Evidence map›Paper›PMID 41242402›Full record

ArticleMagnetic resonance imaging2026

Repeatability of in vivo MR cytometry for measuring cell size and density in healthy human livers.

Xiaoyu Jiang, Chaoyang Jin, Hakmook Kang, Gracie Piantek, Zhongliang Zu, John C Gore, Junzhong Xu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Magnetic resonance imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Xiaoyu JiangVanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA. Electronic address: xiaoyu.jiang@vanderbilt.edu.
Chaoyang JinVanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA.
Hakmook KangDepartment of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37232, USA.
Gracie PiantekVanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA.
Zhongliang ZuVanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA.
John C GoreVanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, USA; Department of Physics and Astronomy, Vanderbilt University, Nashville, TN 37232, USA.
Junzhong XuVanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, USA; Department of Physics and Astronomy, Vanderbilt University, Nashville, TN 37232, USA.

Funding

MRI Diffusion in Tumors Using Oscillating GradientsR01CA109106 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI XU, JUNZHONG · 2006 to 2022
$4.0M
MRI of tumor-infiltrating lymphocytes using MRI-cytometryR01CA269620 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI XU, JUNZHONG · 2022 to 2025
$2.3M
in vivo MR characterization of pathological changes in liver microstructuresR01DK135950 · NIDDK · VANDERBILT UNIVERSITY MEDICAL CENTER · PI Xiaoyu Jiang · 2024 to 2026
$1.2M
Differentiation of tumor progression from radiation necrosis using MR cell size imagingR21CA270731 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI XU, JUNZHONG · 2023 to 2024
$440k
NCI NIH HHS R01 CA109106NCI NIH HHS R01 CA269620NCI NIH HHS R21 CA270731NIDDK NIH HHS R01 DK135950
6 · The paper itself

Abstract

purposeTo evaluate the repeatability of MRI-derived cell size and density measurements in the healthy human liver using MR cytometry, a non-invasive technique for quantifying tissue microstructure,

methodsEight healthy subjects underwent two identical MR cytometry scans (2-7 days apart) on a Phillips 3 T scanner. Diffusion data were acquired using pulsed gradient spin echo (PGSE) and oscillating gradient spin echo (OGSE) sequences at multiple diffusion times. Bayes factor model selection compared different MR cytometry signal models, such as IMPULSED, which ignores water exchange between intra and extracellular compartments, and JOINT, which accounts for it. Repeatability of MRI-derived parameters were assessed via whole-volume and multi-size ROI analyses using Bland-Altman plots and correlation coefficients.

resultsThe JOINT model was decisively preferred (>90 % of voxels). High repeatability was observed for d, v

conclusionROI-based MR cytometry provides repeatable measurements of mean cell size and density, supporting its potential for clinical applications such as assessing steatohepatitis and detecting hepatocellular carcinoma. Protocol optimization to improve SNR is needed for reliable quantification of water exchange.

Indexed as

LiverMagnetic Resonance ImagingAdultCell CountCell SizeFemaleHealthy VolunteersHumansMaleMiddle AgedReproducibility of ResultsCell densityCell sizeLiver microstructureMR cytometryOGSERepeatability

Identifiers

PMID41242402
PMCPMC12784474

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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.