Evidence map›Paper›PMID 35225365›Full record

ArticleMagnetic resonance in medicine2022

Performance of orientation distribution function-fingerprinting with a biophysical multicompartment diffusion model.

Patryk Filipiak, Timothy Shepherd, Ying-Chia Lin, Dimitris G Placantonakis, Fernando E Boada, Steven H Baete

Open access · greenAbstract read
In one paragraph

Article in Magnetic resonance in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.2field-weighted citation impact, top 24% of its field
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

9 citing papers in PubMed, 10 citations in OpenAlex.

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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

6 authors at 3 institutions in 1 country.

Patryk FilipiakCenter for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Langone Health, New York, New York, USA.ORCID 0000-0003-3429-4572
Timothy ShepherdCenter for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Langone Health, New York, New York, USA.
Ying-Chia LinCenter for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Langone Health, New York, New York, USA.ORCID 0000-0001-7243-0980
Dimitris G PlacantonakisDepartment of Neurosurgery, Perlmutter Cancer Center, Neuroscience Institute, Kimmel Center for Stem Cell Biology, NYU Langone Health, New York, New York, USA.
Fernando E BoadaCenter for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Langone Health, New York, New York, USA.
Steven H BaeteCenter for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Langone Health, New York, New York, USA.
Advanced Imaging Research (United States) · USNYU Langone Health · USStanford Medicine · US

Funding

Mapping the Human Connectome: Structure, Function, and HeritabilityU54MH091657 · NIMH · WASHINGTON UNIVERSITY · PI UGURBIL, KAMIL, VAN ESSEN, DAVID C · 2010 to 2014
$34.7M
Research Education ComponentP30AG066512 · NIA · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI Mary Sherman Mittelman · 2020 to 2026
$28.4M
TR&D 4: Revealing Microstructure: Biophysical modeling and validation for discovery and clinical careP41EB017183 · NIBIB · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI Hersh Chandarana · 2014 to 2026
$19.3M
Fingerprinting-Based Neuronal Fiber Identification in Brain SurgeryR01EB028774 · NIBIB · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI BAETE, STEVEN H. · 2020 to 2023
$1.9M
Q-Space Trajectories for Fast Diffusion Spectrum-based High-Definition Fiber TracR01NS082436 · NINDS · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI BOADA, FERNANDO E · 2014 to 2017
$1.8M
NIA NIH HHS P30 AG066512NIBIB NIH HHS P41 EB017183NIBIB NIH HHS R01 EB028774NIMH NIH HHS U54 MH091657NINDS NIH HHS R01 NS082436
6 · The paper itself

Abstract

purposeOrientation Distribution Function (ODF) peak finding methods typically fail to reconstruct fibers crossing at shallow angles below 40°, leading to errors in tractography. ODF-Fingerprinting (ODF-FP) with the biophysical multicompartment diffusion model allows for breaking this barrier.

methodsA randomized mechanism to generate a multidimensional ODF-dictionary that covers biologically plausible ranges of intra- and extra-axonal diffusivities and fraction volumes is introduced. This enables ODF-FP to address the high variability of brain tissue. The performance of the proposed approach is evaluated on both numerical simulations and a reconstruction of major fascicles from high- and low-resolution in vivo diffusion images.

resultsODF-FP with the suggested modifications correctly identifies fibers crossing at angles as shallow as 10 degrees in the simulated data. In vivo, our approach reaches 56% of true positives in determining fiber directions, resulting in visibly more accurate reconstruction of pyramidal tracts, arcuate fasciculus, and optic radiations than the state-of-the-art techniques. Moreover, the estimated diffusivity values and fraction volumes in corpus callosum conform with the values reported in the literature.

conclusionThe modified ODF-FP outperforms commonly used fiber reconstruction methods at shallow angles, which improves deterministic tractography outcomes of major fascicles. In addition, the proposed approach allows for linearization of the microstructure parameters fitting problem.

Indexed as

AlgorithmsWhite MatterBrainCorpus CallosumDiffusion Magnetic Resonance ImagingImage Processing, Computer-Assistedcrossing fibersdiffusion MRIfingerprintingmicrostructure model fittingmulticompartment diffusion modelorientation distribution functionshallow anglestractography

Identifiers

PMID35225365
PMCPMC9142101
OpenAlexW4214483574

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.