Open AccessFat Quantification in Dual-Layer Detector Spectral Computed Tomography
Author(s) -
Isabel Molwitz,
Graeme Campbell,
Jin Yamamura,
Tobias Knopp,
Klaus Toedter,
Roland Fischer,
Zhiyue Wang,
Alina Busch,
AnnKathrin Ozga,
Shuo Zhang,
Thomas Lindner,
Florian Sevecke,
Mirco Grosser,
Gerhard Adam,
Patryk Szwargulski
Publication year - 2022
Publication title -
investigative radiology
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 2.33
H-Index - 114
eISSN - 1536-0210
pISSN - 0020-9996
DOI - 10.1097/rli.0000000000000858
Subject(s) - imaging phantom , nuclear medicine , medicine , tomography , magnetic resonance imaging , dual layer , steatosis , radiology , materials science , layer (electronics) , composite material
Fat quantification by dual-energy computed tomography (DECT) provides contrast-independent objective results, for example, on hepatic steatosis or muscle quality as parameters of prognostic relevance. To date, fat quantification has only been developed and used for source-based DECT techniques as fast kVp-switching CT or dual-source CT, which require a prospective selection of the dual-energy imaging mode.It was the purpose of this study to develop a material decomposition algorithm for fat quantification in phantoms and validate it in vivo for patient liver and skeletal muscle using a dual-layer detector-based spectral CT (dlsCT), which automatically generates spectral information with every scan.