ArticleJournal of digital imaging2021
MR Denoising Increases Radiomic Biomarker Precision and Reproducibility in Oncologic Imaging.
Article in Journal of digital imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Image Quality Standardization in Radiomics: A Systematic Review of Artifacts, Variability, and Feature Stability.Sensors (Basel, Switzerland) · 2026Pooled it
- PET|MR Open Source Radiomics Models for Treatment Outcome Prediction in Recurrent GBM.Cancers · 2026Article
- Deep Learning Auto-segmentation of Diffuse Midline Glioma on Multimodal Magnetic Resonance Images.Journal of imaging informatics in medicine · 2026Article
- Risk stratification in neuroblastoma patients through machine learning in the multicenter PRIMAGE cohort.Frontiers in oncology · 2025Article
- Reproducibility Analysis of Radiomic Features on T2-weighted MR Images after Processing and Segmentation Alterations in Neuroblastoma Tumors.Radiology. Artificial intelligence · 2024Article
- Imaging biomarkers and radiomics in pediatric oncology: a view from the PRIMAGE (PRedictive In silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers) project.Pediatric radiology · 2024Review
- Deep learning-based automatic segmentation of meningioma from T1-weighted contrast-enhanced MRI for preoperative meningioma differentiation using radiomic features.BMC medical imaging · 2024Article
- Pilot Lightweight Denoising Algorithm for Multiple Sclerosis on Spine MRI.Journal of digital imaging · 2023Article
- Data infrastructures for AI in medical imaging: a report on the experiences of five EU projects.European radiology experimental · 2023Review
- Pancreatic cancer, radiomics and artificial intelligence.The British journal of radiology · 2022Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Several noise sources, such as the Johnson-Nyquist noise, affect MR images disturbing the visualization of structures and affecting the subsequent extraction of radiomic data. We evaluate the performance of 5 denoising filters (anisotropic diffusion filter (ADF), curvature flow filter (CFF), Gaussian filter (GF), non-local means filter (NLMF), and unbiased non-local means (UNLMF)), with 33 different settings, in T2-weighted MR images of phantoms (N = 112) and neuroblastoma patients (N = 25). Filters were discarded until the most optimal solutions were obtained according to 3 image quality metrics: peak signal-to-noise ratio (PSNR), edge-strength similarity-based image quality metric (ESSIM), and noise (standard deviation of the signal intensity of a region in the background area). The selected filters were ADFs and UNLMs. From them, 107 radiomics features preservation at 4 progressively added noise levels were studied. The ADF with a conductance of 1 and 2 iterations standardized the radiomic features, improving reproducibility and quality metrics.
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