Evidence map›Paper›PMID 40626426›Full record

ArticleMagnetic resonance in medicine2025

Fast MR signal simulations of microvascular and diffusion contributions using histogram-based approximation and recurrent neural networks.

Thomas Coudert, Maitê Silva Martins Marçal, Aurélien Delphin, Antoine Barrier, Lila Cunge, Loïc Legris, Jan M Warnking, Benjamin Lemasson, Emmanuel L Barbier, Thomas Christen

Abstract read
In one paragraph

Article in Magnetic resonance in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
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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

10 authors.

Thomas CoudertUniversité Grenoble Alpes, INSERM, U1216, Grenoble Institute Neurosciences, GIN, Grenoble, France.ORCID https://orcid.org/0009-0006-0158-6105
Maitê Silva Martins MarçalUniversité Grenoble Alpes, INSERM, U1216, Grenoble Institute Neurosciences, GIN, Grenoble, France.
Aurélien DelphinUniversité Grenoble Alpes, INSERM, US17, CNRS, UAR3552, CHU Grenoble Alpes, IRMaGe, Grenoble, France.
Antoine BarrierUniversité Grenoble Alpes, INSERM, U1216, Grenoble Institute Neurosciences, GIN, Grenoble, France.
Lila CungeUniversité Grenoble Alpes, INSERM, U1216, Grenoble Institute Neurosciences, GIN, Grenoble, France.
Loïc LegrisUniversité Grenoble Alpes, INSERM, U1216, Grenoble Institute Neurosciences, GIN, Grenoble, France.
Jan M WarnkingUniversité Grenoble Alpes, INSERM, U1216, Grenoble Institute Neurosciences, GIN, Grenoble, France.
Benjamin LemassonUniversité Grenoble Alpes, INSERM, U1216, Grenoble Institute Neurosciences, GIN, Grenoble, France.
Emmanuel L BarbierUniversité Grenoble Alpes, INSERM, U1216, Grenoble Institute Neurosciences, GIN, Grenoble, France.
Thomas ChristenUniversité Grenoble Alpes, INSERM, U1216, Grenoble Institute Neurosciences, GIN, Grenoble, France.

Funding

Agence Nationale de la Recherche ANR-11-INBS-0006Agence Nationale de la Recherche ANR-20-CE19-0030 MRFUSE
6 · The paper itself

Abstract

purposeAccurate MR signal simulation, including microvascular structures and water diffusion, is crucial for MRI techniques like fMRI BOLD modeling and MR vascular Fingerprinting (MRF), which use susceptibility effects on MR signals for tissue characterization. However, integrating microvascular features and diffusion remains computationally challenging, limiting the accuracy of the estimates. Using advanced modeling and deep neural networks, we propose a novel simulation tool that efficiently accounts for susceptibility and diffusion effects.

methodsWe used dimension reduction of magnetic field inhomogeneity matrices combined with deep learning methodology to accelerate the simulations while maintaining their accuracy. We validated our results through an in silico study against a reference method and in vivo MRF experiments.

resultsThis approach accelerates MR signal generation by a factor of almost 13 000 compared to previously used simulation methods while preserving accuracy.

conclusionThe MR-WAVES method allows fast generation of MR signals accounting for microvascular structures and water-diffusion contribution.

Indexed as

BrainImage Processing, Computer-AssistedMagnetic Resonance ImagingMicrovesselsNeural Networks, ComputerAlgorithmsComputer SimulationDeep LearningDiffusionDiffusion Magnetic Resonance ImagingHumansRecurrent Neural NetworksBOLDcerebral blood volume (CBV)magnetic resonance fingerprinting (MRF)numerical simulationsRNNvascularization

Identifiers

PMID40626426
PMCPMC12393200

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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.