TY - JOUR
T1 - Fat Quantification in Dual-Layer Detector Spectral Computed Tomography
T2 - Experimental Development and First In-Patient Validation
AU - Molwitz, Isabel
AU - Campbell, Graeme Michael
AU - Yamamura, Jin
AU - Knopp, Tobias
AU - Toedter, Klaus
AU - Fischer, Roland
AU - Wang, Zhiyue Jerry
AU - Busch, Alina
AU - Ozga, Ann Kathrin
AU - Zhang, Shuo
AU - Lindner, Thomas
AU - Sevecke, Florian
AU - Grosser, Mirco
AU - Adam, Gerhard
AU - Szwargulski, Patryk
N1 - Publisher Copyright:
© Wolters Kluwer Health, Inc. All rights reserved.
PY - 2022/7/1
Y1 - 2022/7/1
N2 - Objectives 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. Materials and Methods For this feasibility study, phantoms were created with 0%, 5%, 10%, 25%, and 40% fat and 0, 4.9, and 7.0 mg/mL iodine, respectively. Phantom scans were performed with the IQon spectral CT (Philips, the Netherlands) at 120 kV and 140 kV and 3 T magnetic resonance (MR) (Philips, the Netherlands) chemical-shift relaxometry (MRR) and MR spectroscopy (MRS). Based on maps of the photoelectric effect and Compton scattering, 3-material decomposition was done for fat, iodine, and phantom material in the image space. After written consent, 10 patients (mean age, 55 ± 18 years; 6 men) in need of a CT staging were prospectively included. All patients received contrast-enhanced abdominal dlsCT scans at 120 kV and MR imaging scans for MRR. As reference tissue for the liver and the skeletal muscle, retrospectively available non-contrast-enhanced spectral CT data sets were used. Agreement between dlsCT and MR was evaluated for the phantoms, 3 hepatic and 2 muscular regions of interest per patient by intraclass correlation coefficients (ICCs) and Bland-Altman analyses. Results The ICC was excellent in the phantoms for both 120 kV and 140 kV (dlsCT vs MRR 0.98 [95% confidence interval (CI), 0.94-0.99]; dlsCT vs MRS 0.96 [95% CI, 0.87-0.99]) and in the skeletal muscle (0.96 [95% CI, 0.89-0.98]). For log-transformed liver fat values, the ICC was moderate (0.75 [95% CI, 0.48-0.88]). Bland-Altman analysis yielded a mean difference of -0.7% (95% CI, -4.5 to 3.1) for the liver and of 0.5% (95% CI, -4.3 to 5.3) for the skeletal muscle. Interobserver and intraobserver agreement were excellent (>0.9). Conclusions Fat quantification was developed for dlsCT and agreement with MR techniques demonstrated for patient liver and muscle. Hepatic steatosis and myosteatosis can be detected in dlsCT scans from clinical routine, which retrospectively provide spectral information independent of the imaging mode.
AB - Objectives 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. Materials and Methods For this feasibility study, phantoms were created with 0%, 5%, 10%, 25%, and 40% fat and 0, 4.9, and 7.0 mg/mL iodine, respectively. Phantom scans were performed with the IQon spectral CT (Philips, the Netherlands) at 120 kV and 140 kV and 3 T magnetic resonance (MR) (Philips, the Netherlands) chemical-shift relaxometry (MRR) and MR spectroscopy (MRS). Based on maps of the photoelectric effect and Compton scattering, 3-material decomposition was done for fat, iodine, and phantom material in the image space. After written consent, 10 patients (mean age, 55 ± 18 years; 6 men) in need of a CT staging were prospectively included. All patients received contrast-enhanced abdominal dlsCT scans at 120 kV and MR imaging scans for MRR. As reference tissue for the liver and the skeletal muscle, retrospectively available non-contrast-enhanced spectral CT data sets were used. Agreement between dlsCT and MR was evaluated for the phantoms, 3 hepatic and 2 muscular regions of interest per patient by intraclass correlation coefficients (ICCs) and Bland-Altman analyses. Results The ICC was excellent in the phantoms for both 120 kV and 140 kV (dlsCT vs MRR 0.98 [95% confidence interval (CI), 0.94-0.99]; dlsCT vs MRS 0.96 [95% CI, 0.87-0.99]) and in the skeletal muscle (0.96 [95% CI, 0.89-0.98]). For log-transformed liver fat values, the ICC was moderate (0.75 [95% CI, 0.48-0.88]). Bland-Altman analysis yielded a mean difference of -0.7% (95% CI, -4.5 to 3.1) for the liver and of 0.5% (95% CI, -4.3 to 5.3) for the skeletal muscle. Interobserver and intraobserver agreement were excellent (>0.9). Conclusions Fat quantification was developed for dlsCT and agreement with MR techniques demonstrated for patient liver and muscle. Hepatic steatosis and myosteatosis can be detected in dlsCT scans from clinical routine, which retrospectively provide spectral information independent of the imaging mode.
KW - detector-based dual-energy CT
KW - dual-layer CT
KW - hepatic steatosis
KW - liver
KW - material decomposition
KW - muscle quality
KW - myosteatosis
KW - phantom study
KW - sarcopenia
KW - spectral CT
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U2 - 10.1097/RLI.0000000000000858
DO - 10.1097/RLI.0000000000000858
M3 - Article
C2 - 35148536
AN - SCOPUS:85132451776
SN - 0020-9996
VL - 57
SP - 463
EP - 469
JO - Investigative Radiology
JF - Investigative Radiology
IS - 7
ER -