Evidence map›Paper›PMID 37345703›Full record

ArticleMagnetic resonance in medicine2023

Improving motion robustness of 3D MR fingerprinting with a fat navigator.

Siyuan Hu, Yong Chen, Xiaopeng Zong, Weili Lin, Mark Griswold, Dan Ma

Abstract 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 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. "MR Fingerprinting for Imaging Brain Hemodynamics and Oxygenation".Journal of magnetic resonance imaging : JMRI · 2025
    Review
  6. Article
  7. Time-resolved MR fingerprinting for TMagnetic resonance in medicine · 2025
    Article
  8. Article
  9. Article
  10. Magnetic Resonance Fingerprinting of the Pediatric Brain.Magnetic resonance imaging clinics of North America · 2021
    Review
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

6 authors.

Siyuan HuDepartment of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.ORCID 0000-0003-3137-0605
Yong ChenDepartment of Radiology, Case Western Reserve University, Cleveland, Ohio, USA.
Xiaopeng ZongSchool of Biomedical Engineering, ShanghaiTech University, Shanghai, China.
Weili LinDepartment of Radiology, University of North Carolina, Chapel Hill, North Carolina, USA.
Mark GriswoldDepartment of Radiology, Case Western Reserve University, Cleveland, Ohio, USA.
Dan MaDepartment of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.

Funding

MR Fingerprinting for EpilepsyR01NS109439 · NINDS · CLEVELAND CLINIC LERNER COM-CWRU · PI Dan Ma, Zhong Irene Wang · 2019 to 2026
$4.2M
MR Fingerprinting based Quantitative Imaging and Analysis Platform (MRF-QIA) for brain tumors.R01CA269604 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI BADVE, CHAITRA, DAVATZIKOS, CHRISTOS · 2023 to 2025
$1.7M
Comprehensive MR Fingerprinting for Infants and Young Children at Risk for Developmental Delays.R01HD112923 · NICHD · DUKE UNIVERSITY · PI Dan Ma, DEANNE E WILSON-COSTELLO · 2024 to 2026
$1.7M
Optimization of Magnetic Resonance Fingerprintingusing Quantum Inspired AlgorithmsR21EB029658 · NIBIB · CASE WESTERN RESERVE UNIVERSITY · PI MCGIVNEY, DEBRA · 2021 to 2023
$644k
A Framework to Design 3D Quantitative Magnetic Resonance Fingerprinting (MRF) Scans and Reduce Patient AnxietyR21EB026764 · NIBIB · CASE WESTERN RESERVE UNIVERSITY · PI MA, DAN · 2018 to 2020
$641k
Medical Research Council MR/W031566/1NCI NIH HHS R01 CA269604NIBIB NIH HHS EB026764NIBIB NIH HHS R21 EB026764NIBIB NIH HHS R21 EB029658NICHD NIH HHS R01 HD112923NINDS NIH HHS R01 NS109439
6 · The paper itself

Abstract

purposeTo develop a 3D MR fingerprinting (MRF) method in combination with fat navigators to improve its motion robustness for neuroimaging.

methodsA rapid fat navigator was developed using the stack-of-spirals acquisition and non-Cartesian spiral GRAPPA. The fat navigator module was implemented in the 3D MRF sequence with high scan efficiency. The developed method was first validated in phantoms and five healthy subjects with intentional head motion. The method was further applied to infants with neonatal opioid withdrawal symptoms. The 3D MRF scans with fat navigators acquired with and without acceleration along the partition-encoding direction were both examined in the study.

resultsBoth phantom and in vivo results demonstrated that the added fat navigator modules did not influence the quantification accuracy in MRF. In combination with non-Cartesian spiral GRAPPA, a rapid fat navigator sampling with whole-brain coverage was achieved in ˜0.5 s at 3T, reducing its sensitivity to potential motion. Based on the motion waveforms extracted from fat navigators, the motion robustness of the 3D MRF was largely improved. With the proposed method, the motion-corrupted MRF datasets yielded T1 and T2 maps with significantly reduced artifacts and high correlations with measurements from the reference motion-free MRF scans.

conclusionWe developed a 3D MRF method coupled with rapid fat navigators to improve its motion robustness for quantitative neuroimaging. Our results demonstrate that (1) accurate tissue quantification was preserved with the fat navigator modules and (2) the motion robustness for quantitative tissue mapping was largely improved with the developed method.

Indexed as

AlgorithmsImage Processing, Computer-AssistedBrainHumansInfant, NewbornMagnetic Resonance ImagingMotionPhantoms, Imagingfat navigatormagnetic resonance fingerprintingmotion correctionrelaxometryspiral GRAPPA

Identifiers

PMID37345703
PMCPMC10524525

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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.