Quantification of the Fat Fraction in the Liver Using Dual-Energy Computed Tomography and Multimaterial Decomposition:

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.

Dual-Energy CT Material Decomposition: The Value in the Detection of Lymph Node Metastasis from Breast Cancer

PURPOSE

To evaluate the diagnostic performance of a dual-energy computed tomography (DECT)-based material decomposition algorithm for iodine quantification and fat fraction analysis to detect lymph node metastases in breast cancer patients.

MATERIALS AND METHODS

30 female patients (mean age, 63.12 ± 14.2 years) diagnosed with breast cancer who underwent pre-operative chest DECT were included. To establish a reference standard, the study correlated histologic repots after lymphadenectomy or confirming metastasis in previous/follow-up examinations. Iodine concentration and fat fraction were determined through region-of-interest measurements on venous DECT iodine maps. Receiver operating characteristic curve analysis was conducted to identify the optimal threshold for differentiating between metastatic and non-metastatic lymph nodes.

RESULTS

A total of 168 lymph nodes were evaluated, divided into axillary (metastatic: 46, normal: 101) and intramammary (metastatic: 10, normal: 11). DECT-based fat fraction values exhibited significant differences between metastatic (9.56 ± 6.20%) and non-metastatic lymph nodes (41.52 ± 19.97%) (p < 0.0001). Absolute iodine concentrations showed no significant differences (2.25 ± 0.97 mg/mL vs. 2.08 ± 0.97 mg/mL) (p = 0.7999). The optimal fat fraction threshold for diagnosing metastatic lymph nodes was determined to be 17.75%, offering a sensitivity of 98% and a specificity of 94%.

CONCLUSIONS

DECT fat fraction analysis emerges as a promising method for identifying metastatic lymph nodes, overcoming the morpho-volumetric limitations of conventional CT regarding lymph node assessment. This innovative approach holds potential for improving pre-operative lymph node evaluation in breast cancer patients, offering enhanced diagnostic accuracy.

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.

Liver fat volume fraction measurements based on multi-material decomposition algorithm in patients with nonalcoholic fatty liver disease: the influences of blood vessel, location, and iodine contrast

BACKGROUND

In recent years, spectral CT-derived liver fat quantification method named multi-material decomposition (MMD) is playing an increasingly important role as an imaging biomarker of hepatic steatosis. However, there are various measurement ways with various results among different researches, and the impact of measurement methods on the research results is unknown. The aim of this study is to evaluate the reproducibility of liver fat volume fraction (FVF) using MMD algorithm in nonalcoholic fatty liver disease (NAFLD) patients when taking blood vessel, location, and iodine contrast into account during measurement.

METHODS

This retrospective study was approved by the institutional ethics committee, and the requirement for informed consent was waived because of the retrospective nature of the study. 101 patients with NAFLD were enrolled in this study. Participants underwent non-contrast phase (NCP) and two-phase enhanced CT scanning (late arterial phase (LAP) and portal vein phase (PVP)) with spectral mode. Regions of interest (ROIs) were placed at right posterior lobe (RPL), right anterior lobe (RAL) and left lateral lobe (LLL) to obtain FVF values on liver fat images without and with the reference of enhanced CT images. The differences of FVF values measured under different conditions (ROI locations, with/without enhancement reference, NCP and enhanced phases) were compared. Friedman test was used to compare FVF values among three phases for each lobe, while the consistency of FVF values was assessed between each two phases using Bland-Altman analysis.

RESULTS

Significant difference was found between FVF values obtained without and with the reference of enhanced CT images. There was no significant difference about FVF values obtained from NCP images under the reference of enhanced CT images between any two lobes or among three lobes. The FVF value increased after the contrast injection, and there were significant differences in the FVF values among three scanning phases. Poor consistencies of FVF values between each two phases were found in each lobe by Bland-Altman analysis.

CONCLUSION

MMD algorithm quantifying hepatic fat was reproducible among different lobes, while was influenced by blood vessel and iodine contrast.

Quantitative dual-energy CT techniques in the abdomen

Advances in dual-energy CT (DECT) technology and spectral techniques are catalyzing the widespread implementation of this technology across multiple radiology subspecialties. The inclusion of energy- and material-specific datasets has ushered overall improvements in CT image contrast and noise as well as artifacts reduction, leading to considerable progress in radiologists' ability to detect and characterize pathologies in the abdomen. The scope of this article is to provide an overview of various quantitative clinical DECT applications in the abdomen and pelvis. Several of the reviewed applications have not reached mainstream clinical use and are considered investigational. Nonetheless awareness of such applications is critical to having a fully comprehensive knowledge base to DECT and fostering future clinical implementation.

Dual-energy CT: Technical considerations and clinical applications

Although dual-energy CT was initially described by Hounsfield in 1973, it remains underused in clinical practice. It is therefore important to emphasize the clinical benefits and limitations of this technique. Iodine mapping makes it possible to quantify the uptake of iodine, which is very important in characterizing tumors, lung perfusion, pulmonary nodules, and the tumor response to new treatments. Dual-energy CT also makes it possible to obtain virtual single-energy images and virtual images without iodinated contrast or without calcium, as well as to separate materials such as uric acid or fat and to elaborate hepatic iron overload maps. In this article, we review some of the clinical benefits and technical limitations to improve understanding of dual-energy CT and expand its use in clinical practice.

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.

Detection of fatty liver using virtual non-contrast dual-energy CT

PURPOSE

Determine whether liver attenuation measured on dual-energy CT (DECT) virtual non-contrast examinations predicts the presence of fatty liver.

METHODS

Single-institution retrospective review from 2016 to 2020 found patients with DECT and proton density fat fraction MRI (MRI PDFF) within 30 days. MRI PDFF was the reference standard for determining hepatic steatosis. Attenuation measurements from VNC and mixed 120 kVp-like images were compared to MRI PDFF in the right and left lobes. Performance of VNC was compared to measurement of the liver-spleen attenuation difference (LSAD).

RESULTS

128 patients were included (69 men, 59 women) with mean age 51.6 years (range 14-98 years). > 90% of patients received CT and MRI in the emergency department or as inpatients. Median interval between DECT and MRI PDFF was 2 days (range 0-28 days). Prevalence of fatty liver using the reference standard (MRI PDFF > 6%) was 24%. Pearson correlation coefficient between VNC and MRI- DFF was -0.64 (right) and -0.68 (left, both p < 0.0001). For LSAD, correlation was - 0.43 in both lobes (p < 0.0001). Considering MRI PDFF > 6% as diagnostic of steatosis, area under the receiver operator characteristic curve (AUC) was 0.834 and 0.872 in the right and left hepatic lobes, with an optimal threshold of 54.8 HU (right) and 52.5 HU (left), yielding sensitivity/specificity of 57%/93.9% (right) and 67.9%/90% (left). For LSAD, AUC was 0.808 (right) and 0.767 (left) with optimal sensitivity/specificity of 93.3%/57.1% (right) and 78.6%/68% (left).

CONCLUSION

Attenuation measured at VNC CT was moderately correlated with liver fat content and had > 90% specificity for diagnosis of fatty liver.

Quantitative Evaluation of Nonalcoholic Fatty Liver in Rat by Material Decomposition Techniques using Rapid-switching Dual Energy CT

RATIONALE AND OBJECTIVES

To evaluate the material decomposition (MD) techniques in rapid kVp switching dual-energy CT (rsDECT) for quantifying liver fat content in rats with nonalcoholic fatty liver.

MATERIALS AND METHODS

Fifty male Sprague-Dawley (SD) rats were divided into study group (n=37) and control group (n = 13) and underwent rsDECT examination at different intervals. All the data analysis was performed using AW4.7 workstation. The fat contents under the traditional fat(water), fat(blood), and fat(muscle) material decomposition (MD) images and the fat volume fraction (FVF) from the liver fat maps generated using multi-material decomposition (MMD) technique were measured. The pathological grades (grade 0, 1, 2 and 3) of fatty liver were determined after euthanasia. The measurement differences among different grades and the correlation of measurements with different grades was analyzed using ANOVA and Spearman correlation, respectively. A receiver operating characteristics (ROC) curve was used to analyze the diagnostic efficacies of fat contents and FVF.

RESULTS

There were statistically significant differences in FVF and fat contents under fat(water), fat(blood), fat(muscle) based MD images among different grades. These values correlated well with the pathological grades (R-value: 0.90, 0.75, 0.79, 0.80, all p <0.001), with FVF having the highest correlation. The area-under-the-curve in ROC of using FVF was the highest, with the cut-off value of 0.92 for sensitivity of 89.2% and specificity of 100%.

CONCLUSION

The rsDECT MD techniques could quantitatively evaluate the fat content of fatty liver in rat, with the FVF from MMD having the highest correlation with pathological grades.

Assessment of Liver Fat: Dual-Energy CT versus Conventional CT with and without Contrast

We assessed the correlation between liver fat percentage using dual-energy CT (DECT) and Hounsfield unit (HU) measurements in contrast and non-contrast CT. This study included 177 patients in two patient groups: Group A (n = 125) underwent whole body non-contrast DECT and group B (n = 52) had a multiphasic DECT including a conventional non-contrast CT. Three regions of interest were placed on each image series, one in the left liver lobe and two in the right to measure Hounsfield Units (HU) as well as liver fat percentage. Linear regression analysis was performed for each group as well as combined. Receiver operating characteristic (ROC) curve was generated to establish the optimal fat percentage threshold value in DECT for predicting a non-contrast threshold of 40 HU correlating to moderate-severe liver steatosis. We found a strong correlation between fat percentage found with DECT and HU measured in non-contrast CT in group A and B individually (R² = 0.81 and 0.86, respectively) as well as combined (R² = 0.85). No significant difference was found when comparing venous and arterial phase DECT fat percentage measurements in group B (p = 0.67). A threshold of 10% liver fat found with DECT had 95% sensitivity and 95% specificity for the prediction of a 40 HU threshold using non-contrast CT. In conclusion, liver fat quantification using DECT shows high correlation with HU measurements independent of scan phase.

Dual-Energy Computed Tomography in Diffuse Liver Diseases

Dual-energy computed tomography (DECT) is an advancement in the field of CT, where images are acquired at two energies. Materials are identified and quantified based on their attenuation pattern at two different energy beams using various material decomposition algorithms. With its ability to identify and quantify materials such as fat, calcium, iron, and iodine, DECT adds great value to conventional CT and has innumerable applications in body imaging. Continuous technological advances in CT scanner hardware, material decomposition algorithms, and image reconstruction software have led to considerable growth of these applications. Among all organs, the liver is the most widely investigated by DECT, and DECT has shown promising results in most liver applications. In this article, we aim to provide an overview of the role of DECT in the assessment of diffuse liver diseases, mainly the deposition of fat, fibrosis, and iron and review the most relevant literature.

Diagnostic Modalities of Non-Alcoholic Fatty Liver Disease: From Biochemical Biomarkers to Multi-Omics Non-Invasive Approaches

Non-Alcoholic Fatty Liver Disease (NAFLD) is currently the most common cause of chronic liver disease worldwide, and its prevalence is increasing globally. NAFLD is a multifaceted disorder, and its spectrum includes steatosis to steatohepatitis, which may evolve to advanced fibrosis and cirrhosis. In addition, the presence of NAFLD is independently associated with a higher cardiometabolic risk and increased mortality rates. Considering that the vast majority of individuals with NAFLD are mainly asymptomatic, early diagnosis of non-alcoholic steatohepatitis (NASH) and accurate staging of fibrosis risk is crucial for better stratification, monitoring and targeted management of patients at risk. To date, liver biopsy remains the gold standard procedure for the diagnosis of NASH and staging of NAFLD. However, due to its invasive nature, research on non-invasive tests is rapidly increasing with significant advances having been achieved during the last decades in the diagnostic field. New promising non-invasive biomarkers and techniques have been developed, evaluated and assessed, including biochemical markers, imaging modalities and the most recent multi-omics approaches. Our article provides a comprehensive review of the currently available and emerging non-invasive diagnostic tools used in assessing NAFLD, also highlighting the importance of accurate and validated diagnostic tools.

Influence of Radiation Dose and Reconstruction Kernel on Fat Fraction Analysis in Dual-energy CT: A Phantom Study

BACKGROUND/AIM

The quantitative evaluation of fat tissue, mainly for the determination of liver steatosis, is possible by using dual-energy computed tomography. Different photon energy acquisitions allow for estimation of attenuation coefficients. The effect of variation in radiation doses and reconstruction kernels on fat fraction estimation was investigated.

MATERIALS AND METHODS

A six-probe-phantom with fat concentrations of 0%, 20%, 40%, 60%, 80%, and 100% were scanned in Sn140/100 kV with radiation doses ranging between 20 and 200 mAs before and after calibration. Images were reconstructed using iterative kernels (I26,Q30,I70).

RESULTS

Fat fractions measured in dual-energy computed tomography (DECT) were consistent with the 20%-stepwise varying actual concentrations. Variation in radiation dose resulted in 3.1% variation of fat fraction. Softer reconstruction kernel (I26) underestimated the fat fraction (-9.1%), while quantitative (Q30) and sharper kernel (I70) overestimated fat fraction (10,8% and 13,1, respectively).

CONCLUSION

The fat fraction in DECT approaches the actual fat concentration when calibrated to the reconstruction kerneö. Variation of radiation dose caused an acceptable 3% variation.

Multimaterial decomposition algorithm for quantification of fat in hepatocellular carcinoma using rapid kilovoltage-switching dual-energy CT: A comparison with chemical-shift MR imaging

Understanding intratumoral fat in hepatocellular carcinoma (HCC) is clinically important to elucidate prognosis. We sought to quantify HCC and liver fat with a multimaterial decomposition (MMD) algorithm with rapid kilovoltage-switching dual-energy computed tomography (DECT) relative to chemical-shift magnetic resonance imaging (CSI).In this retrospective study, 40 consecutive patients with HCC underwent non-contrast-enhanced (non-CE) and four-phases contrast-enhanced (four-CE) DECT (80 and 140 kVp) and abdominal MR imaging (including CSI) between April 2011 and December 2012. Fat volume fraction (FVFDECT) maps were generated by MMD algorithm to quantify HCC and liver fat. Fat fraction measured by CSI (FFCSI) was determined for HCC and liver on dual-echo sequence using 1.5- or 3-Tesla MR systems. The correlation between FVFDECT and FFCSI was evaluated using Pearson correlation test, while non-CE FVFDECT and four-CE FVFDECT were compared by one-way ANOVA and Bland-Altman analysis.Forty patients (mean age, 70.1 years ± 7.8; 25 males) were evaluated. FVFDECT and FFCSI exhibited weak to moderate correlations for HCC in non-CE and four-CE except in equilibrium phase (r = 0.42, 0.44, 0.35, and 0.33; all P < .05), and very strong correlations for liver in all phases (r = 0.86, 0.83, 0.85, 0.87, and 0.84; all P < .05). Those correlation coefficients were significantly higher for liver for each phase (all P < .05). FVFDECT did not differ significantly across scan phases regarding HCC or liver (P = .076 and 0.56). Bland-Altman analysis showed fixed bias in all phases between non- and four-CE FVFDECT in HCC and liver.As compared with liver, correlations between FVF measured by DECT-based MMD and FF measured by CSI were weak in HCC in all phases. FVF is reproducible across all scan phases in HCC and liver. The MMD algorithm requires modification for HCC fat quantification given the heterogeneous components of HCC.

Dual-energy CT of the liver: True noncontrast vs. virtual noncontrast images derived from multiple phases for the diagnosis of fatty liver

PURPOSE

To evaluate the difference in liver density and to compare the performance to diagnose fatty liver between true noncontrast (TNC) images and virtual noncontrast (VNC) images generated from dual-energy CT (DECT).

MATERIALS AND METHODS

Patients who underwent liver dynamic DECT and MRI were included (n = 49). Two observers measured the liver and spleen densities on TNC images and three VNC images from the arterial, portal and delayed phases of DECT (VNCa, VNCp and VNCd, respectively). The liver-minus-spleen density (density L-S) and liver-to-spleen ratio (density L/S) were calculated. The CT parameters were compared between normal liver patients and fatty liver patients by using the independent t-test. Differences and agreements between measurements on TNC images and VNC images were evaluated by using the paired t-test and Bland-Altman analysis. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of CT parameters for diagnosing fatty liver.

RESULTS

All CT parameters measured on TNC and VNC images were significantly higher in normal liver patients than in fatty liver patients. Although the mean liver densities on VNC images were significantly lower than those on TNC images, all CT parameters showed good agreement between TNC images and VNC images. The diagnostic performances of CT parameters on VNC images were not significantly different from those on TNC images.

CONCLUSION

Although the liver and spleen density on VNC images was significantly lower than that on TNC images, the diagnostic performances of CT parameters on three VNC images from multiple phases were similar to those on TNC images for diagnosing fatty liver.

Comparison of Multimaterial Decomposition Fat Fraction with DECT and Proton Density Fat Fraction with IDEAL IQ MRI for Quantification of Liver Steatosis in a Population Exposed to Chemotherapy

Introduction:

Oncologic patients who develop chemotherapy-associated liver injury (CALI) secondary to chemotherapy treatment tend to have worse outcomes. Biopsy remains the gold standard for the diagnosis of hepatic steatosis. The purpose of this article is to compare 2 alternatives: Proton-Density-Fat-Fraction (PDFF) MRI and MultiMaterial-Decomposition (MMD) DECT.

Materials and Methods:

49 consecutive oncologic patients treated with Chemotherapy underwent abdominal DECT and abdominal MRI within 2 weeks of each other. Two radiologists tracked Regions of Interest independently both in the PDFF fat maps and in the MMD DECT fat maps. Non-parametric exact Wilcoxon signed rank test and Cohen’s K were used to compare the 2 sequences and to evaluate the agreement.

Results:

There was no statistically significant difference in the fat fraction measured as a continuous value between PDFF and DECT between 2 readers. Within the same imaging method (PDFF) the degree of agreement based on the k coefficient between reader 1 and reader 2 is 0.88 (p-value < 0.05). Similarly, for single-source DECT(ssDECT) the degree of agreement based on the k coefficient between reader 1 and reader 2 is 0.97 (p-value < 0.05).

Conclusions:

The results of this study demonstrate that the hepatic fat fraction of ssDECT with MMD are not significantly different from PDFF. This could be an advantage in an oncological population that undergoes serial CT scans for follow up of chemotherapy response.

The correlation between hepatic fat fraction evaluated by dual-energy computed tomography and high-risk coronary plaques in patients with non-alcoholic fatty liver disease

PURPOSE

To determine the relationship between non-alcoholic fatty liver disease (NAFLD) evaluated by a hepatic fat fraction (HFF) using dual-energy computed tomography (DECT) and high-risk coronary plaques (HRP) in NAFLD patients.

METHODS

We conducted a matched case-control study involving 172 NAFLD individuals recruited from August 2019 to September 2020. They underwent dual-energy coronary computed tomographic angiography and were classified as no-plaque, HRP negative and HRP positive groups. HFF values were measured using multimaterial decomposition algorithm of DECT, and the differences among three groups were compared. Multiple logistic regression analysis was performed to determine the independent correlation between HFF and HRP. Spearman rank correlation was used to assess the correlations between HFF and multiple variables.

RESULTS

HRP positive group (15.3%) had higher HFF values than no-plaque (6.9%) and HRP negative groups (8.9%) (P < 0.001). After adjusting for confounding variables, the results indicated that HFF was an independent risk factor for HRP (OR 1.93, P < 0.001). Additionally, HFF significantly correlated with coronary artery calcium score, hepatic CT attenuation, epicardial and pericoronary adipose tissue volume, and CT attenuation (all P < 0.001).

CONCLUSIONS

As a new imaging marker for the quantification of liver fat, HFF was independently associated with HRP.

Skeletal muscle fat quantification by dual-energy computed tomography in comparison with 3T MR imaging

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.

Quantification of Hepatic Fat Fraction in Patients With Nonalcoholic Fatty Liver Disease: Comparison of Multimaterial Decomposition Algorithm and Fat (Water)-Based Material Decomposition Algorithm Using Single-Source Dual-Energy Computed Tomography

METHODS

Hepatic fat fractions were quantified by noncontrast (HFFnon-CE) and contrast-enhanced single-source dual-energy computed tomography in arterial phase (HFFAP), portal venous phase (HFFPVP) and equilibrium phase (HFFEP) using MMD in 19 nonalcoholic fatty liver disease patients. The fat concentration was measured on fat (water)-based images. As the standard of reference, magnetic resonance iterative decomposition of water and fat with echo asymmetry and least-squares estimation-iron quantification images were reconstructed to obtain HFF (HFFIDEAL-IQ).

RESULTS

There was a strong correlation between HFFnon-CE, HFFAP, HFFPVP, HFFEP, fat concentration and HFFIDEAL-IQ (r = 0.943, 0.923, 0.942, 0.952, and 0.726) with HFFs having better correlation with HFFIDEAL-IQ. Hepatic fat fractions did not significantly differ across scanning phases. The HFFs of 3-phase contrast-enhanced computed tomography had a good consistency with HFFnon-CE.

CONCLUSIONS

Hepatic fat fraction using MMD has excellent correlation with that of magnetic resonance imaging, is independent of the computed tomography scanning phases, and may be used as a routine technique for quantitative assessment of HFF.

Dual-Energy Computed Tomography of the Liver: Uses in Clinical Practices and Applications

Dual-energy computed tomography (DECT) is an imaging technique based on data acquisition at two different energy settings. Recent advances in CT have allowed data acquisitions and simultaneous analyses of X-rays at two energy levels, and have resulted in novel developments in the field of abdominal imaging. The use of low and high X-ray tube voltages in DECT provide fused images that improve the detection of liver tumors owing to the higher contrast-to-noise ratio (CNR) of the tumor compared with the liver. The use of contrast agents in CT scanning improves image quality by enhancing the CNR and signal-to-noise ratio while reducing beam-hardening artifacts. DECT can improve detection and characterization of hepatic abnormalities, including mass lesions. The technique can also be used for the diagnosis of steatosis and iron overload. This article reviews and illustrates the different applications of DECT in liver imaging.

Dual-energy CT in diffuse liver disease: is there a role?

Dual-energy CT (DECT) can be defined as the use of two different energy levels to identify and quantify material composition. Since its inception, DECT has benefited from remarkable improvements in hardware and clinical applications. DECT enables accurate identification and quantification of multiple materials, including fat, iron, and iodine. As a consequence, multiple studies have investigated the potential role of DECT in the assessment of diffuse liver diseases. While this role is evolving, this article aims to review the most relevant literature on use of DECT for assessment of diffuse liver diseases. Moreover, the basic concepts on DECT techniques, types of image reconstruction, and DECT-dedicated software will be described, focusing on the areas that are most relevant for the evaluation of diffuse liver diseases. Also, we will review the evidence of added value of DECT in detection and assessment of hepatocellular carcinoma which is a known risk in patients with diffuse liver disease.

Dual-Energy Computed Tomography for Fat Quantification in the Liver and Bone Marrow: A Literature Review

Background With dual-energy computed tomography (DECT) it is possible to quantify certain elements and tissues by their specific attenuation, which is dependent on the X-ray spectrum. This systematic review provides an overview of the suitability of DECT for fat quantification in clinical diagnostics compared to established methods, such as histology, magnetic resonance imaging (MRI) and single-energy computed tomography (SECT).

Method Following a systematic literature search, studies which validated DECT fat quantification by other modalities were included. The methodological heterogeneity of all included studies was processed. The study results are presented and discussed according to the target organ and specifically for each modality of comparison.

Results Heterogeneity of the study methodology was high. The DECT data was generated by sequential CT scans, fast-kVp-switching DECT, or dual-source DECT. All included studies focused on the suitability of DECT for the diagnosis of hepatic steatosis and for the determination of the bone marrow fat percentage and the influence of bone marrow fat on the measurement of bone mineral density. Fat quantification in the liver and bone marrow by DECT showed valid results compared to histology, MRI chemical shift relaxometry, magnetic resonance spectroscopy, and SECT. For determination of hepatic steatosis in contrast-enhanced CT images, DECT was clearly superior to SECT. The measurement of bone marrow fat percentage via DECT enabled the bone mineral density quantification more reliably.

Conclusion DECT is an overall valid method for fat quantification in the liver and bone marrow. In contrast to SECT, it is especially advantageous to diagnose hepatic steatosis in contrast-enhanced CT examinations. In the bone marrow DECT fat quantification allows more valid quantification of bone mineral density than conventional methods. Complementary studies concerning DECT fat quantification by split-filter DECT or dual-layer spectral CT and further studies on other organ systems should be conducted.

Key points:

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Role of Magnetic Resonance Imaging in the Monitoring of Patients with Nonalcoholic Fatty Liver Disease: Comparison with Ultrasonography, Lipid Profile, and Body Mass Index

AIM

The aim of this study was to study the role of magnetic resonance imaging (MRI) in monitoring hepatic fat content in cases of nonalcoholic fatty liver disease (NAFLD).

MATERIALS AND METHODS

41 adults (mean age: 39 years, 22 males; 19 females) with NAFLD were included after obtaining approval from the institutional ethics committee. The baseline clinical (weight, body mass index [BMI]) and biochemical parameters, fatty liver grade on ultrasonography (USG), and hepatic fat signal fraction (FSF) using dual-echo chemical shift imaging and proton density fat fraction on magnetic resonance spectroscopy (MRS-PDFF) were assessed, before and after intervention (dietary and lifestyle changes and oral vitamin E for six months). They were categorized into Group A (good compliance to intervention) and Group B (poor compliance), and the clinical and imaging parameters were compared between them.

RESULTS

After intervention, Group A (n = 30) showed significant reduction in BMI (28.35 ± 3.25 to 27.14 ± 3.24 kg/m²; P < 0.001), hepatic FSF (19.30 ± 9.09% to 11.18 ± 7.61%; P < 0.05), and MRS-PDFF (18.79 ± 8.53% to 10.64 ± 6.66%). In Group B (n = 11), there was significant increase in BMI (28.85 ± 2.41 to 29.31 ± 2.57 kg/m²; P < 0.001), hepatic FSF (18.96 ± 9.79% to 21.48 ± 11.80%; P < 0.05), and reduction in high-density lipoproteins (P < 0.05). Although there was good correlation between USG and MRS in quantifying liver fat (r = 0.84-0.87; P < 0.001), USG was unable to detect <5.3% change in hepatic fat. There was poor correlation between lipid profile and MRS-PDFF. Change in body weight significantly correlated with change in hepatic fat content (r = 0.76; P < 0.001).

CONCLUSION

MRI is useful in accurately quantifying and in monitoring hepatic fat content and is better than clinical and biochemical parameters and USG.

Characterization of clear cell renal cell carcinoma and other renal tumors: evaluation of dual-energy CT using material-specific iodine and fat imaging

OBJECTIVE

This study aimed to assess material-specific iodine and fat images for diagnosis of clear cell renal cell carcinoma (cc-RCC) compared to papillary RCC (p-RCC) and other renal masses.

MATERIALS AND METHODS

With IRB approval, we identified histologically confirmed solid renal masses that underwent rapid-kVp-switch DECT between 2016 and 2018: 25 cc-RCC (7 low grade versus 18 high grade), 11 p-RCC, and 6 other tumors (2 clear cell papillary RCC, 2 chromophobe RCC, 1 oncocytoma, 1 renal angiomyomatous tumor). A blinded radiologist measured iodine and fat concentration on material-specific iodine-water and fat-water basis pair images. Comparisons were performed between groups using univariate analysis and diagnostic accuracy calculated by ROC.

RESULTS

Iodine concentration was higher in cc-RCC (6.14 ± 1.79 mg/mL) compared to p-RCC (1.40 ± 0.54 mg/mL, p < 0.001), but not compared to other tumors (5.0 ± 2.2 mg/mL, p = 0.370). Intratumoral fat was seen in 36.0% (9/25) cc-RCC (309.6 ± 234.3 mg/mL [71.1-762.3 ng/mL]), 9.1% (1/11) papillary RCC (97.11 mg/mL), and no other tumors (p = 0.036). Iodine concentration ≥ 3.99 mg/mL achieved AUC and sensitivity/specificity of 0.88 (CI 0.76-1.00) and 92.31%/82.40% to diagnose cc-RCC. To diagnose p-RCC, iodine concentration ≤ 2.5 mg/mL achieved AUC and sensitivity/specificity of 0.99 (0.98-1.00) and 100%/100%. The presence of intratumoral fat had AUC 0.64 (CI 0.53-0.75) and sensitivity/specificity of 34.6%/93.8% to diagnose cc-RCC. A logistic regression model combining iodine concentration and presence of fat increased AUC to 0.91 (CI 0.81-1.0) with sensitivity/specificity of 80.8%/93.8% to diagnose cc-RCC.

CONCLUSION

Iodine concentration values are highly accurate to differentiate clear cell RCC from papillary RCC; however, they overlap with other tumors. Fat-specific images may improve differentiation of clear cell RCC from other avidly enhancing tumors.

KEY POINTS

• Clear cell renal cell carcinoma (RCC) has significantly higher iodine concentration than papillary RCC, but there is an overlap in values comparing clear cell RCC to other renal tumors. • Iodine concentration ≤ 2.5 mg/mL is highly accurate to differentiate papillary RCC from clear cell RCC and other renal tumors. • The presence of microscopic fat on material-specific fat images was specific for clear cell RCC, helping to differentiate clear cell RCC from other avidly enhancing renal tumors.

Evaluation on Heterogeneity of Fatty Liver in Rats: A Multiparameter Quantitative Analysis by Dual Energy CT

RATIONALE AND OBJECTIVES

To investigate the value of using rapid kVp switching dual energy computed tomography (rsDECT) for the multi-parameter analysis of the heterogeneity of fatty liver in rat.

MATERIALS AND METHODS

Twenty-four male rats were randomly divided into experimental (n = 16) and control (n = 8) groups. Four rats in the experimental group and two in the control group were examined by rsDECT every 3 weeks starting from week 8. The liver fat contents (LFC) of the left and right liver lobes were measured on the fat(water)-based material decomposition images to calculate fat content and CT value of liver and spleen were measured on the 70keV monochromatic images to calculate the liver-to-spleen CT value ratio [(L/S)_70 keV] and difference at 70keV[(L-S)_70 keV], and the spectral curve slopes of the left and right liver lobes (Slope-L, Slope-R). Measurements were evaluated statistically.

RESULTS

The statistical analysis showed that the (L/S)_70 keV, (L-S)_70 keV, Slope and LFC in the different fatty liver groups were all significantly different (p < 0.05). Pearson correlation analysis results showed that the rsDECT results of the left and right liver lobes correlated with the fat percentage from pathological analysis (p < 0.05), with the left liver lobe having better correlation. Paired t-tests showed that the fat percentage of the left liver lobe was significantly higher than that of the right one, while (L/S)_70 keV, and (L-S)_70 keV were significantly lower. Diagnostic analysis using ROC curve showed that the areas under the curves with parameters of the left liver lobe were also greater than those of the right liver lobe.

CONCLUSION

rsDECT multi-parameter imaging could quantitatively evaluate the heterogeneity of fat deposition in the liver, providing valuable information for the accurate and effective assessment of the heterogeneity of fatty liver.

Quantification of contrast agent materials using a new image- domain multi material decomposition algorithm based on dual energy CT

OBJECTIVE

Dual-Energy CT (DECT) is an imaging modality in which the objects are scanned by two different energy spectra. Using these two measurements, two type of materials can be separated and density image pairs can be generated as well. Decomposing more than two materials is necessary in both clinical and industrial CT applications.

METHODS

In our MMD, barycentric coordinates were chosen using an innovative local clustering method. Local clustering increases precision in the barycentric coordinates assignment by decreasing search domain. Therefore the algorithm can be run in parallel. For optimizing coordinates selection, a fast bi-directional Hausdorff distance measurement is used. To deal with the significant obstacle of noise, we used Doubly Local Wiener Filter Directional Window (DLWFDW) algorithm.

RESULTS

Briefly, the proposed algorithm separates blood and fat ROIs with errors of less than 2 and 9 % respectively on the clinical images. Also, the ability to decompose different materials with different concentrations is evaluated employing the phantom data. The highest accuracy obtained in separating different materials with different concentrations was 93 % (for calcium plaque) and 97.1 % (for iodine contrast agent) respectively. The obtained results discussed in detail in the following results section.

CONCLUSION

In this study, we propose a new material decomposition algorithm. It improves the MMD work flow by employing tools which are easy to implement. Furthermore, in this study, an effort has been made to turn the MMD algorithm into a semi-automatic algorithm by employing clustering concept in material coordinate's assignment. The performance of the proposed method is comparable to existing methods from qualitative and quantitative aspects.

ADVANCES IN KNOWLEDGE

All decomposition methods have their own specific problems. Image- domain decomposition also has barriers and problems, including the need for a predetermined table for the separation of different materials with specified coordinates. In the present study, it attempts to solve this problem by using clustering methods and relying on the intervals between different materials in the attenuation domain.

Accurate effective atomic number determination with polychromatic grating-based phase-contrast computed tomography

The demand for quantitative medical imaging is increasing in the ongoing digitalization. Conventional computed tomography (CT) is energy-dependent and therefore of limited comparability. In contrast, dual-energy CT (DECT) allows for the determination of absolute image contrast quantities, namely the electron density and the effective atomic number, and is already established in clinical radiology and radiation therapy. Grating-based phase-contrast computed tomography (GBPC-CT) is an experimental X-ray technique that also allows for the measurement of the electron density and the effective atomic number. However, the determination of both quantities is challenging when dealing with polychromatic GBPC-CT setups. In this paper, we present how to calculate the effective atomic numbers with a polychromatic, laboratory GBPC-CT setup operating between 35 and 50\,kVp. First, we investigated the accuracy of the measurement of the attenuation coefficients and electron densities. For this, we performed a calibration using the concept of effective energy. With the reliable experimental quantitative values, we were able to evaluate the effective atomic numbers of the investigated materials using a method previously shown with monochromatic X-ray radiation. In detail, we first calculated the ratio of the electron density and attenuation coefficient, which were experimentally determined with our polychromatic GBPC-CT setup. Second, we compared this ratio with tabulated total attenuation cross sections from literature values to determine the effective atomic numbers. Thus, we were able to calculate two physical absolute quantities -- the electron density and effective atomic number -- that are in general independent of the specific experimental conditions like the X-ray beam spectrum or the setup design.

Imaging of nonalcoholic fatty liver disease and its clinical utility

The prevalence of nonalcoholic fatty liver disease has been continuously rising over the last three decades and is projected to become the most common indication for liver transplantation in the near future. Its pathophysiology and complex interplay with diabetes and the metabolic syndrome are not as yet fully understood despite growing scientific interest and research. Modern imaging techniques offer significant assistance in this field by enabling the study of the liver noninvasively and evaluation of the degree of both steatosis and fibrosis, and even in attempting to diagnose the presence of inflammation (steatohepatitis). The derived measurements are highly precise, accurate and reproducible, performing better than biopsy in terms of quantification. In this article, these imaging techniques are overviewed and their performance regarding diagnosis, stratification and monitoring are evaluated. Their expanding role both in the research arena and in clinical practice along with their limitations is also discussed.

Multimaterial Decomposition Algorithm for the Quantification of Liver Fat Content by Using Fast-Kilovolt-Peak Switching Dual-Energy CT: Clinical Evaluation

Purpose To assess the clinical accuracy and reproducibility of liver fat quantification with the multimaterial decomposition (MMD) algorithm, comparing the performance of MMD with that of magnetic resonance (MR) spectroscopy by using liver biopsy as the reference standard. Materials and Methods This prospective study was approved by the institutional ethics committee, and patients provided written informed consent. Thirty-three patients suspected of having hepatic steatosis underwent non-contrast material-enhanced and triple-phase dynamic contrast-enhanced dual-energy computed tomography (CT) (80 and 140 kVp) and single-voxel proton MR spectroscopy within 30 days before liver biopsy. Percentage fat volume fraction (FVF) images were generated by using the MMD algorithm on dual-energy CT data to measure hepatic fat content. FVFs determined by using dual-energy CT and percentage fat fractions (FFs) determined by using MR spectroscopy were compared with histologic steatosis grade (0-3, as defined by the nonalcoholic fatty liver disease activity score system) by using Jonckheere-Terpstra trend tests and were compared with each other by using Bland-Altman analysis. Real non-contrast-enhanced FVFs were compared with triple-phase contrast-enhanced FVFs to determine the reproducibility of MMD by using Bland-Altman analyses. Results Both dual-energy CT FVF and MR spectroscopy FF increased with increasing histologic steatosis grade (trend test, P < .001 for each). The Bland-Altman plot of dual-energy CT FVF and MR spectroscopy FF revealed a proportional bias, as indicated by the significant positive slope of the line regressing the difference on the average (P < .001). The 95% limits of agreement for the differences between real non-contrast-enhanced and contrast-enhanced FVFs were not greater than about 2%. Conclusion The MMD algorithm quantifying hepatic fat in dual-energy CT images is accurate and reproducible across imaging phases. ^© RSNA, 2017 Online supplemental material is available for this article.

White Paper of the Society of Computed Body Tomography and Magnetic Resonance on Dual-Energy CT, Part 4: Abdominal and Pelvic Applications

This is the fourth of a series of 4 white papers that represent expert consensus documents developed by the Society of Computed Body Tomography and Magnetic Resonance through its task force on dual-energy computed tomography. This article, part 4, discusses DECT for abdominal and pelvic applications and, at the end of each, will offer our consensus opinions on the current clinical utility of the application and opportunities for further research.

Quantitative Imaging Biomarkers of NAFLD

Conventional imaging modalities, including ultrasonography (US), computed tomography (CT), and magnetic resonance (MR), play an important role in the diagnosis and management of patients with nonalcoholic fatty liver disease (NAFLD) by allowing noninvasive diagnosis of hepatic steatosis. However, conventional imaging modalities are limited as biomarkers of NAFLD for various reasons. Multi-parametric quantitative MRI techniques overcome many of the shortcomings of conventional imaging and allow comprehensive and objective evaluation of NAFLD. MRI can provide unconfounded biomarkers of hepatic fat, iron, and fibrosis in a single examination-a virtual biopsy has become a clinical reality. In this article, we will review the utility and limitation of conventional US, CT, and MR imaging for the diagnosis NAFLD. Recent advances in imaging biomarkers of NAFLD are also discussed with an emphasis in multi-parametric quantitative MRI.