Simultaneous hepatic iron and fat quantification with dual-energy CT in a rabbit model of coexisting iron and fat

Yun Peng; Jing Ye; Chang Liu; Hongru Jia; Jun Sun; Jun Ling; Martin Prince; Chang Li; Xianfu Luo

doi:10.21037/qims-20-902

Simultaneous hepatic iron and fat quantification with dual-energy CT in a rabbit model of coexisting iron and fat

Quant Imaging Med Surg. 2021 May;11(5):2001-2012. doi: 10.21037/qims-20-902.

Authors

Yun Peng¹, Jing Ye¹, Chang Liu¹, Hongru Jia¹, Jun Sun¹, Jun Ling¹, Martin Prince², Chang Li³, Xianfu Luo¹

Affiliations

¹ Department of Radiology, Northern Jiangsu People's Hospital, Clinical Medical College, Yangzhou University, Yangzhou, China.
² Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA.
³ Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.

Abstract

Background: Liver iron and fat are often co-deposited, synergistically aggravating the progression of chronic liver disease. Accurate determination of liver iron and fat content is helpful for patient management. To assess the accuracy of hepatic iron/fat decomposition using dual-energy computed tomography (DECT) for simultaneously quantifying hepatic iron and fat when both are present.

Methods: Sixty-eight New Zealand rabbits on a high-fat/cholesterol diet plus iron injections were used to establish a model of coexisting hepatic iron/fat. Abdominal imaging was performed using dual-source DECT. The iron and fat fractions (Iron-_CT and Fat-_CT, respectively) were calculated using a 3-material decomposition algorithm. The spectroscopic liver iron concentration (LIC) grading (normal, mild, moderate, severe, and massive iron overload) and the histopathological fat fraction (Fat-ref) grading (normal, mild, moderate, severe steatosis) were used as references. Correlations between the DECT parameters and the references were analyzed. Hepatic iron/fat quantification equations were established and validated. Analysis of covariance was used to assess the influence of fat on iron measurements and vice versa.

Results: Iron-_CT highly correlated with LIC (r=0.94, P<0.001), and Fat-_CT highly correlated with Fat-_ref (r=0.88, P<0.001). Both the Iron-_CT- and Fat-_CT-derived LIC and fat fraction showed good agreement with spectroscopy/histology. The linear relationship between Iron-_CT and spectroscopic LIC was not affected by the grade of hepatic fat (F=1.93, P=0.16). The linear relationship between Fat-_CT and Fat-_ref was unaffected by hepatic iron grades from normal to severe (F=0.18, P=0.91). However, with massive iron overload [>15.0 mg Fe/g (270 µmol/g)] the regression began to deviate, causing fat underestimation (F=5.50, P=0.04).

Conclusions: Our DECT-based iron/fat decomposition algorithm accurately measured hepatic iron and fat when both were present in a rabbit model. Hepatic fat may be underestimated when there is massive iron overload.

Keywords: Dual-energy scanned projection radiography; X-ray computed tomography; fatty liver; iron overload.