Evidence map›Paper›PMID 40808286›Full record

ArticleMagnetic resonance in medicine2025

Automatic detection of arterial input function for brain DCE-MRI in multi-site cohorts.

Lucas Saca, Raghav Gaggar, Ioannis Pappas, Tammie Benzinger, Eric M Reiman, Mark S Shiroishi, Elizabeth B Joe, John M Ringman, Hussein N Yassine, Lon S Schneider and 6 more

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
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
  2. Review
  3. Article
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

16 authors.

Lucas SacaDepartment of Radiology, Loma Linda University, Loma Linda, California, USA.ORCID https://orcid.org/0009-0008-3291-0750
Raghav GaggarDepartment of Physiology and Neuroscience, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
Ioannis PappasLaboratory of NeuroImaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
Tammie BenzingerMallinckrodt Institute of Radiology, Washington University in St. Louis School of Medicine, St. Louis, Missouri, USA.
Eric M ReimanBanner Alzheimer Institute, Phoenix, Arizona, USA.
Mark S ShiroishiDepartment of Radiology, University of Southern California, Los Angeles, California, USA.
Elizabeth B JoeAlzheimer's Disease Research Center, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
John M RingmanAlzheimer's Disease Research Center, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
Hussein N YassineDepartment of Physiology and Neuroscience, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
Lon S SchneiderAlzheimer's Disease Research Center, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
Helena C ChuiAlzheimer's Disease Research Center, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
Daniel A NationDepartment of Physiology and Neuroscience, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
Berislav V ZlokovicDepartment of Physiology and Neuroscience, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
Arthur W TogaLaboratory of NeuroImaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
Ararat ChakhoyanDepartment of Physiology and Neuroscience, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
Samuel BarnesDepartment of Radiology, Loma Linda University, Loma Linda, California, USA.ORCID https://orcid.org/0000-0002-1065-8442

Funding

Smartphone-Based "Burst" Cognitive AssessmentsP01AG003991 · NIA · WASHINGTON UNIVERSITY · PI JOHN MORRIS · 1985 to 2026
$69.5M
Vascular Contributions to Dementia and Genetic Risk Factors for Alzheimer's DiseaseP01AG052350 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Daniel A Nation, ARTHUR W TOGA · 2016 to 2026
$28.4M
USCADRC Diversity Supplement PachicanoP30AG066530 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI HELENA Chang CHUI · 2020 to 2026
$27.8M
Locus Coeruleus Imaging Markers in Preclinical Alzheimers disease, Cerebrovascular Disease and Cognitive DeclineR01AG082073 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI BONDI, MARK W, MATHER, MARA · 2023 to 2025
$4.9M
Endothelial progenitor cells and neurovascular injury in the aging brainR01AG064228 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Daniel A Nation · 2019 to 2026
$4.5M
Cerebrovascular resistance in cognitive aging and Alzheimer's disease riskR01AG060049 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI NATION, DANIEL A · 2020 to 2023
$2.4M
Loma Linda University GRASPNIA NIH HHS P01 AG003991NIA NIH HHS P01 AG052350NIA NIH HHS P30 AG066530NIA NIH HHS R01 AG060049NIA NIH HHS R01 AG064228NIA NIH HHS R01 AG082073NIH HHS P01AG052350NIH HHS P30AG066530NIH HHS R01AG060049NIH HHS R01AG064228NIH HHS R01AG082073
6 · The paper itself

Abstract

purposeArterial input function (AIF) extraction is a crucial step in quantitative pharmacokinetic modeling of DCE-MRI. This work proposes a robust deep learning model that can precisely extract an AIF from DCE-MRI images.

methodsA diverse dataset of human brain DCE-MRI images from 289 participants, totaling 384 scans, from five different institutions with extracted gadolinium-based contrast agent curves from large penetrating arteries, and with most data collected for blood-brain barrier (BBB) permeability measurement, was retrospectively analyzed. A 3D UNet model was implemented and trained on manually drawn AIF regions. The testing cohort was compared using proposed AIF quality metric AIFitness and K

resultsThe resulting 3D UNet model achieved an average AIFitness score of 93.9 compared to 99.7 for manually selected AIFs, and white matter K

conclusionFindings suggest a 3D UNet model with additional convolutional neural network kernels and a modified Huber loss function achieves superior performance for identifying AIF curves from DCE-MRI in a diverse multi-center cohort. AIFitness scores and DCE-MRI-derived metrics, such as K

Indexed as

ArteriesBrainContrast MediaImage Processing, Computer-AssistedMagnetic Resonance ImagingAdultAgedAlgorithmsBlood-Brain BarrierCohort StudiesDeep LearningDynamic Contrast Enhanced Magnetic Resonance ImagingFemaleGadoliniumHumansImage Interpretation, Computer-AssistedContrast MediaGadolinium3D UNetAIF detectionartificial intelligenceblood–brain barrierDCE‐MRImulti‐site data

Identifiers

PMID40808286
PMCPMC12501662

What OpenQuestion holds

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LicenceCC BY-NC
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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.