ArticlePloS one2014
A robust post-processing workflow for datasets with motion artifacts in diffusion kurtosis imaging.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed.
- Utilizing multivariate pattern analysis to uncover the neurobiological underpinnings of communication disorders in children with bilateral spastic cerebral palsy: insights from morphological and structural connectivity changes.Quantitative imaging in medicine and surgery · 2026Article
- Structural brain changes in subacute spinal cord injury: an analysis of diffusion kurtosis imaging and diffusion tensor imaging metrics with clinical correlation.Frontiers in neuroscience · 2025Article
- Analysis of white matter tract integrity using diffusion kurtosis imaging reveals the correlation of white matter microstructural abnormalities with cognitive impairment in type 2 diabetes mellitus.Frontiers in endocrinology · 2024Article
- Mean kurtosis-Curve (MK-Curve) correction improves the test-retest reproducibility of diffusion kurtosis imaging at 3 T.NMR in biomedicine · 2023Article
- Test-retest reliability of diffusion kurtosis imaging metrics in the healthy adult brain.Neuroimage. Reports · 2022Article
- Widespread effects of dMRI data quality on diffusion measures in children.Human brain mapping · 2022Article
- Mapping white matter maturational processes and degrees on neonates by diffusion kurtosis imaging with multiparametric analysis.Human brain mapping · 2022Article
- Inter-site harmonization based on dual generative adversarial networks for diffusion tensor imaging: application to neonatal white matter development.Biomedical engineering online · 2020Article
- Observational
- Early white matter injuries associated with dopamine transporter dysfunction in patients with acute CO intoxication: A diffusion kurtosis imaging and Tc-99m TRODAT-1 SPECT study.European radiology · 2019Article
- Diffusion MRI of white matter microstructure development in childhood and adolescence: Methods, challenges and progress.Developmental cognitive neuroscience · 2018Review
- Characterization of Extensive Microstructural Variations Associated with Punctate White Matter Lesions in Preterm Neonates.AJNR. American journal of neuroradiology · 2017Observational
- Differentiating T2 hyperintensity in neonatal white matter by two-compartment model of diffusional kurtosis imaging.Scientific reports · 2016Article
- The impact of quality assurance assessment on diffusion tensor imaging outcomes in a large-scale population-based cohort.NeuroImage · 2016Article
- A simple noise correction scheme for diffusional kurtosis imaging.Magnetic resonance imaging · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe aim of this study was to develop a robust post-processing workflow for motion-corrupted datasets in diffusion kurtosis imaging (DKI). MATERIALS AND
methodsThe proposed workflow consisted of brain extraction, rigid registration, distortion correction, artifacts rejection, spatial smoothing and tensor estimation. Rigid registration was utilized to correct misalignments. Motion artifacts were rejected by using local Pearson correlation coefficient (LPCC). The performance of LPCC in characterizing relative differences between artifacts and artifact-free images was compared with that of the conventional correlation coefficient in 10 randomly selected DKI datasets. The influence of rejected artifacts with information of gradient directions and b values for the parameter estimation was investigated by using mean square error (MSE). The variance of noise was used as the criterion for MSEs. The clinical practicality of the proposed workflow was evaluated by the image quality and measurements in regions of interest on 36 DKI datasets, including 18 artifact-free (18 pediatric subjects) and 18 motion-corrupted datasets (15 pediatric subjects and 3 essential tremor patients).
resultsThe relative difference between artifacts and artifact-free images calculated by LPCC was larger than that of the conventional correlation coefficient (p<0.05). It indicated that LPCC was more sensitive in detecting motion artifacts. MSEs of all derived parameters from the reserved data after the artifacts rejection were smaller than the variance of the noise. It suggested that influence of rejected artifacts was less than influence of noise on the precision of derived parameters. The proposed workflow improved the image quality and reduced the measurement biases significantly on motion-corrupted datasets (p<0.05).
conclusionThe proposed post-processing workflow was reliable to improve the image quality and the measurement precision of the derived parameters on motion-corrupted DKI datasets. The workflow provided an effective post-processing method for clinical applications of DKI in subjects with involuntary movements.
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.