Evidence map›Paper›PMID 38703246›Full record

ArticleMagma (New York, N.Y.)2024

Spatial profiling of in vivo diffusion-weighted MRI parameters in the healthy human kidney.

Nima Gilani, Artem Mikheev, Inge M Brinkmann, Malika Kumbella, James S Babb, Dibash Basukala, Andreas Wetscherek, Thomas Benkert, Hersh Chandarana, Eric E Sigmund

Abstract read
In one paragraph

Article in Magma (New York, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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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.

Nima GilaniDepartment of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R), Center for Biomedical Imaging, NYU Langone Health, New York, USA. nima.gilani@nyulangone.org.ORCID http://orcid.org/0000-0002-6129-7763
Artem MikheevDepartment of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R), Center for Biomedical Imaging, NYU Langone Health, New York, USA.
Inge M BrinkmannSiemens Healthcare GmbH, Erlangen, Germany.
Malika KumbellaDepartment of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R), Center for Biomedical Imaging, NYU Langone Health, New York, USA.
James S BabbDepartment of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R), Center for Biomedical Imaging, NYU Langone Health, New York, USA.
Dibash BasukalaDepartment of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R), Center for Biomedical Imaging, NYU Langone Health, New York, USA.
Andreas WetscherekJoint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK.
Thomas BenkertMR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany.
Hersh ChandaranaDepartment of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R), Center for Biomedical Imaging, NYU Langone Health, New York, USA.
Eric E SigmundDepartment of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R), Center for Biomedical Imaging, NYU Langone Health, New York, USA.

Funding

TR&D 4: Revealing Microstructure: Biophysical modeling and validation for discovery and clinical careP41EB017183 · NIBIB · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI Daniel K Sodickson · 2014 to 2026
$19.3M
Advanced Diffusion Imaging for Management of Renal Cancer: Oncologic Control and Renal Functional Reserve - Resubmission - 1R01CA245671 · NCI · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI CHANDARANA, HERSH, SIGMUND, ERIC EDWARD · 2020 to 2025
$3.0M
National Institutes of Health (NIH) R01CA245671NCI NIH HHS R01 CA245671NIBIB NIH HHS P41 EB017183
6 · The paper itself

Abstract

objectiveDiffusion-weighted MRI is a technique that can infer microstructural and microcirculatory features from biological tissue, with particular application to renal tissue. There is extensive literature on diffusion tensor imaging (DTI) of anisotropy in the renal medulla, intravoxel incoherent motion (IVIM) measurements separating microstructural from microcirculation effects, and combinations of the two. However, interpretation of these features and adaptation of more specific models remains an ongoing challenge. One input to this process is a whole organ distillation of corticomedullary contrast of diffusion metrics, as has been explored for other renal biomarkers. MATERIALS AND

methodsIn this work, we probe the spatial dependence of diffusion MRI metrics with concentrically layered segmentation in 11 healthy kidneys at 3 T. The metrics include those from DTI, IVIM, a combined approach titled "REnal Flow and Microstructure AnisotroPy (REFMAP)", and a multiply encoded model titled "FC-IVIM" providing estimates of fluid velocity and branching length.

resultsFractional anisotropy decreased from the inner kidney to the outer kidney with the strongest layer correlation in both parenchyma (including cortex and medulla) and medulla with Spearman correlation coefficients and p-values (r, p) of (0.42, <0.001) and (0.37, <0.001), respectively. Also, dynamic parameters derived from the three models significantly decreased with a high correlation from the inner to the outer parenchyma or medulla with (r, p) ranges of (0.46-0.55, <0.001).

conclusionsThese spatial trends might find implications for indirect assessments of kidney physiology and microstructure using diffusion MRI.

Indexed as

Diffusion Magnetic Resonance ImagingKidneyAdultAlgorithmsAnisotropyDiffusion Tensor ImagingFemaleHealthy VolunteersHumansImage Processing, Computer-AssistedKidney MedullaMaleMicrocirculationMiddle AgedMotionYoung AdultDTIIVIMMicrostructureMicrovasculatureMRIRenal imaging

Identifiers

PMID38703246
PMCPMC11963357

What OpenQuestion holds

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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.