ArticleFrontiers in neuroinformatics2016
Intensity Inhomogeneity Correction of Structural MR Images: A Data-Driven Approach to Define Input Algorithm Parameters.
Article in Frontiers in neuroinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
34 citing papers in PubMed.
- Multimodal ultra-high-field MRI, clinical, cognitive, and genetic profiles across the ALS-FTD spectrum.Scientific data · 2026Article
- An Unsupervised Approach for Artifact Severity Scoring in Multi-Contrast MR Images.Proceedings of machine learning research · 2026Article
- Easy-to-use and easy-to-interpret quality control of 3D gradient echo T1-weighted MR acquisition sequences for improved test-retest stability of MRI-based hippocampus volumetry.Journal of Alzheimer's disease : JAD · 2025Article
- Regional gray matter volume is associated with motor imagery performance in children with and without developmental coordination disorder.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- Emo-FilM: A multimodal dataset for affective neuroscience using naturalistic stimuli.Scientific data · 2025Article
- China's social fake news database release with brain structural, functional, and behavioural measures.Scientific data · 2025Article
- Deep learning for efficient reconstruction of highly accelerated 3D FLAIR MRI in neurological deficits.Magma (New York, N.Y.) · 2025Article
- Automated quality control of T1-weighted brain MRI scans for clinical research datasets: methods comparison and design of a quality prediction classifier.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Longitudinal neuroanatomical increases from early to one-year postpartum.Brain structure & function · 2024Article
- Macro- and microvascular contributions to cerebral structural alterations in patients with asymptomatic carotid artery stenosis.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2024Article
- Longitudinal Neuroanatomical Increases from Early to One-Year Postpartum.Research square · 2024Article
- Simulating rigid head motion artifacts on brain magnitude MRI data-Outcome on image quality and segmentation of the cerebral cortex.PloS one · 2024Article
- Superpixel-ComBat modeling: A joint approach for harmonization and characterization of inter-scanner variability in T1-weighted images.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Cortical grey matter volume differences in children with developmental coordination disorder compared to typically developing children.Frontiers in human neuroscience · 2024Article
- A three-step, "brute-force" approach toward optimized affine spatial normalization.Frontiers in computational neuroscience · 2024Article
- Brain asymmetry is globally different in males and females: exploring cortical volume, area, thickness, and mean curvature.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- A multi-institutional pediatric dataset of clinical radiology MRIs by the Children's Brain Tumor Network.ArXiv · 2023Article
- Multimodal neuroimaging data from a 5-week heart rate variability biofeedback randomized clinical trial.Scientific data · 2023Article
- Global multi-center and multi-modal magnetic resonance imaging study of obsessive-compulsive disorder: Harmonization and monitoring of protocols in healthy volunteers and phantoms.International journal of methods in psychiatric research · 2023Article
- A comparison of structural morphometry in children and adults with persistent developmental stuttering.Brain communications · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Intensity non-uniformity (INU) in magnetic resonance (MR) imaging is a major issue when conducting analyses of brain structural properties. An inaccurate INU correction may result in qualitative and quantitative misinterpretations. Several INU correction methods exist, whose performance largely depend on the specific parameter settings that need to be chosen by the user. Here we addressed the question of how to select the best input parameters for a specific INU correction algorithm. Our investigation was based on the INU correction algorithm implemented in SPM, but this can be in principle extended to any other algorithm requiring the selection of input parameters. We conducted a comprehensive comparison of indirect metrics for the assessment of INU correction performance, namely the coefficient of variation of white matter (CVWM), the coefficient of variation of gray matter (CVGM), and the coefficient of joint variation between white matter and gray matter (CJV). Using simulated MR data, we observed the CJV to be more accurate than CVWM and CVGM, provided that the noise level in the INU-corrected image was controlled by means of spatial smoothing. Based on the CJV, we developed a data-driven approach for selecting INU correction parameters, which could effectively work on actual MR images. To this end, we implemented an enhanced procedure for the definition of white and gray matter masks, based on which the CJV was calculated. Our approach was validated using actual T1-weighted images collected with 1.5 T, 3 T, and 7 T MR scanners. We found that our procedure can reliably assist the selection of valid INU correction algorithm parameters, thereby contributing to an enhanced inhomogeneity correction in MR images.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.