative Knee Arthrography in a Large Animal Model of Osteoarthritis Using Photon-Counting Detector CT.Investigative radiology · 2020Article
- Quantification of Coronary Calcification using High-Resolution Photon-Counting-Detector CT and an Image Domain Denoising Algorithm.Proceedings of SPIE--the International Society for Optical EngineeringArticle
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
The clinical demand for low image noise often limits the slice thickness used in many CT applications. However, a thick-slice image is more susceptible to longitudinal partial volume effects, which can blur key anatomic structures and pathologies of interest. In this work, we develop a prior knowledge aware iterative denoising (PKAID) framework that utilizes spatial data redundancy in the slice increment direction to generate low-noise, thin-slice images, and demonstrate its application in non-contrast head CT exams. The proposed technique takes advantage of the low noise of thicker images and exploits the structural similarity between the thick- and thin-slice images to reduce noise in the thin-slice image. Phantom data and patient cases (
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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.