Liver fat volume fraction assessment in medium and large anthropomorphic phantoms–dual energy virtual monochromatic imaging versus single-energy CT

Background

Fat volume fraction (FVF) is an important biomarker for non-alcoholic fatty liver disease. However, current CT-based FVF quantification methods lack sufficient accuracy, particularly at lower FVF values.

Purpose

We aimed to analyze the relationship between FVF and Hounsfield units (HU) in unenhanced fatty lesions and identify optimal settings to minimize FVF quantification errors by comparing virtual monochromatic imaging (VMI) from dual-energy CT (DECT) with single-energy CT (SECT) across different patient sizes.

Methods

Six fatty lesions (5%–40% FVF) were embedded in an anthropomorphic liver within a medium-sized abdomen-pelvis phantom (25 cm×32.5 cm). DECT acquisitions were conducted at CTDI_vol of 14.5 mGy using both fast kV-switching (FKVS) and dual-source CT (DSCT), producing VMI images from 40 to 140 keV. HU values were measured across all VMI energies for each lesion in three repeated acquisitions. The measured HU values were correlated with the known FVF. For comparison, repeated single-energy images were also acquired at 120 kV with the same dose, and a similar analysis was performed. To study the impact of the patient size, a large phantom (31 cm×39 cm) consisted of an additional soft-tissue equivalent layer to the medium-size phantom was scanned on the FKVS CT with noise matched CTDI_vol = 21 mGy using DECT and SECT.

Results

It was found that the validity of the linear relation for FVF-HU is x-ray beam energy and VMI energy-dependent. For the medium-sized phantom, the root-mean-square (RMS) FVF estimation errors from the linearity assumption were slightly higher than 9% with the SECT at 120 kV and CTDI_vol = 14.5 mGy on both units, and the corresponding maximum individual FVF errors were 16.5%–23% at FVF ≤ 10%. With the FKVS CT, four VMI settings resulted in lower RMS errors than SECT with the smallest RMS. error (∼5%) at 90 and 100 keV, where the maximum individual FVF errors were approximately 10% occurred at FVF ≤ 10%. For the DSCT with spectra 80/Sn 150 kV, five VMI settings resulted in smaller RMS errors than 9%, with the lowest RMS error (∼2.5%) at 120 and 130 keV, where the maximum individual FVF errors ≤4.4% occurred at 30% FVF, respectively. For the large phantom, however, the linear model at single energy of 140 kV resulted in the lowest RMS error of 9.2% with the maximum individual FVF error of 20% at 5% FVF, while the smallest RMS error from VMI was 15.3% with the maximum individual FVF error of 27% at 10% FVF.

Conclusions

The validity of linear assumption about the FVF-HU relationship was found to depend on x-ray beam energy, VMI energy, and patient size, which impacts the FVF assessment accuracy. The settings resulting in best accuracy were identified. For the medium-sized phantom, the use of VMI with wider dual energy spectral separation showed the RMS errors ∼2.5%. For the large phantom, the use of single energy at 140 kV on the FKVS CT showed the best estimate, albeit with a RMS error of 9.2%.