ArticleIEEE transactions on bio-medical engineering2019
Effect of Region of Interest Size on the Repeatability of Quantitative Brain Imaging Biomarkers.
Article in IEEE transactions on bio-medical engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Anatomical zone and tissue type impacts the repeatability of quantitative MRI parameters and radiomic features for longitudinal monitoring of treatment response in the prostate.Magma (New York, N.Y.) · 2025Article
- Characterisation of Paediatric Neuroblastic Tumours by Quantitative Structural and Diffusion-Weighted MRI.Journal of clinical medicine · 2024Article
- Detecting Clinically Significant Prostate Cancer in PI-RADS 3 Lesions Using T2w-Derived Radiomics Feature Maps in 3T Prostate MRI.Current oncology (Toronto, Ont.) · 2024Article
- Can we predict pathology without surgery? Weighing the added value of multiparametric MRI and whole prostate radiomics in integrative machine learning models.European radiology · 2024Article
- Article
- Comparison of region-of-interest delineation methods for diffusion tensor imaging in patients with cervical spondylotic radiculopathy.BMC musculoskeletal disorders · 2022Article
- Quantification of individual remyelination during short-term disease course by synthetic magnetic resonance imaging.Brain communications · 2022Article
- Stability of Radiomic Features across Different Region of Interest Sizes-A CT and MR Phantom Study.Tomography (Ann Arbor, Mich.) · 2021Article
- Brain functional network modeling and analysis based on fMRI: a systematic review.Cognitive neurodynamics · 2021Review
- Daily Intravoxel Incoherent Motion (IVIM) In Prostate Cancer Patients During MR-Guided Radiotherapy-A Multicenter Study.Frontiers in oncology · 2021Article
- Variability and Standardization of Quantitative Imaging: Monoparametric to Multiparametric Quantification, Radiomics, and Artificial Intelligence.Investigative radiology · 2020Review
- Quantitative Imaging Informatics for Cancer Research.JCO clinical cancer informatics · 2020Article
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Authors and funding
4 authors.
Funding
Abstract
In the repeatability analysis, when the measurement is the mean value of a parametric map within a region of interest (ROI), the ROI size becomes important as by increasing the size, the measurement will have a smaller variance. This is important in decision-making in prospective clinical studies of brain when the ROI size is variable, e.g., in monitoring the effect of treatment on lesions by quantitative MRI, and in particular when the ROI is small, e.g., in the case of brain lesions in multiple sclerosis. Thus, methods to estimate repeatability measures for arbitrary sizes of ROI are desired. We propose a statistical model of the values of parametric map within the ROI and a method to approximate the model parameters, based on which we estimate a number of repeatability measures including repeatability coefficient, coefficient of variation, and intraclass correlation coefficient for an ROI with an arbitrary size. We also show how this gives an insight into related problems such as spatial smoothing in voxel-wise analysis. Experiments are conducted on simulated data as well as on scan-rescan brain MRI of healthy subjects. The main application of this study is the adjustment of the decision threshold based on the lesion size in treatment monitoring.
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