ArticleImaging neuroscience (Cambridge, Mass.)2025
Article in Imaging neuroscience (Cambridge, Mass.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Informed Dictionary-Guided Monte Carlo Inversion for Robust and Reproducible Multidimensional MRI.Magnetic resonance in medicine · 2026Article
- HARMONIZATION MITIGATES DIFFUSION MRI SCANNER EFFECTS IN INFANCY: INSIGHTS FROM THE HEALTHY BRAIN AND CHILD DEVELOPMENT (HBCD) STUDY.Proceedings. IEEE International Symposium on Biomedical Imaging · 2026Article
- Article
- Did you know? State-of-the-art preprocessing diffusion MRI data can improve tractography.Brain structure & function · 2026Review
- An Open, Fully-processed, Longitudinal Data Resource to Study Brain Development and Transdiagnostic Executive Function.bioRxiv : the preprint server for biology · 2025Article
- In vivo disentanglement of diffusion frequency-dependence, tensor shape, and relaxation using multidimensional MRI.Human brain mapping · 2024Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diffusion MRI (dMRI) data suffer from a number of artifacts, including, but not limited to, low SNR, Gibbs ringing, bulk subject motion, within volume motion, eddy-current distortions, susceptibility-induced EPI distortions, and ghost artifacts. Appropriate pre-processing of diffusion-weighted images prior to model fitting is vital for accurate quantitative analysis. Over the years, the nature of dMRI data has evolved (smaller voxel sizes, significantly larger number of volumes and b-values, wider variety of acquisition paradigms, etc.) as have the required processing tools. Additionally, very large multi-site dMRI studies, on potentially uncooperative subjects (young children, geriatric populations, patients with movement disorders, etc.), have increased the necessity for dMRI processing pipelines that are fast, robustly capable of handling a variety of artifacts/distortions, and that have summary reporting capabilities to pinpoint problematic data.
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.