Evidence map›Paper›PMID 42487251›Full record

ArticleMagnetic resonance in medicine2026

Fast Submillimeter Whole-Brain T

Sreekanth Madhusoodhanan Nair, Bryan Quah, Jae W Song, Jin Jin, Chang Gao, Fei Han, Elaina Luskin, Michael Luu, Brian Renner, Ke Cheng Liu and 19 more

Abstract read
PubMed Full text at PMC Publisher
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. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

29 authors.

Sreekanth Madhusoodhanan NairNeuroimaging Program, Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, California, USA.ORCID https://orcid.org/0000-0003-3549-3848
Bryan QuahNeuroimaging Program, Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, California, USA.
Jae W SongDepartment of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Jin JinSiemens Healthineers Pty Ltd, Brisbane, Australia.ORCID https://orcid.org/0000-0001-6357-9329
Chang GaoSiemens Healthineers, Malvern, Pennsylvania, USA.
Fei HanSiemens Healthineers, Malvern, Pennsylvania, USA.
Elaina LuskinNeuroimaging Program, Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, California, USA.
Michael LuuBiostatistics Shared Resource, Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, California, USA.
Brian RennerNeuroimaging Program, Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, California, USA.
Ke Cheng LiuSiemens Healthineers, Malvern, Pennsylvania, USA.
Sunil PatilSiemens Healthineers, Malvern, Pennsylvania, USA.
John A DerbyshireFunctional MRI Facility, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland, USA.
Ken SakaieDiagnostics Institute-Imaging, Cleveland Clinic, Cleveland, Ohio, USA.
Jonathan LeeDiagnostics Institute-Imaging, Cleveland Clinic, Cleveland, Ohio, USA.
Mark A ElliottDepartment of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Russell T ShinoharaDepartment of Biostatistics, Epidemiology, and Informatics, Penn Statistics in Imaging and Visualization Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
Matthew K SchindlerDepartment of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.ORCID https://orcid.org/0000-0003-0157-5669
Michel BilelloDepartment of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Marwa KaiseyDepartment of Neurology, Cedars-Sinai Medical Center, Los Angeles, California, USA.
Omar Al-LouziDepartment of Neurology, Cedars-Sinai Medical Center, Los Angeles, California, USA.
Nader BineshDepartment of Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA.
Marcel MayaDepartment of Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA.
Debiao LiBiomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.
Andrew J SolomonLarner College of Medicine, University of Vermont, Burlington, Vermont, USA.
Daniel S ReichTranslational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA.
Nancy L SicotteDepartment of Neurology, Cedars-Sinai Medical Center, Los Angeles, California, USA.
Mark J LoweDiagnostics Institute-Imaging, Cleveland Clinic, Cleveland, Ohio, USA.
Daniel OntanedaMellen Center, Department of Neurology, Neurological Institute, Cleveland Clinic, Cleveland, Ohio, USA.
Pascal SatiNeuroimaging Program, Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, California, USA.

Funding

Central Vein Sign: A Diagnostic Biomarker in Multiple SclerosisU01NS116776 · NINDS · CLEVELAND CLINIC LERNER COM-CWRU · PI ONTANEDA, DANIEL, SATI, PASCAL · 2020 to 2024
$7.1M
Harmonization of Multi-Site Neuroimaging Data from Complex Study DesignsR01MH123550 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI SHINOHARA, RUSSELL TAKESHI · 2020 to 2025
$4.0M
Advanced Statistical Analytics of MRI in MSR01NS112274 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI SHINOHARA, RUSSELL TAKESHI · 2020 to 2024
$2.8M
NIMH NIH HHS R01 MH123550NINDS NIH HHS R01MH123550NINDS NIH HHS R01 NS112274NINDS NIH HHS R01NS112274NINDS NIH HHS U01 NS116776NINDS NIH HHS U01-NS116776the Cedars-Sinai Women's Guild Neurology Projectthe Erwin Rautenberg Foundationthe National MS Society (NMSS) RG-2110-38526
6 · The paper itself

Abstract

purposeRecent updates to the diagnostic criteria of multiple sclerosis (MS) require whole-brain T

methodsFifty-two research participants were imaged at 3T using the 3D-EPI sequence acquired at different CAIPIRINHA acceleration factors (R = 2, 3, and 4) and denoised using a dedicated denoising convolutional neural network (DnCNN). Quantitative assessment of the accelerated T

resultsT

conclusionsThe 3D-EPI sequence combined with CAIPIRINHA and deep learning denoising enables fast submillimeter whole-brain T

Indexed as

BrainDeep LearningEcho-Planar ImagingImaging, Three-DimensionalMultiple SclerosisAdultAlgorithmsConvolutional Neural NetworksFemaleHumansImage EnhancementImage Interpretation, Computer-AssistedImage Processing, Computer-AssistedMaleMiddle AgedSignal-To-Noise Ratio3D‐EPICAIPIRINHA accelerationdeep learning denoisingsubmillimeter resolution

Identifiers

PMID42487251
PMCPMC13527241

What Socratic holds

Textmetadata
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 Socratic graph.