Fat Fraction Measurements Using a Three-Material Decomposition Dual-Energy CT Technique Accounting for Bone Minerals: Evaluation in a Bone Marrow Phantom Using MRI as Reference

Author(s):  
Baojun Li ◽  
Ning Hua ◽  
Janelle Li ◽  
V. Carlota Andreu-Arasa ◽  
Christina LeBedis ◽  
...  
Author(s):  
I. Molwitz ◽  
M. Leiderer ◽  
R. McDonough ◽  
R. Fischer ◽  
A-K. Ozga ◽  
...  

Abstract 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 cm2) within the posterior paraspinal muscle. For validation, the MR-based fat fraction (MR-FF) was assessed by chemical shift relaxometry. Patients were categorized into groups of high or low skeletal muscle mean radiation attenuation (SMRA) and classified as either sarcopenic or non-sarcopenic, according to the skeletal muscle index (SMI) and cut-off values from non-contrast-enhanced single-energy CT. Spearman’s and intraclass correlation, Bland-Altman analysis, and mixed linear models were employed. 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.


2019 ◽  
Vol 23 (04) ◽  
pp. 392-404 ◽  
Author(s):  
Frances E. Walstra ◽  
Jonathan Hickle ◽  
Peter Duggan ◽  
Rashid Alsharhan ◽  
Nicolas Murray ◽  
...  

Dual-energy computed tomography (DECT) has the potential to detect musculoskeletal pathology with greater sensitivity than conventional CT alone at no additional radiation dose to the patient. It therefore has the potential to reduce the need for further diagnostic imaging or procedures (e.g., joint aspirations in the case of gout or magnetic resonance imaging to confirm undisplaced fractures).DECT is a well-established technique for the detection of gout arthropathy. Multiple newer applications have shown clinical potential including bone marrow edema detection and metal artifact reduction. Collagen analysis, bone marrow lesion detection, and iodine mapping in CT arthrography are areas of possible future application and development.This article outlines 10 tips on the use of DECT imaging of the musculoskeletal system, explaining the technique and indications with practical suggestions to help guide the radiologist.


Author(s):  
Vitali Koch ◽  
Felix Christoph Müller ◽  
Kasper Gosvig ◽  
Moritz H. Albrecht ◽  
Ibrahim Yel ◽  
...  

2015 ◽  
Vol 62 (2) ◽  
pp. 754-765 ◽  
Author(s):  
Wenli Cai ◽  
June-Goo Lee ◽  
Da Zhang ◽  
Se Hyung Kim ◽  
Michael Zalis ◽  
...  

2018 ◽  
Vol 19 (5) ◽  
pp. 676-683 ◽  
Author(s):  
Lianna D. Di Maso ◽  
Jessie Huang ◽  
Michael F. Bassetti ◽  
Larry A. DeWerd ◽  
Jessica R. Miller

2019 ◽  
Vol 24 (5) ◽  
pp. 499-506
Author(s):  
Daisuke Kawahara ◽  
Shuichi Ozawa ◽  
Kazushi Yokomachi ◽  
Toru Higaki ◽  
Takehiro Shiinoki ◽  
...  

2020 ◽  
Vol 75 (2) ◽  
pp. 156.e11-156.e19
Author(s):  
Y. Wang ◽  
Y. Chen ◽  
H. Zheng ◽  
X. Huang ◽  
C. Shan ◽  
...  

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