Effect of radiation dose on quantification of the liver iron and fat fractions using dual-energy computed tomography and material decomposition

Introduction

Quantitative measurements of liver fat and iron deposition can be used to evaluate fatty liver and chronic liver diseases, such as hepatic haematosis, hepatic leukaemia, viral hepatitis and cirrhosis, indicating not only the progression of the disease but also the clinical efficacy of treatment (1,2). However, in many cases, hepatic iron deposition and hepatic fat deposition coexist. Specifically, iron accumulation in the liver is frequently accompanied by varying degrees of steatosis in chronic liver diseases. Moreover, alcoholic, non-alcoholic, and drug-induced fatty liver diseases are commonly associated with varying levels of hepatic iron deposition (3). Traditional single-energy computed tomography (CT) reflects liver iron deposition by an increase in the liver parenchyma CT value above 72 Hounsfield units while fat deposition causes a reduction in the CT density (4). Distinguishing specific material components with the same CT value and determining the concentration of a variety of mixed material components, such as the iron and fat concentrations, is crucial for the diagnosis and clinical evaluation of diseases (5). Dual-energy CT (DECT)-based material decomposition technology has promising applications in measuring liver iron and fat to improve diagnostic accuracy (6).

DECT offers advantages over conventional CT by leveraging material-specific differences in X-ray absorption at distinct energy levels. This technique enables quantitative material differentiation based on variations in linear attenuation coefficients between low- and high-energy spectra (7). Through rapid tube voltage switching between 80 and 140 kVp, DECT maximizes material contrast by exploiting the characteristic energy-dependent attenuation patterns of different substances (8). The material differentiation capability follows two fundamental principles: (I) materials with higher atomic numbers exhibit greater CT value differences between energy levels; and (II) substances with lower fat density demonstrate more pronounced energy-specific attenuation variations (9). The underlying physical principle states that the linear attenuation coefficient of any material can be mathematically represented as a weighted combination of two basis materials. While initially developed for material separation applications, this technique has evolved to enable precise quantification of material composition in clinical investigations (10).

DECT has become well-established in clinical practice over the past decade. While radiation doses in modern DECT have significantly decreased, ongoing optimization remains crucial for specific clinical scenarios—particularly when serial quantitative assessments are required. In this study we applied DECT material separation technology to scan liver iron and fat models to examine the influence of the radiation dose on the quantification of liver iron and fat deposition with the aim of laying a foundation for the future use of low-dose CT in this situation.

Methods

In vitro models

The liver was dissected and isolated from 20 normal Sprague-Dawley rats which were provided by the Animal Experiment Center of the Shenzhen Peking University-Hong Kong University of Science and Technology Medical Center, under license number SYXK (Yue) 2020-0106.

Three-month-old adult rats were housed and cared for at the center in accordance with laboratory regulations. They were kept in a temperature- and humidity-controlled environment, fed at regular intervals with sufficient water provided, and their cages were routinely cleaned and disinfected, with bedding replaced periodically.

Anesthesia was administered via intravenous injection of sodium pentobarbital at a dose of 100 mg/kg. Euthanasia was carried out using an overdose of the anesthetic to ensure a painless and rapid death. The study was approved by the Animal Experiment Center of Peking University Shenzhen Hospital. Dissected liver samples were rinsed, cut up and packed into 4 mL polyvinyl chloride (PVC) tubes (inner diameter, 10 mm) and placed in a homogenizer. Homogenization was repeated until the fresh liver tissue was completely homogenized and ready for use.

Preparation of liver iron deposition model: iron dextran (Pharmacosmos, Holbaek, Denmark; specification, 2 mL: 100 mg of Fe) was prepared in distilled water atconcentrations of 25.000, 12.500, 6.250, 3.125, 1.563, and 0 mg/mL; 2 mL of each of the solutions was mixed with 2 mL of the liver tissue slurry and placed in a 4 mL PVC tube diluting the above concentrations by 50%. The samples were oscillated using a suspension oscillator until a homogeneous appearance was observed. Samples remaining homogeneous for more than 6 hours without stratification, were considered successfully prepared.

Preparation of liver fat deposition model: five 4 mL PVC tubes containing 0, 1.6, 2.8, 3.6, and 4.0 mL of normal rat liver tissue slurry were prepared and pure triglycerides (olive oil, Olivoila, fat content: 100.0 g/100 g, density: 0.910 g/mL) were added until the overall volume of each tube was 4 mL; yielding fat concentrations of 0.910, 0.546, 0.273, 0.091, and 0 g/mL, respectively. The samples were oscillated using a suspension oscillator until a homogeneous appearance was observed. Samples kept for more than 6 hours without stratification were considered successful.

The prepared PVC tubes containing the samples were then arranged and immersed in a large cylindrical water phantom (diameter: 200 mm) to simulate the scattering environment and attenuation of an adult human abdomen during CT scanning.

The study was approved by the Research Animal Resource Center of Peking University Shenzhen Hospital (No. SYXK2015-0106), in compliance with institutional and national guidelines for the care and use of laboratory animals.

Image acquisition

All samples were scanned using a 256-row CT scanner (Revolution CT, GE HealthCare, Waukesha, WI, USA), as follows. Iron dextran mixtures were scanned in GSI mode with rapid tube voltage switching between 80 and 140 kVp and tube currents of 200, 320, and 485 mA, respectively. The corresponding CT dose indexes (CTDIvols) were 4.88, 8.21, and 12.64 mGy. Changing the tube current equated to changing the radiation dose when the tube voltage, pitch and scanning range remained unchanged. The other parameters were as follows: rev, 0.5 r/s; field of view, 25 mm; reconstruction thickness/gap, 1.25 mm; pitch, 0.984 mm. The reconstruction function of STND was employed, and the adaptive statistical iterative reconstruction-V was 50%. Thus, in total, 33 groups of images were obtained after scanning the liver iron and liver fat models at different tube currents.

Data measurement

All raw data were transmitted to an ADW 4.6 workstation (GE HealthCare) and processed with GSI software for material decomposition and characterization. The iron concentration (on iron-water bases) and the fat concentration (on fat-water bases) were determined with consistent regions of interest placed in the centre of the tube with a diameter of 6 mm and an area of 28.26 mm². For each sample, measurements were recorded in three different regions and the average value was taken as the virtual iron concentration (VIC) or virtual fat concentration (VFC) and included in the database.

Statistical analysis

Bland-Altman analysis and the Kolmogorov-Smirnov test were used to estimate data normality using MedCalc (Ver. 18.2.1, Belgium) and SPSS 20.0 software (Chicago, IL, USA). The correlation between the liver iron concentrations (LICs) and each VIC was assessed using Pearson’s correlation analysis, as was the correlation between the liver fat concentrations (LFCs) and each VFC. One-way analysis of variance was performed to determine differences among the three VICs and three VFCs. P values less than 0.05 were considered statistically significant.

Results

The raw images were reconstructed with a thickness/gap of 1.25 mm and a pitch of 0.984 mm and processed with GSI Viewer (Figure 1A-1F). Bland-Altman analysis showed good consistency of the three scanning protocols with the different CTDIvols (Figure 2A-2F).

Figure 1 Iron-water-base images of the rat liver iron model and fat-water-base images of the rat liver fat model at different radiation doses. (A-C) Iron-water-base images of the rat liver iron model at radiation doses of 4.88, 8.21 and 12.64 mGy, respectively, as shown by GSI Viewer. The normal rat liver homogenate of the six samples from left to right (clockwise) had iron concentrations of 25.000, 12.500, 6.250, 3.125, 1.563, and 0 mg/mL, respectively: image intensity decreased as the concentration decreased. (D-F) Fat-water-base images of the rat liver fat model at radiation doses of 4.88, 8.21 and 12.64 mGy, respectively, as shown by GSI Viewer. The six samples (from left to right) contained fat concentrations of 0, 0.091, 0.273, 0.546, 0.910, and 0 g/mL, respectively. This layout included a duplicate control (0 g/mL) to ensure phantom symmetry and minimize imaging artifacts. The image intensity increased as the fat concentration increased.

Figure 2 Bland-Altman analysis results. (A) The mean VFC at 4.88 and 8.21 mGy. (B) The mean VFC at 4.88 and 12.64 mGy. (C) The mean VFC at 8.21 and 12.64 mGy. (D) The mean VIC at 4.88 and 8.21 mGy. (E) The mean VIC at 4.88 and 12.64 mGy. (F) The mean VIC at 8.21 and 12.64 mGy. The X-axis represents the mean virtual concentrations (of fat and iron) measured at different CT radiation doses, while the Y-axis represents differences from the mean. Although there was good consistency among the three scanning protocols and different doses, the differences inevitably increased as the fat or iron concentration increased. CT, computed tomography; SD, standard deviation; VFC, virtual fat concentration; VIC, virtual iron concentration.

In the liver iron model, the correlation coefficients between the LIC and each VIC were 0.999, 1.000, and 0.999, respectively. Pearson’s correlation analysis revealed a significant strong linear correlation. Analysis of variance showed no significant differences in the VIC between the three tube currents (P>0.05). However, VIC was significantly, consistently lower than LIC (P<0.05). In the liver fat model, the correlation coefficients between the LFC and each VFC were all 0.999. Pearson’s correlation analysis revealed a significant linear correlation. There were no significant differences in the VFC between the three tube currents (P>0.05) (Tables 1,2 and Figure 3A,3B). However, VFC was significantly, consistently higher than LFC (P<0.05) (Tables 1,2 and Figure 3A,3B).

Figure 3 The virtual and actual concentrations of rat liver iron and fat at different CT radiation doses. (A) VIC quantification of different LICs. (B) VFC quantification of different LFCs showed clear close positive relationships at different CT radiation doses. CT, computed tomography; LFC, liver fat concentration; LIC, liver iron concentration; VFC, virtual fat concentration; VIC, virtual iron concentration.

Discussion

In the present study, we used a DECT material separation technique to quantitatively determine liver iron and fat concentrations. We found that the VIC was systematically lower than the LIC, while the VFC was systematically higher than the LFC. The difference between the virtual and actual concentrations may be because DECT is not as accurate in the quantitative measurement of low levels of iron or high levels of fat, related to the different spectral behaviour of iron and fat and the partial volume averaging effects (5,10). Moreover, inequivalent image noise at different concentrations may also have played a role.

Goldberg et al. (11) performed DECT (80 kVp/120 kVp) scanning of the liver in a haemochromatosis dog model. They designed a gradient concentration series of iron dextran phantoms to obtain a linear relationship between the iron concentration and the DECT difference, enabling measurement of dog liver iron content with good correlation with the liver biopsy values (r=0.99). Peng et al. (6) scanned liver tissue phantoms mixed with different proportions of fat and iron solutions [light (10%), medium (30%), and heavy (50%) fat content; iron content of 20, 60, 100, 200, 400, and 800 µmol/g] using DECT and applied a material separation method to determine the iron content. The results showed that DECT could accurately measure the liver iron content while conventional CT could not.

In clinical practice, base materials are selected on the basis of a large difference in atomic number or materials are paired according to the purpose of diagnosis. In the liver iron model, the equivalent attenuation of each pixel was produced by the combination of two base materials: iron and water (7,9). Additionally, the attenuation of each pixel in the liver fat model was produced by the combination of two base materials: fat and water (9). To the best of our knowledge, the CTDI of conventional abdominal CT ranges from 11.6 to 20.2 mGy (12,13). In our study the CDTIs were relatively low: 4.88, 8.21, and 12.64 mGy. The results showed no significant differences between the VICs and VFCs measured at different CTDIvols and therefore support the use of low-dose CT.

Strengths of our study include the range of concentrations of iron and fat studied and the robustness of the scanning protocol. Potential weaknesses include the lack of alternative base materials, the lack of comparison of model liver CT values with animal in vivo values (including iron and fat loaded rat models) and attempts to modulate signal noise. In addition, most of the iron and fat concentrations tested in this study were well above normal clinical values, and it is unclear whether the method is equally effective at normal values. Further intact livers studies are required before pilot human studies are undertaken. While this study focused on dual-material decomposition, future research will implement three-material decomposition algorithms to address the clinically critical scenario of concurrent iron and fat deposition. Furthermore, while our abdominal-sized phantom ensured clinically representative scanning conditions, future studies should incorporate quantitative measurements of image noise across different radiation doses. This will provide a more comprehensive assessment of the trade-offs between dose reduction and image quality in DECT quantification.

Conclusions

This study showed no effect on quantification of the liver iron or fat content using different DECT radiation doses, employing material decomposition, laying the foundation for use of low-dose CT for this purpose. Quantification of the liver iron content is important for determining the severity of liver haemochromatosis and long-term transfusion iron overload, as well as choosing a treatment option and evaluating the efficacy of iron therapy. Additionally, quantification of the liver fat content is widely applicable in lipid metabolic diseases, and the principles may be applicable in various other tissues, including in brain, breast, and bone.

Acknowledgments

None.

Footnote

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-714/dss

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-714/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was approved by the Research Animal Resource Center of Peking University Shenzhen Hospital (No. SYXK2015-0106), in compliance with institutional and national guidelines for the care and use of laboratory animals.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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