Evidence map›Paper›PMID 41646297›Full record

ArticleResearch square2026

Pharmacokinetic modeling of hepatospecific MRI contrast agent flux in large animal models by dynamic contrast-enhanced MRI.

Matthew T Latourette, Jeremy M-L Hix, Jie Huang, Christiane L Mallett, Kirk A Muñoz, Erik M Shapiro

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In one paragraph

Article in Research square, 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

5 · Who and what money

Authors and funding

6 authors.

Matthew T LatouretteDepartment of Radiology, Michigan State University, East Lansing, MI, USA.
Jeremy M-L HixDepartment of Radiology, Michigan State University, East Lansing, MI, USA.
Jie HuangDepartment of Radiology, Michigan State University, East Lansing, MI, USA.
Christiane L MallettDepartment of Radiology, Michigan State University, East Lansing, MI, USA.
Kirk A MuñozDepartment of Small Animal Clinical Sciences, Michigan State University, East Lansing, MI, USA.
Erik M ShapiroDepartment of Radiology, Michigan State University, East Lansing, MI, USA.ORCID 0000-0002-0901-5781

Funding

Quantitative molecular and cellular MRI of hepatocyte transplantationR01DK107697 · NIDDK · MICHIGAN STATE UNIVERSITY · PI SHAPIRO, ERIK · 2015 to 2018
$2.1M
NIDDK NIH HHS R01 DK107697
6 · The paper itself

Abstract

Purpose: Evaluate the feasibility of using dogs and a pig as translational models for assessing hepatic flux of hepatospecific gadolinium-based contrast agents using dynamic contrast-enhanced MRI (DCE-MRI). Procedures: DCE-MRI was performed with hepatospecific agents, gadoxetate disodium (Gd-EOB-DTPA) and gadobenate dimeglumine (Gd-BOPTA), and non-hepatospecific agents. Two validated pharmacokinetic models were applied: a single-input reference region model and a dual-input model using arterial and venous blood signals. Results: In dogs, hepatic enhancement rose rapidly and plateaued after Gd-EOB-DTPA administration, with minimal decline over an hour. In the pig, both hepatospecific agents showed a rapid rise, blunt peak, and gradual decline. The single-input model revealed significantly higher uptake rates for hepatospecific agents, confirming its sensitivity to hepatocyte uptake. The dual-input model effectively distinguished contrast agent dynamics in dogs and showed promise in the pig. Conclusions: In this feasibility study, canine Gd-EOB-DTPA pharmacokinetic values closely mirrored those observed in humans. These results establish baseline model parameters in a large-animal setting and support further, more comprehensive investigations into their translational potential for assessing hepatic function using DCE-MRI.

Indexed as

DCE-MRIhepatic functionlarge animal modelspharmacokineticstranslational imaging

Identifiers

PMID41646297
PMCPMC12869614

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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.