|
1000
|
Abstract/Summary
|
-
Objectives!#!To quantify the proportion of fat within the skeletal muscle as a measure of muscle quality using dual-energy CT (DECT) and to validate this methodology with MRI.!##!Methods!#!Twenty-one patients with abdominal contrast-enhanced DECT scans (100 kV/Sn 150 kV) underwent abdominal 3-T MRI. The fat fraction (DECT-FF), determined by material decomposition, and HU values on virtual non-contrast-enhanced (VNC) DECT images were measured in 126 regions of interest (≥ 6 cm!##!Results!#!The correlation was excellent between DECT-FF and MR-FF (r = 0.91), DECT VNC HU and MR-FF (r = - 0.90), and DECT-FF and DECT VNC HU (r = - 0.98). Intraclass correlation between DECT-FF and MR-FF was good (r = 0.83 [95% CI 0.71-0.90]), with a mean difference of - 0.15% (SD 3.32 [95% CI 6.35 to - 6.66]). Categorization using the SMRA yielded an eightfold difference in DECT VNC HU values between both groups (5 HU [95% CI 23-11], 42 HU [95% CI 33-56], p = 0.05). No significant relationship between DECT-FF and SMI-based classifications was observed.!##!Conclusions!#!Fat quantification within the skeletal muscle using DECT is both feasible and reliable. DECT muscle analysis offers a new approach to determine muscle quality, which is important for the diagnosis and therapeutic monitoring of sarcopenia, as a comorbidity associated with poor clinical outcome.!##!Key points!#!• Dual-energy CT (DECT) material decomposition and virtual non-contrast-enhanced DECT HU values assess muscle fat reliably. • Virtual non-contrast-enhanced dual-energy CT HU values allow to differentiate between high and low native skeletal muscle mean radiation attenuation in contrast-enhanced DECT scans. • Measuring muscle fat by dual-energy computed tomography is a new approach for the determination of muscle quality, an important parameter for the diagnostic confirmation of sarcopenia as a comorbidity associated with poor clinical outcome.
|
|
1000
|
Hinweis
|
-
DeepGreen-ID: d58579d55ce849d0b7e6d0d6f5526c42 ; 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)
|
