Evidence map›Paper›PMID 36404637›Full record

ArticleMagnetic resonance in medicine2023

Motion-robust, blood-suppressed, reduced-distortion diffusion MRI of the liver.

Ruiqi Geng, Yuxin Zhang, James Rice, Matthias R Muehler, Jitka Starekova, David R Rutkowski, Nataliya V Uboha, Ali Pirasteh, Alejandro Roldán-Alzate, Arnaud Guidon and 1 more

Open access · hybridAbstract read
In one paragraph

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 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
2.0field-weighted citation impact, top 14% of its field
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

15 citing papers in PubMed, 15 citations in OpenAlex.

  1. Diffusion MRI of the Body: Beyond PGSE-EPI.Journal of magnetic resonance imaging : JMRI · 2026
    Review
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  4. Usefulness of Adaptive Complex Signal Average Diffusion-weighted MR Imaging of the Abdomen.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026
    Article
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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

11 authors at 2 institutions in 2 countries.

Ruiqi GengDepartment of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA.ORCID 0000-0002-5790-5711
Yuxin ZhangDepartment of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA.ORCID 0000-0001-9852-7959
James RiceDepartment of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA.ORCID 0000-0002-2029-6705
Matthias R MuehlerDepartment of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA.ORCID 0000-0003-4204-2084
Jitka StarekovaDepartment of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA.ORCID 0000-0002-4285-3090
David R RutkowskiDepartment of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA.ORCID 0000-0002-1168-5004
Nataliya V UbohaDivision of Hematology and Oncology, Department of Medicine, University of Wisconsin-Madison, Madison, Wisconsin, USA.ORCID 0000-0003-3449-1680
Ali PirastehDepartment of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA.ORCID 0000-0003-0797-4636
Alejandro Roldán-AlzateDepartment of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA.ORCID 0000-0003-4149-4038
Arnaud GuidonGlobal MR Application and Workflow, GE Healthcare, Boston, Massachusetts, USA.ORCID 0000-0001-6777-1633
Diego HernandoDepartment of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA.ORCID 0000-0002-0016-0317
University of Wisconsin–Madison · USGeneral Electric (Spain) · ES

Funding

Training Program in Translational Cardiovascular Science (TPTCS)T32HL007936 · NHLBI · UNIVERSITY OF WISCONSIN-MADISON · PI Lee Lochbaum Eckhardt, Gail A Robertson · 2001 to 2026
$11.6M
Diffusion MRI of the AbdomenR01EB030497 · NIBIB · UNIVERSITY OF WISCONSIN-MADISON · PI HERNANDO, DIEGO · 2021 to 2021
$1.6M
NHLBI NIH HHS T32 HL007936NIBIB NIH HHS R01 EB030497
6 · The paper itself

Abstract

purposeTo evaluate feasibility and reproducibility of liver diffusion-weighted (DW) MRI using cardiac-motion-robust, blood-suppressed, reduced-distortion techniques.

methodsDW-MRI data were acquired at 3T in an anatomically accurate liver phantom including controlled pulsatile motion, in eight healthy volunteers and four patients with known or suspected liver metastases. Standard monopolar and motion-robust (M1-nulled, and M1-optimized) DW gradient waveforms were each acquired with single-shot echo-planar imaging (ssEPI) and multishot EPI (msEPI). In the motion phantom, apparent diffusion coefficient (ADC) was measured in the motion-affected volume. In healthy volunteers, ADC was measured in the left and right liver lobes separately to evaluate ADC reproducibility between the two lobes. Image distortions were quantified using the normalized cross-correlation coefficient, with an undistorted T2-weighted reference.

resultsIn the motion phantom, ADC mean and SD in motion-affected volumes substantially increased with increasing motion for monopolar waveforms. ADC remained stable in the presence of increasing motion when using motion-robust waveforms. M1-optimized waveforms suppressed slow flow signal present with M1-nulled waveforms. In healthy volunteers, monopolar waveforms generated significantly different ADC measurements between left and right liver lobes (

conclusionSynergistic effects of combined M1-optimized diffusion waveforms and msEPI acquisitions enable reproducible liver DWI with motion robustness, blood signal suppression, and reduced distortion.

Indexed as

Diffusion Magnetic Resonance ImagingLiver NeoplasmsEcho-Planar ImagingHumansMotionReproducibility of Resultsdiffusionlivermotion-robustmultishotphantom

Identifiers

PMID36404637
PMCPMC9792444
OpenAlexW4309511763

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.