Evidence map›Paper›PMID 29517819›Full record

ArticleJournal of magnetic resonance imaging : JMRI2018

MR-assisted PET motion correction in simultaneous PET/MRI studies of dementia subjects.

Kevin T Chen, Stephanie Salcedo, Daniel B Chonde, David Izquierdo-Garcia, Michael A Levine, Julie C Price, Bradford C Dickerson, Ciprian Catana

Abstract read
In one paragraph

Article in Journal of magnetic resonance imaging : JMRI, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

24 citing papers in PubMed.

  1. Article
  2. Article
  3. Clinical validation of a deep learning model for low-count PET image enhancement.European journal of nuclear medicine and molecular imaging · 2025
    Article
  4. Effects of List-Mode-Based Intraframe Motion Correction in Dynamic Brain PET Imaging.IEEE transactions on radiation and plasma medical sciences · 2024
    Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Assessment of motion and model bias on the detection of dopamine response to behavioral challenge.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2022
    Article
  12. Rigid motion-resolvedMagnetic resonance in medicine · 2022
    Article
  13. Article
  14. Article
  15. Synergistic motion compensation strategies for positron emission tomography when acquired simultaneously with magnetic resonance imaging.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2021
    Review
  16. Article
  17. Dose Reduction in Brain [Molecular imaging and biology · 2020
    Article
  18. Advances in simultaneous PET/MR for imaging neuroreceptor function.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2020
    Review
  19. Article
  20. 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

8 authors.

Kevin T ChenAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA.
Stephanie SalcedoAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA.
Daniel B ChondeAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA.
David Izquierdo-GarciaAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA.
Michael A LevineAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA.
Julie C PriceAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA.
Bradford C DickersonAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA.
Ciprian CatanaAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA.

Funding

Imaging tau, amyloid, and neurodegeneration in PPAR01DC014296 · NIDCD · MASSACHUSETTS GENERAL HOSPITAL · PI DICKERSON, BRADFORD C, JOHNSON, KEITH A. · 2016 to 2020
$4.3M
MR-assisted PET data optimization for neuroimaging studiesR01EB014894 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI CATANA, CIPRIAN · 2013 to 2016
$2.6M
Neuromodulation of memory and language networks in Alzheimer's diseaseR21AG056958 · NIA · MASSACHUSETTS GENERAL HOSPITAL · PI CAMPRODON, JOAN A, DICKERSON, BRADFORD C · 2017 to 2018
$467k
NIA NIH HHS R21 AG056958NIBIB NIH HHS R01 EB014894NIDCD NIH HHS R01 DC014296
6 · The paper itself

Abstract

backgroundSubject motion in positron emission tomography (PET) studies leads to image blurring and artifacts; simultaneously acquired magnetic resonance imaging (MRI) data provides a means for motion correction (MC) in integrated PET/MRI scanners. PURPOSE: To assess the effect of realistic head motion and MR-based MC on static [ STUDY TYPE: Observational study. POPULATION: Thirty dementia subjects were recruited. FIELD STRENGTH/SEQUENCE: 3T hybrid PET/MR scanner where EPI-based and T ASSESSMENT: Head motion parameters estimated from high temporal resolution MR volumes were used for PET MC. The MR-based MC method was compared to PET frame-based MC methods in which motion parameters were estimated by coregistering 5-minute frames before and after accounting for the attenuation-emission mismatch. The relative changes in standardized uptake value ratios (SUVRs) between the PET volumes processed with the various MC methods, without MC, and the PET volumes with simulated motion were compared in relevant brain regions. STATISTICAL TESTS: The absolute value of the regional SUVR relative change was assessed with pairwise paired t-tests testing at the P = 0.05 level, comparing the values obtained through different MR-based MC processing methods as well as across different motion groups. The intraregion voxelwise variability of regional SUVRs obtained through different MR-based MC processing methods was also assessed with pairwise paired t-tests testing at the P = 0.05 level.

resultsMC had a greater impact on PET data quantification in subjects with larger amplitude motion (higher than 18% in the medial orbitofrontal cortex) and greater changes were generally observed for the MR-based MC method compared to the frame-based methods. Furthermore, a mean relative change of ∼4% was observed after MC even at the group level, suggesting the importance of routinely applying this correction. The intraregion voxelwise variability of regional SUVRs was also decreased using MR-based MC. All comparisons were significant at the P = 0.05 level. DATA

conclusionIncorporating temporally correlated MR data to account for intraframe motion has a positive impact on the FDG PET image quality and data quantification in dementia patients. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:1288-1296.

Indexed as

Magnetic Resonance ImagingMultimodal ImagingPositron-Emission TomographyAgedAged, 80 and overAlgorithmsArtifactsBrainDementiaFemaleFluorodeoxyglucose F18Head MovementsHumansImage Processing, Computer-AssistedMaleNormal DistributionFluorodeoxyglucose F18dementiamotion correctionPET/MRIsimultaneous imaging

Identifiers

PMID29517819
PMCPMC6129224

What OpenQuestion holds

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Registered trials

None linked

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.