Evidence map›Paper›PMID 39279845›Full record

ArticleArXiv2024

Ground-truth effects in learning-based fiber orientation distribution estimation in neonatal brains.

Rizhong Lin, Hamza Kebiri, Ali Gholipour, Yufei Chen, Jean-Philippe Thiran, Davood Karimi, Meritxell Bach Cuadra

Abstract readPreprint
In one paragraph

Article in ArXiv, 2024. 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

7 authors.

Rizhong LinSignal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.ORCID 0009-0000-1468-6734
Hamza KebiriCIBM Center for Biomedical Imaging, Switzerland.ORCID 0000-0001-7592-3166
Ali GholipourComputational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA.ORCID 0000-0001-7699-4564
Yufei ChenCollege of Electronic and Information Engineering, Tongji University, Shanghai, China.ORCID 0000-0002-3645-9046
Jean-Philippe ThiranSignal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.ORCID 0000-0003-2938-9657
Davood KarimiComputational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA.ORCID 0000-0002-5155-2644
Meritxell Bach CuadraCIBM Center for Biomedical Imaging, Switzerland.ORCID 0000-0003-2730-4285

Funding

Enhanced Imaging of the Fetal Brain MicrostructureR01EB032366 · NIBIB · UNIVERSITY OF CALIFORNIA-IRVINE · PI GHOLIPOUR-BABOLI, ALI · 2022 to 2025
$2.0M
Enabling the Assessment of Fetal Brain Development and Degeneration with Machine LearningR01NS128281 · NINDS · BOSTON CHILDREN'S HOSPITAL · PI Davood Karimi · 2023 to 2026
$1.8M
Advancing Microstructural and Vascular Neuroimaging in Perinatal StrokeR01NS106030 · NINDS · BOSTON CHILDREN'S HOSPITAL · PI WARFIELD, SIMON K · 2019 to 2023
$1.7M
Accurate, reliable, and interpretable machine learning for assessment of neonatal and pediatric brain micro-structureR01HD110772 · NICHD · BOSTON CHILDREN'S HOSPITAL · PI Davood Karimi · 2023 to 2026
$1.5M
NIBIB NIH HHS R01 EB032366NICHD NIH HHS R01 HD110772NINDS NIH HHS R01 NS106030NINDS NIH HHS R01 NS128281
6 · The paper itself

Abstract

Diffusion Magnetic Resonance Imaging (dMRI) is a noninvasive method for depicting brain microstructure

Indexed as

Age domain shiftDeep learningFOD estimationMSMT-CSDNeonatal brainSS3T-CSD

Identifiers

PMID39279845
PMCPMC11398543

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.