Evidence map›Paper›PMID 28562622›Full record

ArticlePloS one2017

SUV-quantification of physiological lung tissue in an integrated PET/MR-system: Impact of lung density and bone tissue.

Ferdinand Seith, Holger Schmidt, Sergios Gatidis, Ilja Bezrukov, Christina Schraml, Christina Pfannenberg, Christian la Fougère, Konstantin Nikolaou, Nina Schwenzer

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2017. 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
0.9field-weighted citation impact, top 26% 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

10 citing papers in PubMed, 10 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Attenuation correction for human PET/MRI studies.Physics in medicine and biology · 2020
    Article
  9. Review
  10. 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

9 authors at 2 institutions in 1 country.

Ferdinand SeithDiagnostic and Interventional Radiology, Department of Radiology, Eberhard Karls University, Hoppe-Seyler-Straße 3, Tuebingen, Germany.
Holger SchmidtDiagnostic and Interventional Radiology, Department of Radiology, Eberhard Karls University, Hoppe-Seyler-Straße 3, Tuebingen, Germany.ORCID http://orcid.org/0000-0002-9939-7452
Sergios GatidisDiagnostic and Interventional Radiology, Department of Radiology, Eberhard Karls University, Hoppe-Seyler-Straße 3, Tuebingen, Germany.
Ilja BezrukovPreclinical Imaging and Radiopharmacy, Laboratory for Preclinical Imaging and Imaging Technology of the Werner Siemens Foundation, Department of Radiology, Eberhard Karls University, Hoppe-Seyler-Straße 3, Tuebingen, Germany.
Christina SchramlDiagnostic and Interventional Radiology, Department of Radiology, Eberhard Karls University, Hoppe-Seyler-Straße 3, Tuebingen, Germany.
Christina PfannenbergDiagnostic and Interventional Radiology, Department of Radiology, Eberhard Karls University, Hoppe-Seyler-Straße 3, Tuebingen, Germany.
Christian la FougèreNuclear Medicine, Department of Radiology, Eberhard Karls University, Hoppe-Seyler-Straße 3, Tuebingen, Germany.
Konstantin NikolaouDiagnostic and Interventional Radiology, Department of Radiology, Eberhard Karls University, Hoppe-Seyler-Straße 3, Tuebingen, Germany.
Nina SchwenzerDiagnostic and Interventional Radiology, Department of Radiology, Eberhard Karls University, Hoppe-Seyler-Straße 3, Tuebingen, Germany.
University of Tübingen · DESiemens (Germany) · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe aim of the study was to investigate the influence of lung density changes as well as bone proximity on the attenuation correction of lung standardized uptake values (SUVs). METHODS AND MATERIALS: 15 patients with mostly oncologic diseases were examined in 18F-FDG-PET/CT and subsequently in a fully integrated PET/MR scanner. From each PET dataset acquired in PET/MR, four different PET reconstructions were computed using different attenuation maps (μ-maps): i) CT-based μ-map (gold standard); ii) CT-based μ-map in which the linear attenuation coefficients (LAC) of the lung tissue was replaced by the lung LAC from the MR-based segmentation method; iii) based on reconstruction ii), the LAC of bone structures was additionally replaced with the LAC from the MR-based segmentation method; iv) the vendor-provided MR-based μ-map (segmentation-based method). Those steps were performed using MATLAB. CT Hounsfield units (HU) and SUVmean was acquired in different levels and regions of the lung. Relative differences between the differently corrected PETs were computed.

resultsCompared to the gold standard, reconstruction ii), iii) and iv) led to a relative underestimation of SUV in the posterior regions of -9.0%, -13.4% and -14.0%, respectively. Anterior and middle regions were less affected with an overestimation of about 6-8% in reconstructions ii)-iv).

conclusionIt could be shown that both, differences in lung density and the vicinity of bone tissue in the μ-map may have an influence on SUV, mostly affecting the posterior lung regions.

Indexed as

Bone DensityHumansLungMagnetic Resonance ImagingMultimodal ImagingPositron-Emission TomographyProspective Studies

Identifiers

PMID28562622
PMCPMC5451041
OpenAlexW2618982275

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LicenceCC BY
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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.