Article in Magnetic resonance in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
0numbers the graph read from it
0cells of the map it votes in
5citing 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.
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.
Haoyang PeiBioMedical Engineering and Imaging Institute (BMEII) and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Ding XiaBioMedical Engineering and Imaging Institute (BMEII) and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Xiang XuBioMedical Engineering and Imaging Institute (BMEII) and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Yang YangBioMedical Engineering and Imaging Institute (BMEII) and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.ORCID 0000-0002-2841-4243
Yao WangDepartment of Electrical and Computer Engineering, NYU Tandon School of Engineering, New York, New York, USA.
Fang LiuAthinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.ORCID 0000-0001-8032-6681
Li FengBioMedical Engineering and Imaging Institute (BMEII) and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.ORCID 0000-0002-8692-7645
Funding
TR&D 4: Revealing Microstructure: Biophysical modeling and validation for discovery and clinical careP41EB017183 · NIBIB · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI Christopher M Collins · 2014 to 2026
$19.3M
Deep Learning Technology for Rapid Morphological and Quantitative Imaging of Knee PathologyR01AR079442 · NIAMS · MASSACHUSETTS GENERAL HOSPITAL · PI Fang Liu · 2022 to 2026
$2.2M
Rapid Three-dimensional Simultaneous Knee Multi-Relaxation MappingR01AR081344 · NIAMS · MASSACHUSETTS GENERAL HOSPITAL · PI Fang Liu · 2022 to 2026
$2.0M
Rapid Motion-Robust and Easy-to-Use Dynamic Contrast-Enhanced MRI for Liver Perfusion QuantificationR01EB030549 · NIBIB · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI FENG, LI · 2021 to 2024
$1.7M
Rapid Structure-Function MRI of the Lung for Post-COVID-19 ManagementR01EB031083 · NIBIB · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI FENG, LI · 2021 to 2021
$1.5M
Development and Translation of D-glucose as an MRI Contrast Agent for Multiple SclerosisR00EB026312 · NIBIB · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI XU, XIANG · 2020 to 2022
$747k
Deep Learning Reconstruction for Rapid Multi-Component RelaxometryR21EB031185 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI LIU, FANG · 2022 to 2024
$690k
3D Free-Breathing Fat and Iron Corrected T1 MappingR21EB032917 · NIBIB · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI FENG, LI · 2022 to 2023
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.