ArticleNeuroinformatics2016
Quantitative Evaluation of Intensity Inhomogeneity Correction Methods for Structural MR Brain Images.
Article in Neuroinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- An fMRI dataset of verbalized spontaneous thought with annotated transcripts and self-report trait measures.bioRxiv : the preprint server for biology · 2026Article
- A geometric function theoretic approach to MRI distortion correction using Miller Ross Poisson series.Scientific reports · 2026Article
- Complementary MR measures of white matter and their relation to cardiovascular health and cognition.Scientific reports · 2025Article
- SynSpine: an automated workflow for the generation of longitudinal spinal cord synthetic MRI data.Frontiers in neuroinformatics · 2025Article
- Brain age prediction using combined deep convolutional neural network and multi-layer perceptron algorithms.Scientific reports · 2023Article
- 3D Capsule Networks for Brain Image Segmentation.AJNR. American journal of neuroradiology · 2023Article
- Comparing 3D, 2.5D, and 2D Approaches to Brain Image Auto-Segmentation.Bioengineering (Basel, Switzerland) · 2023Article
- Linking lesions in sensorimotor cortex to contralateral hand function in multiple sclerosis: a 7 T MRI study.Brain : a journal of neurology · 2022Article
- Full-field MRI measurements of in-vivo positional brain shift reveal the significance of intra-cranial geometry and head orientation for stereotactic surgery.Scientific reports · 2021Article
- Intensity non-uniformity correction in MR imaging using residual cycle generative adversarial network.Physics in medicine and biology · 2020Article
- Methodological Considerations for Neuroimaging in Deep Brain Stimulation of the Subthalamic Nucleus in Parkinson's Disease Patients.Journal of clinical medicine · 2020Article
- Towards an optimised processing pipeline for diffusion magnetic resonance imaging data: Effects of artefact corrections on diffusion metrics and their age associations in UK Biobank.Human brain mapping · 2019Article
- Context Dependent Fuzzy Associated Statistical Model for Intensity Inhomogeneity Correction From Magnetic Resonance Images.IEEE journal of translational engineering in health and medicine · 2019Article
- Four-way multimodal fusion of 7 T imaging data using an mCCA+jICA model in first-episode schizophrenia.Human brain mapping · 2018Article
- A Spatial Registration Toolbox for Structural MR Imaging of the Aging Brain.Neuroinformatics · 2018Article
- Test-retest reliability and concurrent validity of in vivo myelin content indices: Myelin water fraction and calibrated THuman brain mapping · 2017Article
- Intensity Inhomogeneity Correction of Structural MR Images: A Data-Driven Approach to Define Input Algorithm Parameters.Frontiers in neuroinformatics · 2016Article
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Authors and funding
3 authors.
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Abstract
The correction of intensity non-uniformity (INU) in magnetic resonance (MR) images is extremely important to ensure both within-subject and across-subject reliability. Here we tackled the problem of objectively comparing INU correction techniques for T1-weighted images, which are the most commonly used in structural brain imaging. We focused our investigations on the methods integrated in widely used software packages for MR data analysis: FreeSurfer, BrainVoyager, SPM and FSL. We used simulated data to assess the INU fields reconstructed by those methods for controlled inhomogeneity magnitudes and noise levels. For each method, we evaluated a wide range of input parameters and defined an enhanced configuration associated with best reconstruction performance. By comparing enhanced and default configurations, we found that the former often provide much more accurate results. Accordingly, we used enhanced configurations for a more objective comparison between methods. For different levels of INU magnitude and noise, SPM and FSL, which integrate INU correction with brain segmentation, generally outperformed FreeSurfer and BrainVoyager, whose methods are exclusively dedicated to INU correction. Nonetheless, accurate INU field reconstructions can be obtained with FreeSurfer on images with low noise and with BrainVoyager for slow and smooth inhomogeneity profiles. Our study may prove helpful for an accurate selection of the INU correction method to be used based on the characteristics of actual MR data.
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