Impact of iron deposit on the accuracy of quantifying liver fat fraction using multi-material decomposition algorithm in dual-energy spectral computed tomography

Using Dual-source Photon-counting Detector CT to Simultaneously Quantify Fat and Iron Content: A Phantom Study

RATIONALE AND OBJECTIVES

To evaluate the feasibility of using photon-counting detector computed tomography (PCD CT) to simultaneously quantify fat and iron content MATERIALS AND METHODS: Phantoms with pure fat, pure iron and fat-iron deposition were scanned by two tube voltages (120 and 140 kV) and two image quality (IQ) settings (80 and 145). Using an iron-specific three-material decomposition algorithm, virtual noniron (VNI) and virtual iron content (VIC) images were generated at quantum iterative reconstruction (QIR) strength levels 1-4.

RESULTS

Significant linear correlations were observed between known fat content (FC) and VNI for pure fat phantoms (r = 0.981-0.999, p < 0.001) and between known iron content (IC) and VIC for pure iron phantoms (r = 0.897-0.975, p < 0.001). In fat-iron phantoms, the measurement for fat content of 5-30% demonstrated good linearity between FC and VNI (r = 0.919-0.990, p < 0.001), and VNI were not affected by 75, 150, and 225 µmol/g iron overload (p = 0.174-0.519). The measurement for iron demonstrated a linear range of 75-225 µmol/g between IC and VIC (r = 0.961-0.994, p < 0.001) and VIC was not confounded by the coexisting 5%, 20%, and 30% fat deposition (p = 0.943-0.999). The Bland-Altman of fat and iron measurements were not significantly different at varying tube voltages and IQ settings (all p > 0.05). No significant difference in VNI and VIC at QIR 1-4.

CONCLUSION

PCD CT can accurately and simultaneously quantify fat and iron, including scan parameters with lower radiation dose.

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.

Photon-counting CT using multi-material decomposition algorithm enables fat quantification in the presence of iron deposits

PURPOSE

A new generation of CT detectors were recently developed with the ability to measure individual photon's energy and thus provide spectral information. The aim of this work was to assess the performance of simultaneous fat and iron quantification using a clinical photon-counting CT (PCCT) and its comparison to dual-energy CT (DECT), MRS and MRI at 3 T.

METHODS

Two 3D printed cylindrical phantoms with 32 samples (n = 12 fat fractions between 0 % and 100 %, n = 20 with mixtures of fat and iron) were scanned with PCCT and DECT scanners for comparison. A three-material decomposition approach was used to estimate the volume fractions of fat (FF), iron and soft tissue. The same phantoms were examined by MRI (6-echo DIXON, a.k.a. Q-DIXON) and MRS (multi-echo STEAM, a.k.a. HISTO) at 3 T for comparison.

RESULTS

PCCT, DECT, MRI and MRS computed FFs showed correlation with reference fat fraction values in samples with no iron (r > 0.98). PCCT decomposition showed slightly weaker correlation with FFref in samples with added iron (r = 0.586) compared to MRI (r = 0.673) and MRS (r = 0.716) methods. On the other hand, it showed no systematic over- or underestimation. Surprisingly, DECT decomposition-derived FF showed strongest correlation (r = 0.758) in these samples, however systematic overestimation was observed. FF values computed by three-material PCCT decomposition, DECT decomposition, MRI and MRS were unaffected by iron concentration.

CONCLUSIONS

This in-vitro study shows for the first time that photon-counting computed tomography may be used for quantification of fat content in the presence of iron deposits.

Narrative review of magnetic resonance imaging in quantifying liver iron load

Objective

To summarize the research progress of magnetic resonance imaging (MRI) in quantifying liver iron load.

Methods

To summarize the current status and progress of MRI technology in the quantitative study of liver iron load through reviewing the relevant literature at home and abroad.

Results

Different MRI sequence examination techniques have formed a series of non-invasive methods for the examination of liver iron load. These techniques have important clinical significance in the imaging diagnosis of liver iron load. So far, the main MRI methods used to assess liver iron load are: signal intensity measurement method (signal intensity, SI) [signal intensity ratio (SIR) and difference in in-phase and out-of-phase signal intensity], T2/R2 measurement (such as FerriScan technique), ultra-short echo time (UTE) imaging technique, and susceptibility weighted imaging (including conventional susceptibility weighted imaging) (SWI), quantitative susceptibility mapping (QSM), T2*/R2* measurement, Dixon and its derivative techniques.

Conclusion

MRI has become the first choice for the non-invasive examination of liver iron overload, and it is helpful to improve the early detection of liver injury, liver fibrosis, liver cirrhosis and liver cancer caused by liver iron overload.

Impact of iron deposit on the accuracy of quantifying liver fat fraction using multi-material decomposition algorithm in dual-energy spectral computed tomography

Objectives

To investigate the accuracy of using multi‐material decomposition (MMD) algorithm in dual‐energy spectral computed tomography (CT) for quantifying fat fraction (FF) in the presence of iron.

Materials

Nine tubes with various proportions of fat and iron were prepared. FF were divided into three levels (10%, 20%, and 30%), recorded as references (FF_ref). Iron concentrations (in mg/100 g) were divided into three ranges (25.25–25.97, 50.38–51.55 and 75.57–77.72). The nine‐tube phantom underwent dual‐energy CT and MR. CT attenuation was measured and FF were determined using MMD in CT (FF_CT) and Iterative Decomposition of water and fat with Echo Asymmetry and Least squares estimation (IDEAL‐IQ) in MR (FF_MR) for each tube. Statistical analyses used were: Spearman rank correlation for correlations between FF_ref and CT attenuation, FF_CT, and FF_MR; one‐way ANOVA, and one‐sample t‐test for the differences between FF_CT and FF_ref and between FF_MR and FF_ref. A multivariate linear regression model was established to analyze the differences between the corresponding values with different iron concentrations under the same FF_ref.

Results

Fat fraction on CT (FFCT) and FF_MR were positively correlated with FF_ref (all p < 0.001), while the CT attenuation was negatively correlated with FF_ref in the three iron concentration ranges. For a given FF_ref, FF_CT decreased and FF_MR increased as the iron concentration increased. The mean difference between FF_CT and FF_ref over the nine tube measurements was 0.25 ± 2.45%, 5.7% lower the 5.98 ± 3.33% value between FF_MR and FF_ref (F = 310.017, p < 0.01).

Conclusion

The phantom results indicate that MMD in dual‐energy CT can directly quantify volumetric FF and is less affected by iron concentration than MR IDEAL‐IQ method.