Evidence map›Paper›PMID 41387990›Full record

ArticleMagnetic resonance in medicine2026

What If Each Voxel Were Measured With a Different Diffusion Protocol?

Santiago Coelho, Gregory Lemberskiy, Ante Zhu, Hong-Hsi Lee, Nastaren Abad, Thomas K F Foo, Els Fieremans, Dmitry S Novikov

Abstract read
In one paragraph

Article in Magnetic resonance in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–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

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Santiago CoelhoCenter for Biomedical Imaging and Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, NY, USA.ORCID https://orcid.org/0000-0002-1039-4803
Gregory LemberskiyCenter for Biomedical Imaging and Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, NY, USA.
Ante ZhuGE HealthCare Technology and Innovation Center, Niskayuna, New York, NY, USA.ORCID https://orcid.org/0000-0001-6251-2341
Hong-Hsi LeeAthinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, South Carolina, USA.ORCID https://orcid.org/0000-0002-3663-6559
Nastaren AbadGE HealthCare Technology and Innovation Center, Niskayuna, New York, NY, USA.ORCID https://orcid.org/0000-0002-7020-0492
Thomas K F FooGE HealthCare Technology and Innovation Center, Niskayuna, New York, NY, USA.
Els FieremansCenter for Biomedical Imaging and Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, NY, USA.ORCID https://orcid.org/0000-0002-1384-8591
Dmitry S NovikovCenter for Biomedical Imaging and Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, NY, USA.ORCID https://orcid.org/0000-0002-4213-3050

Funding

TR&D 4: Revealing Microstructure: Biophysical modeling and validation for discovery and clinical careP41EB017183 · NIBIB · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI Li Feng · 2014 to 2026
$19.3M
Mesoscopic Biomarkers of Neurodegeneration with Diffusion MRIR01NS088040 · NINDS · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI FIEREMANS, ELS, NOVIKOV, DMITRY S · 2014 to 2023
$5.2M
Random Matrix Theory-Based Noise Removal in MRIR01EB027075 · NIBIB · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI FIEREMANS, ELS, NOVIKOV, DMITRY S · 2019 to 2022
$2.7M
Revealing tissue microstructure in the brain gray matter in Alzheimer's disease using in vivo high-gradient diffusion MRIDP5OD031854 · OD · MASSACHUSETTS GENERAL HOSPITAL · PI LEE, HONG HSI · 2021 to 2025
$2.1M
High Performance Gradient System for Advanced Microstructure MRI - Resubmission - 1S10OD034309 · OD · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI FIEREMANS, ELS · 2024 to 2024
$2.0M
Fast High-Resolution Microstructure Diffusion MRI Exploiting Data RedundancyR00EB036080 · NIBIB · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI Santiago Coelho · 2026 to 2026
$249k
Fast High-Resolution Microstructure Diffusion MRI Exploiting Data RedundancyK99EB036080 · NIBIB · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI COELHO, SANTIAGO · 2024 to 2025
$222k
NIBIB NIH HHS K99 EB036080NIBIB NIH HHS K99-EB036080NIBIB NIH HHS P41 EB017183NIBIB NIH HHS P41-EB017183NIBIB NIH HHS R00 EB036080NIBIB NIH HHS R01 EB027075NIBIB NIH HHS R01-EB027075NIH HHS DP5 OD031854NIH HHS S10 OD034309NINDS NIH HHS R01 NS088040NINDS NIH HHS R01-NS088040Office of the Director and National Institute of Dental & Craniofacial Research DP5-OD031854
6 · The paper itself

Abstract

purposeExpansion of diffusion MRI (dMRI) both into the realm of strong gradients and into accessible imaging with portable low-field devices brings about the challenge of gradient nonlinearities. Spatial variations of the diffusion gradients make diffusion weightings and directions non-uniform across the field of view, and deform perfect shells in the

methodsHere, we propose a protocol-independent parameter estimation (PIPE) method that enables fast parameter estimation for the most general case where each voxel is measured with a different protocol in

resultsApplied to in vivo human MRI with linear tensor encoding on a high-performance gradient system, PIPE evaluates fiber response and fODF parameters for the whole brain in the presence of significant gradient nonlinearities in under 3 min.

conclusionsPIPE enables fast parameter estimation in the presence of arbitrary gradient nonlinearities, eliminating the need to arrange dMRI in shells or to retrain the estimator for different protocols in each voxel. PIPE applies to any model based on a convolution of a voxel-wise fiber response and fODF, and data from varying

Indexed as

BrainDiffusion Magnetic Resonance ImagingImage Processing, Computer-AssistedAlgorithmsAnisotropyGray MatterHumansSpinal CordWhite Matterdiffusion MRIgradient nonlinearitiesmachine learningmicrostructurespherical convolution

Identifiers

PMID41387990
PMCPMC12850600

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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.