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1000 Titel
  • Virtual noncontrast images reveal gouty tophi in contrast-enhanced dual-energy CT: a phantom study
1000 Autor/in
  1. Khayata, Karim |
  2. Diekhoff, Torsten |
  3. Mews, Jürgen |
  4. Schmolke, Sydney |
  5. Kotlyarov, Maximilian |
1000 Verlag
  • Springer Vienna
1000 Erscheinungsjahr 2024
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2024-06-12
1000 Erschienen in
1000 Quellenangabe
  • 8(1):69
1000 Copyrightjahr
  • 2024
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1186/s41747-024-00466-w |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11166610/ |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • <jats:title>Abstract</jats:title><jats:sec> <jats:title>Background</jats:title> <jats:p>Dual-energy computed tomography (DECT) is useful for detecting gouty tophi. While iodinated contrast media (ICM) might enhance the detection of monosodium urate crystals (MSU), higher iodine concentrations hamper their detection. Calculating virtual noncontrast (VNC) images might improve the detection of enhancing tophi. The aim of this study was to evaluate MSU detection with VNC images from DECT acquisitions in phantoms, compared against the results with standard DECT reconstructions.</jats:p> </jats:sec><jats:sec> <jats:title>Methods</jats:title> <jats:p>A grid-like and a biophantom with 25 suspensions containing different concentrations of ICM (0 to 2%) and MSU (0 to 50%) were scanned with sequential single-source DECT using an ascending order of tube current time product at 80 kVp (16.5–220 mAs) and 135 kVp (2.75–19.25 mAs). VNC images were equivalently reconstructed at 80 and 135 kVp. Two-material decomposition analysis for MSU detection was applied for the VNC and conventional CT images. MSU detection and attenuation values were compared in both modalities.</jats:p> </jats:sec><jats:sec> <jats:title>Results</jats:title> <jats:p>For 0, 0.25, 0.5, 1, and 2% ICM, the average detection indices (DIs) for all MSU concentrations (35–50%) with VNC postprocessing were respectively 25.2, 36.6, 30.9, 38.9, and 45.8% for the grid phantom scans and 11.7, 9.4, 5.5, 24.0, and 25.0% for the porcine phantom scans. In the conventional CT image group, the average DIs were respectively 35.4, 54.3, 45.4, 1.0, and 0.0% for the grid phantom and 19.4, 17.9, 3.0, 0.0, and 0.0% for the porcine phantom scans.</jats:p> </jats:sec><jats:sec> <jats:title>Conclusions</jats:title> <jats:p>VNC effectively reduces the suppression of information caused by high concentrations of ICM, thereby improving the detection of MSU.</jats:p> </jats:sec><jats:sec> <jats:title>Relevance statement</jats:title> <jats:p>Contrast-enhanced DECT alone may suffice for diagnosing gout without a native acquisition.</jats:p> </jats:sec><jats:sec> <jats:title>Key points</jats:title> <jats:p>• Highly concentrated contrast media hinders monosodium urate crystal detection in CT imaging</jats:p> <jats:p>• Virtual noncontrast imaging redetects monosodium urate crystals in high-iodinated contrast media concentrations.</jats:p> <jats:p>• Contrast-enhanced DECT alone may suffice for diagnosing gout without a native acquisition.</jats:p> </jats:sec><jats:sec> <jats:title>Graphical Abstract</jats:title> </jats:sec>
1000 Sacherschließung
lokal Gout/diagnostic imaging [MeSH]
lokal Swine [MeSH]
lokal Tomography (x-ray computed)
lokal Uric acid
lokal Tomography, X-Ray Computed/methods [MeSH]
lokal Uric Acid/analysis [MeSH]
lokal Animals [MeSH]
lokal Contrast Media [MeSH]
lokal Gout
lokal Original Article
lokal Phantoms, Imaging [MeSH]
lokal Radiography, Dual-Energy Scanned Projection/methods [MeSH]
lokal Contrast media
lokal Phantoms (imaging)
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://orcid.org/0009-0000-2369-7179|https://frl.publisso.de/adhoc/uri/RGlla2hvZmYsIFRvcnN0ZW4=|https://frl.publisso.de/adhoc/uri/TWV3cywgSsO8cmdlbg==|https://frl.publisso.de/adhoc/uri/U2NobW9sa2UsIFN5ZG5leQ==|https://frl.publisso.de/adhoc/uri/S290bHlhcm92LCBNYXhpbWlsaWFu
1000 Hinweis
  • DeepGreen-ID: de83874b72f645e09e09aaeb39c5198a ; metadata provieded by: DeepGreen (https://www.oa-deepgreen.de/api/v1/), LIVIVO search scope life sciences (http://z3950.zbmed.de:6210/livivo), Crossref Unified Resource API (https://api.crossref.org/swagger-ui/index.html), to.science.api (https://frl.publisso.de/), ZDB JSON-API (beta) (https://zeitschriftendatenbank.de/api/), lobid - Dateninfrastruktur für Bibliotheken (https://lobid.org/resources/search)
1000 Label
1000 Förderer
  1. Canon Medical Systems Corporation |
  2. Charité – Universitätsmedizin Berlin |
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1000 Dateien
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    1000 Förderer Canon Medical Systems Corporation |
    1000 Förderprogramm -
    1000 Fördernummer -
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    1000 Förderer Charité – Universitätsmedizin Berlin |
    1000 Förderprogramm -
    1000 Fördernummer -
1000 Objektart article
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1000 Erstellt am 2025-07-06T20:46:19.430+0200
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1000 Zuletzt bearbeitet 2025-07-30T01:22:42.939+0200
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