Download
41747_2024_Article_528.pdf 1,07MB
WeightNameValue
1000 Titel
  • Material decomposition approaches for monosodium urate (MSU) quantification in gouty arthritis: a (bio)phantom study
1000 Autor/in
  1. Diekhoff, Torsten |
  2. Schmolke, Sydney Alexandra |
  3. Khayata, Karim |
  4. Mews, Jürgen |
  5. Kotlyarov, Maximilian |
1000 Verlag Springer Vienna
1000 Erscheinungsjahr 2024
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2024-11-08
1000 Erschienen in
1000 Quellenangabe
  • 8(1):127
1000 Copyrightjahr
  • 2024
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1186/s41747-024-00528-z |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11549270/ |
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 a noninvasive diagnostic tool for gouty arthritis. This study aimed to compare two postprocessing techniques for monosodium urate (MSU) detection: conventional two-material decomposition and material map-based decomposition.</jats:p> </jats:sec><jats:sec> <jats:title>Methods</jats:title> <jats:p>A raster phantom and an <jats:italic>ex vivo</jats:italic> biophantom, embedded with four different MSU concentrations, were scanned in two high-end CT scanners. Scanner 1 used the conventional postprocessing method while scanner 2 employed the material map approach. Volumetric analysis was performed to determine MSU detection, and image quality parameters, such as signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), were computed.</jats:p> </jats:sec><jats:sec> <jats:title>Results</jats:title> <jats:p>The material map-based method demonstrated superior MSU detection. Specifically, scanner 2 yielded total MSU volumes of 5.29 ± 0.28 mL and 4.52 ± 0.29 mL (mean ± standard deviation) in the raster and biophantom, respectively, <jats:italic>versus</jats:italic> 2.35 ± 0.23 mL and 1.15 ± 0.17 mL for scanner 1. Radiation dose correlated positively with detection for the conventional scanner, while there was no such correlation for the material map-based decomposition method in the biophantom. Despite its higher detection rate, material map-based decomposition was inferior in terms of SNR, CNR, and artifacts.</jats:p> </jats:sec><jats:sec> <jats:title>Conclusion</jats:title> <jats:p>While material map-based decomposition resulted in superior MSU detection, it is limited by challenges such as increased artifacts. Our findings highlight the potential of this method for gout diagnosis while underscoring the need for further research to enhance its clinical reliability.</jats:p> </jats:sec><jats:sec> <jats:title>Relevance statement</jats:title> <jats:p>Advanced postprocessing such as material-map-based two-material decomposition might improve the sensitivity for gouty arthritis in clinical practice, thus, allowing for lower radiation doses or better sensitivity for gouty tophi.</jats:p> </jats:sec><jats:sec> <jats:title>Key Points</jats:title> <jats:p><jats:list list-type='bullet'> <jats:list-item> <jats:p>Dual-energy CT showed limited sensitivity for tophi with low MSU concentrations.</jats:p> </jats:list-item> <jats:list-item> <jats:p>Materiel-map-based decomposition increased sensitivity compared to conventional two-material decomposition.</jats:p> </jats:list-item> <jats:list-item> <jats:p>The advantages of material-map-based decomposition outweigh lower image quality and increased artifact load.</jats:p> </jats:list-item> </jats:list></jats:p> </jats:sec><jats:sec> <jats:title>Graphical Abstract</jats:title> </jats:sec>
1000 Sacherschließung
lokal Original Article
lokal Signal-To-Noise Ratio [MeSH]
lokal Humans [MeSH]
lokal Phantoms, Imaging [MeSH]
lokal Arthritis (gouty)
lokal Arthritis, Gouty/diagnostic imaging [MeSH]
lokal Tomography (x-ray computed)
lokal Uric acid
lokal Tomography, X-Ray Computed/methods [MeSH]
lokal Uric Acid [MeSH]
lokal Image processing (computer-assisted)
lokal Phantoms (imaging)
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://orcid.org/0000-0003-3593-1449|https://frl.publisso.de/adhoc/uri/U2NobW9sa2UsIFN5ZG5leSBBbGV4YW5kcmE=|https://frl.publisso.de/adhoc/uri/S2hheWF0YSwgS2FyaW0=|https://frl.publisso.de/adhoc/uri/TWV3cywgSsO8cmdlbg==|https://frl.publisso.de/adhoc/uri/S290bHlhcm92LCBNYXhpbWlsaWFu
1000 Hinweis
  • DeepGreen-ID: dad39f12a68f4e97b0ecd26154b46753 ; 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 Dateien
1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6520081.rdf
1000 Erstellt am 2025-07-05T22:30:05.924+0200
1000 Erstellt von 322
1000 beschreibt frl:6520081
1000 Zuletzt bearbeitet 2025-08-11T08:01:43.440+0200
1000 Objekt bearb. Mon Aug 11 08:01:43 CEST 2025
1000 Vgl. frl:6520081
1000 Oai Id
  1. oai:frl.publisso.de:frl:6520081 |
1000 Sichtbarkeit Metadaten public
1000 Sichtbarkeit Daten public
1000 Gegenstand von

View source