ArticleNeuroImage2018
Using 3D spatial correlations to improve the noise robustness of multi component analysis of 3D multi echo quantitative T2 relaxometry data.
Article in NeuroImage, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed.
- Myelin water and tensor-valued diffusion imaging: (How) are they related?Magnetic resonance in medicine · 2025Article
- Microstructure-Informed Myelin Mapping (MIMM) from routine multi-echo gradient echo data using multiscale physics modeling of iron and myelin effects and QSM.Magnetic resonance in medicine · 2025Article
- An Efficient Algorithm for Spatial-Spectral Partial Volume Compartment Mapping with Applications to Multicomponent Diffusion and Relaxation MRI.IEEE transactions on computational imaging · 2025Article
- Myelin-water imaging and multi-shell diffusion-weighted imaging in adults with adrenoleukodystrophy.Brain communications · 2025Article
- Microstructure-informed brain tissue classification using clustering of quantitative MRI measures.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Improved myelin water fraction mapping with deep neural networks using synthetically generated 3D data.Medical image analysis · 2024Article
- Improved myelin water imaging using BImaging neuroscience (Cambridge, Mass.) · 2024Article
- The CALIPR framework for highly accelerated myelin water imaging with improved precision and sensitivity.Science advances · 2023Article
- Improved estimation of myelin water fractions with learned parameter distributions.Magnetic resonance in medicine · 2021Article
- An atlas for human brain myelin content throughout the adult life span.Scientific reports · 2021Article
- Fast multicomponent 3D-TNMR in biomedicine · 2020Article
- Fast TMagnetic resonance in medicine · 2020Article
- Fast multi-component analysis using a joint sparsity constraint for MR fingerprinting.Magnetic resonance in medicine · 2020Article
- The Myelin Water Fraction Serves as a Marker for Age-Related Myelin Alterations in the Cerebral White Matter - A Multiparametric MRI Aging Study.Frontiers in neuroscience · 2020Article
- Age-Related Measurements of the Myelin Water Fraction derived from 3D multi-echo GRASE reflect Myelin Content of the Cerebral White Matter.Scientific reports · 2018Article
- Myelin water imaging to detect demyelination and remyelination and its validation in pathology.Brain pathology (Zurich, Switzerland) · 2018Review
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
Abstract
purposeWe present a computationally feasible and iterative multi-voxel spatially regularized algorithm for myelin water fraction (MWF) reconstruction. This method utilizes 3D spatial correlations present in anatomical/pathological tissues and underlying B1
methodsSimulated data and in vivo data acquired using 3D non-selective multi-echo spin echo (3DNS-MESE) were used to compare the reconstruction quality of the proposed approach against those of the popular algorithm (the method by Prasloski et al.) and our previously proposed 2D multi-slice spatial regularization spatial regularization approach. We also investigated whether the inter-sequence correlations and agreements improved as a result of the proposed approach. MWF-quantifications from two sequences, 3DNS-MESE vs 3DNS-gradient and spin echo (3DNS-GRASE), were compared for both reconstruction approaches to assess correlations and agreements between inter-sequence MWF-value pairs. MWF values from whole-brain data of six volunteers and two multiple sclerosis patients are being reported as well.
resultsIn comparison with competing approaches such as Prasloski's method or our previously proposed 2D multi-slice spatial regularization method, the proposed method showed better agreements with simulated truths using regression analyses and Bland-Altman analyses. For 3DNS-MESE data, MWF-maps reconstructed using the proposed algorithm provided better depictions of white matter structures in subcortical areas adjoining gray matter which agreed more closely with corresponding contrasts on T2-weighted images than MWF-maps reconstructed with the method by Prasloski et al. We also achieved a higher level of correlations and agreements between inter-sequence (3DNS-MESE vs 3DNS-GRASE) MWF-value pairs.
conclusionThe proposed algorithm provides more noise-robust fits to T2-decay data and improves MWF-quantifications in white matter structures especially in the sub-cortical white matter and major white matter tract regions.
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.