ArticleMagnetic resonance in medicine2013
Assessment of bias in experimentally measured diffusion tensor imaging parameters using SIMEX.
Article in Magnetic resonance in medicine, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Article
- The sensitivity of diffusion MRI to microstructural properties and experimental factors.Journal of neuroscience methods · 2021Review
- Joint BMagnetic resonance imaging · 2020Article
- A deep learning approach to estimation of subject-level bias and variance in high angular resolution diffusion imaging.Magnetic resonance imaging · 2019Article
- Model-based reconstruction framework for correction of signal pile-up and geometric distortions in prostate diffusion MRI.Magnetic resonance in medicine · 2019Article
- Empirical single sample quantification of bias and variance in Q-ball imaging.Magnetic resonance in medicine · 2018Article
- Evaluation of inter-site bias and variance in diffusion-weighted MRI.Proceedings of SPIE--the International Society for Optical Engineering · 2018Article
- Vanderbilt University Institute of Imaging Science Center for Computational Imaging XNAT: A multimodal data archive and processing environment.NeuroImage · 2016Article
- Evaluation of statistical inference on empirical resting state fMRI.IEEE transactions on bio-medical engineering · 2014Article
- Correcting power and p-value calculations for bias in diffusion tensor imaging.Magnetic resonance imaging · 2013Article
- Out-of-atlas likelihood estimation using multi-atlas segmentation.Medical physics · 2013Article
- Integration of XNAT/PACS, DICOM, and Research Software for Automated Multi-modal Image Analysis.Proceedings of SPIE--the International Society for Optical Engineering · 2013Article
- Article
- Do We Really Need Robust and Alternative Inference Methods for Brain MRI?Multimodal brain image analysis : second International Workshop, MBIA 2012, held in conjunction with MICCAI 2012, Nice, France, October 1-5, 2012 : proceedings. MBIA (Workshop) (2nd : 2012 : Nice, France) · 2012Article
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4 authors.
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Abstract
Diffusion tensor imaging enables in vivo investigation of tissue cytoarchitecture through parameter contrasts sensitive to water diffusion barriers at the micrometer level. Parameters are derived through an estimation process that is susceptible to noise and artifacts. Estimated parameters (e.g., fractional anisotropy) exhibit both variability and bias relative to the true parameter value estimated from a hypothetical noise-free acquisition. Herein, we present the use of the simulation and extrapolation (SIMEX) approach for post hoc assessment of bias in a massively univariate imaging setting and evaluate the potential of a SIMEX-based bias correction. Using simulated data with known truth models, spatially varying fractional anisotropy bias error maps are evaluated on two independent and highly differentiated case studies. The stability of SIMEX and its distributional properties are further evaluated on 42 empirical diffusion tensor imaging datasets. Using gradient subsampling, an empirical experiment with a known true outcome is designed and SIMEX performance is compared to the original estimator. With this approach, we find SIMEX bias estimates to be highly accurate offering significant reductions in parameter bias for individual datasets and greater accuracy in averaged population-based estimates.
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