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.
Wonil LeeDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, Connecticut, USA.ORCID 0000-0002-2144-0211
Paul Kyu HanDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, Connecticut, USA.ORCID 0000-0003-1650-1347
Thibault MarinDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, Connecticut, USA.
Ismaël B G MounimeDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, Connecticut, USA.ORCID 0000-0002-8852-5359
Samira Vafay EslahiGordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.
Yanis DjebraDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, Connecticut, USA.
Didi ChiDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, Connecticut, USA.ORCID 0000-0001-7028-1280
Felicitas J BijariDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, Connecticut, USA.
Marc D NormandinDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, Connecticut, USA.
Georges El FakhriDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, Connecticut, USA.
Chao MaDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, Connecticut, USA.ORCID 0000-0002-2016-8084
Funding
Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Unified Joint Statistical Reconstruction of PET & MRP41EB022544 · NIBIB · YALE UNIVERSITY · PI Georges El Fakhri · 2017 to 2026
$14.8M
Quantitative Oncologic PET-MRR01CA165221 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI EL FAKHRI, GEORGES · 2012 to 2023
$4.7M
Postgraduate Training Program in Medical Imaging (PTPMI)T32EB013180 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI Ciprian Catana, Pedram Heidari · 2011 to 2026
$3.7M
PET/MR Mapping of Myocardial Membrane PotentialR01HL137230 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI EL FAKHRI, GEORGES · 2017 to 2020
$3.3M
Improving Image-Guided Radiation Therapy of Gliomas with High-Resolution MR Spectroscopic ImagingR01EB033582 · NIBIB · YALE UNIVERSITY · PI Chao Ma · 2022 to 2026
$2.0M
Improved drug efficacy assessment using joint Bayesian estimation frameworkR01EB035093 · NIBIB · YALE UNIVERSITY · PI Thibault Marin · 2024 to 2026
$1.2M
Permeability Imaging using Simultaneous Dynamic PET and Arterial Spin Labeling MRIK01EB030045 · NIBIB · YALE UNIVERSITY · PI HAN, PAUL KYU · 2020 to 2023
$497k
National Institutes of Health (NIH) K01EB030045National Institutes of Health (NIH) P41EB022544National Institutes of Health (NIH) R01CA165221National Institutes of Health (NIH) R01EB033582National Institutes of Health (NIH) R01EB035093National Institutes of Health (NIH) R01HL137230National Institutes of Health (NIH) T32EB013180NCATS NIH HHS UL1 TR001863NCI NIH HHS R01 CA165221NHLBI NIH HHS R01 HL137230NIBIB NIH HHS K01 EB030045NIBIB NIH HHS P41 EB022544NIBIB NIH HHS R01 EB033582NIBIB NIH HHS R01 EB035093NIBIB NIH HHS T32 EB013180
6 · The paper itself
Abstract
purposeTo develop a new method for free-breathing 3D extracellular volume (ECV) mapping of the whole heart at 3 T.
methodsA free-breathing 3D cardiac ECV mapping method was developed at 3 T. T
resultsThe feasibility of the proposed method was demonstrated using in vivo studies with six healthy volunteers at 3 T. We obtained 3D ECV maps at a spatial resolution of 1.9 × 1.9 × 4.5 mm
conclusionThe proposed method allows for free-breathing 3D ECV mapping of the whole heart within a practically feasible imaging time. The estimated ECV values from the proposed method were comparable to those from the existing method.
Indexed as
AlgorithmsImaging, Three-DimensionalAdultContrast MediaFemaleHeartHumansImage EnhancementImage Interpretation, Computer-AssistedLinear ModelsMagnetic Resonance ImagingMaleReproducibility of ResultsRespirationSensitivity and SpecificityContrast Mediacardiac extracellular volume (ECV) mappingcardiac T1 mappinglinear tangent space alignment (LTSA)manifold learning
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.
Free-breathing 3D cardiac extracellular volume (ECV) mapping using a linear tangent space alignment (LTSA) model. · full record | Socratic