Evidence map›Paper›PMID 27384608›Full record

ArticlePhysics in medicine and biology2016

Transmission imaging for integrated PET-MR systems.

Spencer L Bowen, Niccolò Fuin, Michael A Levine, Ciprian Catana

Abstract read
In one paragraph

Article in Physics in medicine and biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed, 19 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Attenuation correction for human PET/MRI studies.Physics in medicine and biology · 2020
    Article
  8. Review
  9. Intrascanner Reproducibility of an SPM-based Head MR-based Attenuation Correction Method.IEEE transactions on radiation and plasma medical sciences · 2019
    Article
  10. Dixon-VIBE Deep Learning (DIVIDE) Pseudo-CT Synthesis for Pelvis PET/MR Attenuation Correction.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2019
    Article
  11. PET/MRI in the Presence of Metal Implants: Completion of the Attenuation Map from PET Emission Data.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2017
    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

4 authors at 2 institutions in 1 country.

Spencer L BowenAthinoula A. Martinos Center for Biomedical Imaging, Bldg. 149, Rm. 2301, 13th St., Charlestown, MA 02129, USA.
Niccolò FuinAthinoula A. Martinos Center for Biomedical Imaging, Bldg. 149, Rm. 2301, 13th St., Charlestown, MA 02129, USA.
Michael A LevineAthinoula A. Martinos Center for Biomedical Imaging, Bldg. 149, Rm. 2301, 13th St., Charlestown, MA 02129, USA.
Ciprian CatanaAthinoula A. Martinos Center for Biomedical Imaging, Bldg. 149, Rm. 2301, 13th St., Charlestown, MA 02129, USA.
Harvard University Press · USAthinoula A. Martinos Center for Biomedical Imaging · US

Funding

MOLECULAR BIOPHYSICS TRAINING GRANTT32GM008313 · NIGMS · HARVARD UNIVERSITY (MEDICAL SCHOOL) · PI MURTHY, VENKATESH N · 1989 to 2023
$14.6M
MR-assisted PET data optimization for neuroimaging studiesR01EB014894 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI CATANA, CIPRIAN · 2013 to 2016
$2.6M
A Storage Area Network for Structural and Functional Image AnalysisS10RR023401 · NCRR · MASSACHUSETTS GENERAL HOSPITAL · PI FISCHL, BRUCE · 2009 to 2009
$2.0M
SILICON GRAPHICS PRISM EXTREME 128P/1TBS10RR023043 · NCRR · MASSACHUSETTS GENERAL HOSPITAL · PI SORENSEN, ALMA GREGORY · 2006 to 2006
$1.5M
Computeserver Structural &Functional Image AnalysisS10RR019307 · NCRR · MASSACHUSETTS GENERAL HOSPITAL · PI FISCHL, BRUCE · 2004 to 2004
$496k
NCRR NIH HHS S10 RR019307NCRR NIH HHS S10 RR023043NCRR NIH HHS S10 RR023401NIBIB NIH HHS R01 EB014894NIGMS NIH HHS T32 GM008313
6 · The paper itself

Abstract

Attenuation correction for PET-MR systems continues to be a challenging problem, particularly for body regions outside the head. The simultaneous acquisition of transmission scan based μ-maps and MR images on integrated PET-MR systems may significantly increase the performance of and offer validation for new MR-based μ-map algorithms. For the Biograph mMR (Siemens Healthcare), however, use of conventional transmission schemes is not practical as the patient table and relatively small diameter scanner bore significantly restrict radioactive source motion and limit source placement. We propose a method for emission-free coincidence transmission imaging on the Biograph mMR. The intended application is not for routine subject imaging, but rather to improve and validate MR-based μ-map algorithms; particularly for patient implant and scanner hardware attenuation correction. In this study we optimized source geometry and assessed the method's performance with Monte Carlo simulations and phantom scans. We utilized a Bayesian reconstruction algorithm, which directly generates μ-map estimates from multiple bed positions, combined with a robust scatter correction method. For simulations with a pelvis phantom a single torus produced peak noise equivalent count rates (34.8 kcps) dramatically larger than a full axial length ring (11.32 kcps) and conventional rotating source configurations. Bias in reconstructed μ-maps for head and pelvis simulations was  ⩽4% for soft tissue and  ⩽11% for bone ROIs. An implementation of the single torus source was filled with (18)F-fluorodeoxyglucose and the proposed method quantified for several test cases alone or in comparison with CT-derived μ-maps. A volume average of 0.095 cm(-1) was recorded for an experimental uniform cylinder phantom scan, while a bias of  <2% was measured for the cortical bone equivalent insert of the multi-compartment phantom. Single torus μ-maps of a hip implant phantom showed significantly less artifacts and improved dynamic range, and differed greatly for highly attenuating materials in the case of the patient table, compared to CT results. Use of a fixed torus geometry, in combination with translation of the patient table to perform complete tomographic sampling, generated highly quantitative measured μ-maps and is expected to produce images with significantly higher SNR than competing fixed geometries at matched total acquisition time.

Indexed as

Magnetic Resonance ImagingPositron-Emission TomographySystems IntegrationAlgorithmsArtifactsBayes TheoremFluorodeoxyglucose F18HipHumansMonte Carlo MethodPhantoms, ImagingSignal-To-Noise RatioTomography, X-Ray ComputedFluorodeoxyglucose F18

Identifiers

PMID27384608
PMCPMC5004490
OpenAlexW2462673449

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.