ArticleJournal of medical imaging (Bellingham, Wash.)2018
Accounting for reconstruction kernel-induced variability in CT radiomic features using noise power spectra.
Article in Journal of medical imaging (Bellingham, Wash.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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30 citing papers in PubMed.
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- Cross-Software Radiomic Feature Robustness Assessed by Hierarchical Clustering and Composite Index Analysis: A Multi-Cancer Study on Colorectal and Liver Lesions.Bioengineering (Basel, Switzerland) · 2025Article
- Robustness of radiomics within photon-counting detector CT: impact of acquisition and reconstruction factors.European radiology · 2025Article
- Annotated test-retest dataset of lung cancer CT scan images reconstructed at multiple imaging parameters.Scientific data · 2024Article
- Magnetic Resonance-Guided Cancer Therapy Radiomics and Machine Learning Models for Response Prediction.Tomography (Ann Arbor, Mich.) · 2024Review
- Rank acquisition impact on radiomics estimation (AсquIRE) in chest CT imaging: A retrospective multi-site, multi-use-case study.Computer methods and programs in biomedicine · 2024Article
- Nuclear medicine radiomics in digestive system tumors: Concept, applications, challenges, and future perspectives.View (Beijing, China) · 2023Article
- Radiomic phenotyping of the lung parenchyma in a lung cancer screening cohort.Scientific reports · 2023Article
- Uniqueness of radiomic features in non-small cell lung cancer.Journal of applied clinical medical physics · 2022Article
- A radiomics approach for lung nodule detection in thoracic CT images based on the dynamic patterns of morphological variation.European radiology · 2022Article
- Radiomics of Patients with Locally Advanced Rectal Cancer: Effect of Preprocessing on Features Estimation from Computed Tomography Imaging.BioMed research international · 2022Article
- Sinogram-based deep learning image reconstruction technique in abdominal CT: image quality considerations.European radiology · 2021Article
- Reproducibility of lung nodule radiomic features: Multivariable and univariable investigations that account for interactions between CT acquisition and reconstruction parameters.Medical physics · 2021Article
- Article
- Radiogenomics in brain, breast, and lung cancer: opportunities and challenges.Journal of medical imaging (Bellingham, Wash.) · 2021Review
- Distinguishing granulomas from adenocarcinomas by integrating stable and discriminating radiomic features on non-contrast computed tomography scans.European journal of cancer (Oxford, England : 1990) · 2021Article
- Standardization of histogram- and gray-level co-occurrence matrices-based radiomics in the presence of blur and noise.Physics in medicine and biology · 2021Article
- Impact of CT convolution kernel on robustness of radiomic features for different lung diseases and tissue types.The British journal of radiology · 2021Article
- Understanding Sources of Variation to Improve the Reproducibility of Radiomics.Frontiers in oncology · 2021Review
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7 authors.
Funding
Abstract
Large variability in computed tomography (CT) radiomics feature values due to CT imaging parameters can have subsequent implications on the prognostic or predictive significance of these features. Here, we investigated the impact of pitch, dose, and reconstruction kernel on CT radiomic features. Moreover, we introduced correction factors to reduce feature variability introduced by reconstruction kernels. The credence cartridge radiomics and American College of Radiology (ACR) phantoms were scanned on five different scanners. ACR phantom was used for 3-D noise power spectrum (NPS) measurements to quantify correlated noise. The coefficient of variation (COV) was used as the variability assessment metric. The variability in texture features due to different kernels was reduced by applying the NPS peak frequency and region of interest (ROI) maximum intensity as correction factors. Most texture features were dose independent but were strongly kernel dependent, which is demonstrated by a significant shift in NPS peak frequency among kernels. Percentage improvement in robustness was calculated for each feature from original and corrected %COV values. Percentage improvements in robustness of 19 features were in the range of 30% to 78% after corrections. We show that NPS peak frequency and ROI maximum intensity can be used as correction factors to reduce variability in CT texture feature values due to reconstruction kernels.
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