Quantification of liver and muscular fat using contrast-enhanced Dual Source Dual Energy Computed Tomography compared to an established multi-echo Dixon MRI sequence

Dual-Energy CT muscle fat fraction as a new imaging biomarker of body composition and survival predictor in critically ill patients

OBJECTIVE

To analyze changes in the muscular fat fraction (FF) during immobilization at the intensive care unit (ICU) using dual-energy CT (DECT) and evaluate the predictive value of the DECT FF as a new imaging biomarker for morbidity and survival.

METHODS

Immobilized ICU patients (n = 81, 43.2% female, 60.3 ± 12.7 years) were included, who received two dual-source DECT scans (CT1, CT2) within a minimum interval of 10 days between 11/2019 and 09/2022. The DECT FF was quantified for the posterior paraspinal muscle by two radiologists using material decomposition. The skeletal muscle index (SMI), muscle radiodensity attenuation (MRA), subcutaneous-/ visceral adipose tissue area (SAT, VAT), and waist circumference (WC) were assessed. Reasons for ICU admission, clinical scoring systems, therapeutic regimes, and in-hospital mortality were noted. Linear mixed models, Cox regression, and intraclass correlation coefficients were employed.

RESULTS

Between CT1 and CT2 (median 21 days), the DECT FF increased (from 20.9% ± 12.0 to 27.0% ± 12.0, p = 0.001). The SMI decreased (35.7 cm2/m2 ± 8.8 to 31.1 cm2/m2 ± 7.6, p < 0.001) as did the MRA (29 HU ±  10 to 26 HU ± 11, p = 0.009). WC, SAT, and VAT did not change. In-hospital mortality was 61.5%. In multivariable analyses, only the change in DECT FF was associated with in-hospital mortality (hazard ratio (HR) 9.20 [1.78-47.71], p = 0.008), renal replacement therapy (HR 48.67 [9.18-258.09], p < 0.001), and tracheotomy at ICU (HR 37.22 [5.66-245.02], p < 0.001). Inter-observer reproducibility of DECT FF measurements was excellent (CT1: 0.98 [0.97; 0.99], CT2: 0.99 [0.96-0.99]).

CONCLUSION

The DECT FF appears to be suitable for detecting increasing myosteatosis. It seems to have predictive value as a new imaging biomarker for ICU patients.

CLINICAL RELEVANCE STATEMENT

The dual-energy CT muscular fat fraction appears to be a robust imaging biomarker to detect and monitor myosteatosis. It has potential for prognosticating, risk stratifying, and thereby guiding therapeutic nutritional regimes and physiotherapy in critically ill patients.

KEY POINTS

The dual-energy CT muscular fat fraction detects increasing myosteatosis caused by immobilization. Change in dual-energy CT muscular fat fraction was a predictor of  in-hospital morbidity and mortality. Dual-energy CT muscular fat fraction had a predictive value superior to established CT body composition parameters.

Hepatic fat quantification in dual-layer computed tomography using a three-material decomposition algorithm

OBJECTIVES

The purpose of this study was to evaluate a three-material decomposition algorithm for hepatic fat quantification using a dual-layer computed tomography (DL-CT) and MRI as reference standard on a large patient cohort.

METHOD

A total of 104 patients were retrospectively included in our study, i.e., each patient had an MRI exam and a DL-CT exam in our institution within a maximum of 31 days. Four regions of interest (ROIs) were positioned blindly and similarly in the liver, by two independent readers on DL-CT and MRI images. For DL-CT exams, all imaging phases were included. Fat fraction agreement between CT and MRI was performed using intraclass correlation coefficients (ICC), determination coefficients R2, and Bland-Altman plots. Diagnostic performance was determined using sensitivity, specificity, and positive and negative predictive values. The cutoff for steatosis was 5%.

RESULTS

Correlation between MRI and CT data was excellent for all perfusion phases with ICC calculated at 0.99 for each phase. Determination coefficients R2 were also good for all perfusion phases (about 0.95 for all phases). Performance of DL-CT in the diagnosis of hepatic steatosis was good with sensitivity between 89 and 91% and specificity ranging from 75 to 80%, depending on the perfusion phase. The positive predictive value was ranging from 78 to 93% and the negative predictive value from 82 to 86%.

CONCLUSION

Multi-material decomposition in DL-CT allows quantification of hepatic fat fraction with a good correlation to MRI data.

CLINICAL RELEVANCE STATEMENT

The use of DL-CT allows for detection of hepatic steatosis. This is especially interesting as an opportunistic finding CT performed for other reasons, as early detection can help prevent or slowdown the development of liver metabolic disease.

KEY POINTS

• Hepatic fat fractions provided by the dual-layer CT algorithm is strongly correlated with that measured on MRI. • Dual-layer CT is accurate to detect hepatic steatosis ≥ 5%. • Dual-layer CT allows opportunistic detection of steatosis, on CT scan performed for various indications.

Differentiation of Hamartomas and Malignant Lung Tumors in Single-Phased Dual-Energy Computed Tomography

This study investigated the efficacy of single-phase dual-energy CT (DECT) in differentiating pulmonary hamartomas from malignant lung lesions using virtual non-contrast (VNC), iodine, and fat quantification. Forty-six patients with 47 pulmonary lesions (mean age: 65.2 ± 12.1 years; hamartomas-to-malignant lesions = 22:25; male: 67%) underwent portal venous DECT using histology, PET-CT and follow-up CTs as a reference. Quantitative parameters such as VNC, fat fraction, iodine density and CT mixed values were statistically analyzed. Significant differences were found in fat fractions (hamartomas: 48.9%; malignancies: 22.9%; p ≤ 0.0001) and VNC HU values (hamartomas: -20.5 HU; malignancies: 17.8 HU; p ≤ 0.0001), with hamartomas having higher fat content and lower VNC HU values than malignancies. CT mixed values also differed significantly (p ≤ 0.0001), but iodine density showed no significant differences. ROC analysis favored the fat fraction (AUC = 96.4%; sensitivity: 100%) over the VNC, CT mixed value and iodine density for differentiation. The study concludes that the DECT-based fat fraction is superior to the single-energy CT in differentiating between incidental pulmonary hamartomas and malignant lesions, while post-contrast iodine density is ineffective for differentiation.

Quantification of Hepatic Steatosis on Dual-Energy CT in Comparison With MRI mDIXON-Quant Sequence in Breast Cancer

OBJECTIVE

The study aimed to evaluate the correlation and diagnostic value of liver fat quantification in unenhanced dual-energy CT (DECT) using quantitative magnetic resonance imaging (MRI) mDIXON-Quant sequence as reference standard in patients with breast cancer.

METHODS

Patients with breast cancer were prospectively recruited between June 2018 and April 2020. Each patient underwent liver DECT and MRI mDIXON-Quant examination. The DECT-fat volume fraction (FVF) and liver-spleen attenuation differences were compared with the MRI-proton density fat fraction using scatterplots, Bland-Altman plots, and concordance correlation coefficient. Receiver operating characteristic curves were established to determine the diagnostic accuracy of hepatic steatosis by DECT.

RESULTS

A total of 216 patients with breast cancer (mean age, 50.08 ± 9.33 years) were evaluated. The DECT-FVF correlated well with MRI-proton density fat fraction ( r2 = 0.902; P < 0.001), which was higher than the difference in liver-spleen attenuation ( r2 = 0.728; P < 0.001). Bland-Altman analysis revealed slight positive bias; the mean difference was 3.986. The DECT-FVF yielded an average concordance correlation coefficient of 0.677, which was higher than the difference of liver-spleen attenuation (-0.544). The DECT-FVF and the difference in liver-spleen attenuation both lead to mild overestimation of hepatic steatosis. The areas under the curve of DECT-FVF (0.956) were higher than the difference in liver-spleen attenuation (0.807) in identifying hepatic steatosis ( P < 0.001).

CONCLUSIONS

Dual-energy CT-FVF may serve as a reliable screening and quantitative tool for hepatic steatosis in patients with breast cancer.

Quantitative multi-parameter assessment of age- and gender-related variation of back extensor muscles in healthy adults using Dixon MR imaging

OBJECTIVES

Investigate sex differences in age-related back extensor muscle degeneration using Dixon MRI and analyze the relationship between quantitative muscle parameters and back muscle strength in healthy adults.

METHODS

105 healthy subjects underwent lumbar Dixon MRI. Fat fraction (FF), cross-sectional area (CSA), functional CSA (FCSA), and relative FCSA (RFCSA) of multifidus muscle (MF) and erector spinae (ES) were quantified. Back extension muscle strength was measured using an external fixation dynamometer. ANOVA with post hoc Tukey correction was used for age group comparisons. Partial and Spearman's correlation analyzed relationships between age, muscle parameters, and muscle strength.

RESULTS

MF and ES FF significantly increased with age in both genders (r = 0.55-0.85; p < 0.001). Muscle FF increased prominently for females (40-49 years, MF and 50-59 years, ES) and males (60-73 years, MF and ES). In females, total ES FCSA and RFCSA (r =  - 0.42, - 0.37; p < 0.01) correlated with age. While in males, all MF and ES muscle size parameters, except total MF CSA, correlated with age (r =  - 0.30 to - 0.58; p < 0.05). Back extension muscle strength correlated with mean FF, total CSA, and total FCAS for MF and ES individually (p < 0.001). The combined MF + ES FCSA correlation coefficient (r = 0.63) was higher than FF (r =  - 0.51) and CSA (r = 0.59) (p < 0.001).

CONCLUSIONS

Age-related back extensor muscle degeneration varies by muscle type and sex. FCSA has the highest association with back muscle strength compared to FF and CSA.

CLINICAL RELEVANCE STATEMENT

The investigation of sex differences in age-related back extensor muscle degeneration utilizing Dixon imaging may hold significant implications for evaluating spine health and enabling earlier intervention. Muscles' FCSA could contribute to acquiring additional evidence for reflecting muscle function change.

KEY POINTS

• The multifidus muscle (MF) and erector spinae (ES) fat fraction (FF) increased with age at all lumbar disc levels in females and males. • Age-related changes in muscle morphological quantitative parameters of healthy adults were specific by muscle type and gender. • The muscle functional cross-sectional area (FCSA) measured by Dixon imaging may better monitor back extensor muscle strength changes than muscle FF and cross-sectional area (CSA).

Muscle quality determined by computed tomography predicts short-term and long-term survival after liver transplantation

Sarcopenia, the loss of muscle mass and quality, contributes to worse clinical outcome in patients with end-stage liver disease, but its impact on short- and long-term survival remains insufficiently understood. The aim of this study was to evaluate the development of computed tomography (CT) muscle parameters and their impact on short-term and long-term survival after liver transplantation. This retrospective study included patients with liver transplantation between 2011 and 2015 and a pre-transplant CT scan. Clinical characteristics, CT muscle mass and density were assessed pre-transplant, and in available CT scans at short-term (11 months) and long-term follow-up (56 months). Overall, 93/152 (61%) patients (109 male, 55 ± 10 years) suffered from sarcopenia pre-transplant. In short- (n = 50) and long-term follow-up (n = 52) the muscle mass (- 2.65 cm²/m² 95% CI [- 4.52, - 0.77], p = 0.007; - 2.96 cm²/m² [- 4.7, - 1.23], p = 0.001, respectively), and muscle density (- 3 HU [- 6, - 1], p = 0.007; - 2 HU [- 4, 0], p = 0.069) decreased. Myosteatosis was associated with a higher post-transplant mortality (survival probability: 3 months 72% vs. 95%, 1 year 63% vs. 90%, 5 years 54% vs. 84%, p = 0.001), while muscle mass was not. In conclusion, muscle mass and quality did not improve after transplant. Muscle quality predicts short- and long-term survival and could help to identify a patient's risk profile.

Fat Quantification in Dual-Layer Detector Spectral Computed Tomography: Experimental Development and First In-Patient Validation

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.