ArticleFrontiers in oncology2021
The Impact of Artificial Intelligence CNN Based Denoising on FDG PET Radiomics.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed.
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- Deep learning image enhancement algorithms in PET/CT imaging: a phantom and sarcoma patient radiomic evaluation.European journal of nuclear medicine and molecular imaging · 2025Article
- Deep Learning Approaches for Automated Prediction of Treatment Response in Non-Small-Cell Lung Cancer Patients Based on CT and PET Imaging.Tomography (Ann Arbor, Mich.) · 2025Review
- Robust whole-body PET image denoising using 3D diffusion models: evaluation across various scanners, tracers, and dose levels.European journal of nuclear medicine and molecular imaging · 2025Article
- Single-scan adaptive graph filtering for dynamic PET denoising by exploring intrinsic spatio-temporal structure.Frontiers in medicine · 2025Article
- AI-Based Noise-Reduction Filter for Whole-Body Planar Bone Scintigraphy Reliably Improves Low-Count Images.Diagnostics (Basel, Switzerland) · 2024Article
- The impact of introducing deep learning based [EJNMMI research · 2024Article
- Deep learning generation of preclinical positron emission tomography (PET) images from low-count PET with task-based performance assessment.Medical physics · 2024Article
- Human-Machine Collaboration in Diagnostics: Exploring the Synergy in Clinical Imaging with Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2023Article
- Deep Learning Denoising Improves and Homogenizes Patient [Diagnostics (Basel, Switzerland) · 2023Article
- Artificial intelligence-basedFrontiers in medicine · 2023Article
- Development and verification of radiomics framework for computed tomography image segmentation.Medical physics · 2022Article
- Artificial intelligence-based PET denoising could allow a two-fold reduction in [European journal of nuclear medicine and molecular imaging · 2022Article
- Computed Tomography Image Features under Denoising Algorithm for Benign and Malignant Diagnosis of Renal Parenchymal Tumor.Contrast media & molecular imaging · 2022Article
- The Impact of Resampling and Denoising Deep Learning Algorithms on Radiomics in Brain Metastases MRI.Cancers · 2021Article
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6 authors.
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Abstract
backgroundWith a constantly increasing number of diagnostic images performed each year, Artificial Intelligence (AI) denoising methods offer an opportunity to respond to the growing demand. However, it may affect information in the image in an unknown manner. This study quantifies the effect of AI-based denoising on FDG PET textural information in comparison to a convolution with a standard gaussian postfilter (EARL1).
methodsThe study was carried out on 113 patients who underwent a digital FDG PET/CT (VEREOS, Philips Healthcare). 101 FDG avid lesions were segmented semi-automatically by a nuclear medicine physician. VOIs in the liver and lung as reference organs were contoured. PET textural features were extracted with pyradiomics. Texture features from AI denoised and EARL1
resultsThe ratio of concordant features was 90/104 (86.5%) in AI denoised
conclusionsApplying an AI-based denoising on FDG PET images maintains most of the lesion's texture information in contrast to EARL1-compatible Gaussian filter. Predictive features of a trained model could be thus the same, however with an adapted threshold. Artificial intelligence based denoising in PET is a very promising approach as it adapts the denoising in function of the tissue type, preserving information where it should.
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