Evidence map›Paper›PMID 28576170›Full record

ReviewPET clinics2017

Magnetic Resonance-based Motion Correction for Quantitative PET in Simultaneous PET-MR Imaging.

Yothin Rakvongthai, Georges El Fakhri

Abstract readReview
In one paragraph

Review in PET clinics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Quantitative PET in the 2020s: a roadmap.Physics in medicine and biology · 2021
    Article
  5. Article
  6. Article
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

2 authors.

Yothin RakvongthaiDivision of Nuclear Medicine, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand.
Georges El FakhriGordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA. Electronic address: elfakhri@pet.mgh.harvard.edu.

Funding

Unified Joint Statistical Reconstruction of PET & MRP41EB022544 · NIBIB · YALE UNIVERSITY · PI Kira Grogg · 2017 to 2026
$14.8M
Quantitative Oncologic PET-MRR01CA165221 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI EL FAKHRI, GEORGES · 2012 to 2023
$4.7M
Ultra High Resolution Brain PET Scanner for in-vivo Autoradiography ImagingU01EB027003 · NIBIB · YALE UNIVERSITY · PI EL FAKHRI, GEORGES, FONTAINE, RÉJEAN · 2018 to 2022
$4.6M
PET/MR Mapping of Myocardial Membrane PotentialR01HL137230 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI EL FAKHRI, GEORGES · 2017 to 2020
$3.3M
NCI NIH HHS R01 CA165221NHLBI NIH HHS R01 HL137230NIBIB NIH HHS P41 EB022544NIBIB NIH HHS U01 EB027003
6 · The paper itself

Abstract

Motion degrades image quality and quantitation of PET images, and is an obstacle to quantitative PET imaging. Simultaneous PET-MR offers a tool that can be used for correcting the motion in PET images by using anatomic information from MR imaging acquired concurrently. Motion correction can be performed by transforming a set of reconstructed PET images into the same frame or by incorporating the transformation into the system model and reconstructing the motion-corrected image. Several phantom and patient studies have validated that MR-based motion correction strategies have great promise for quantitative PET imaging in simultaneous PET-MR.

Indexed as

Myocardial ContractionOrgan MotionHumansMagnetic Resonance ImagingMagnetic Resonance SpectroscopyMultimodal ImagingPositron-Emission TomographyRadionuclide ImagingRadiopharmaceuticalsTomography, X-Ray ComputedRadiopharmaceuticalsCardiac motionMR-based motion correctionPET-MRRespiratory motion

Identifiers

PMID28576170
PMCPMC6452624

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.