Evidence map›Paper›PMID 26691524›Full record

ArticleNeuroImage2016

Kernel regression estimation of fiber orientation mixtures in diffusion MRI.

Ryan P Cabeen, Mark E Bastin, David H Laidlaw

Abstract read
In one paragraph

Article in NeuroImage, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

24 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Ryan P CabeenDepartment of Computer Science, Brown University, Providence, RI, USA. Electronic address: cabeen@cs.brown.edu.
Mark E BastinCentre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.
David H LaidlawDepartment of Computer Science, Brown University, Providence, RI, USA.

Funding

Neuropathogenesis of clade C HIV in South AfricaR01MH085604 · NIMH · UNIVERSITY OF MISSOURI-ST. LOUIS · PI PAUL, ROBERT HARRIS · 2009 to 2013
$2.9M
Neuromarkers of age-related cognitive declineR01NS052470 · NINDS · UNIVERSITY OF MISSOURI-ST. LOUIS · PI PAUL, ROBERT HARRIS · 2007 to 2011
$2.7M
MRI+DTI-Based Tools for Analyzing White Matter VariationR01EB004155 · NIBIB · BROWN UNIVERSITY · PI LAIDLAW, DAVID H · 2005 to 2008
$2.0M
NIBIB NIH HHS R01 EB004155NIMH NIH HHS R01 MH085604NINDS NIH HHS R01 NS052470
6 · The paper itself

Abstract

We present and evaluate a method for kernel regression estimation of fiber orientations and associated volume fractions for diffusion MR tractography and population-based atlas construction in clinical imaging studies of brain white matter. This is a model-based image processing technique in which representative fiber models are estimated from collections of component fiber models in model-valued image data. This extends prior work in nonparametric image processing and multi-compartment processing to provide computational tools for image interpolation, smoothing, and fusion with fiber orientation mixtures. In contrast to related work on multi-compartment processing, this approach is based on directional measures of divergence and includes data-adaptive extensions for model selection and bilateral filtering. This is useful for reconstructing complex anatomical features in clinical datasets analyzed with the ball-and-sticks model, and our framework's data-adaptive extensions are potentially useful for general multi-compartment image processing. We experimentally evaluate our approach with both synthetic data from computational phantoms and in vivo clinical data from human subjects. With synthetic data experiments, we evaluate performance based on errors in fiber orientation, volume fraction, compartment count, and tractography-based connectivity. With in vivo data experiments, we first show improved scan-rescan reproducibility and reliability of quantitative fiber bundle metrics, including mean length, volume, streamline count, and mean volume fraction. We then demonstrate the creation of a multi-fiber tractography atlas from a population of 80 human subjects. In comparison to single tensor atlasing, our multi-fiber atlas shows more complete features of known fiber bundles and includes reconstructions of the lateral projections of the corpus callosum and complex fronto-parietal connections of the superior longitudinal fasciculus I, II, and III.

Indexed as

AdultAgedAlgorithmsAnatomy, ArtisticAtlases as TopicBrainBrain MappingDiffusion Tensor ImagingFemaleHumansImage Processing, Computer-AssistedMaleMiddle AgedModels, NeurologicalNeural PathwaysWhite MatterAtlasingDiffusion MRIImage processingInterpolationMulti-fiberTractography

Identifiers

PMID26691524
PMCPMC4870009

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Registered trials

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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.