European Radiology

, Volume 28, Issue 5, pp 2003–2012 | Cite as

Validation of goose liver fat measurement by QCT and CSE-MRI with biochemical extraction and pathology as reference

  • Li Xu
  • Yangyang Duanmu
  • Glen M. Blake
  • Chenxin Zhang
  • Yong Zhang
  • Keenan Brown
  • Xiaoqi Wang
  • Peng Wang
  • Xingang Zhou
  • Manling Zhang
  • Chao Wang
  • Zhe Guo
  • Giuseppe Guglielmi
  • Xiaoguang ChengEmail author



This study aimed to validate the accuracy and reliability of quantitative computed tomography (QCT) and chemical shift encoded magnetic resonance imaging (CSE-MRI) to assess hepatic steatosis.


Twenty-two geese with a wide range of hepatic steatosis were collected. After QCT and CSE-MRI examinations, the liver of each goose was removed and samples were taken from the left lobe, upper and lower half of the right lobe for biochemical measurement and histology. Fat percentages by QCT and proton density fat fraction by MRI (MRI-PDFF) were measured within the sample regions of biochemical measurement and histology. The accuracy of QCT and MR measurements were assessed through Spearman correlation coefficients (r) and Passing and Bablok regression equations using biochemical measurement as the "gold standard".


Both QCT and MRI correlated highly with chemical extraction [r = 0.922 (p < 0.001) and r = 0.949 (p < 0.001) respectively]. Chemically extracted triglyceride was accurately predicted by both QCT liver fat percentages (Y = 0.6 + 0.866 × X) and by MRI-PDFF (Y = -1.8 + 0.773 × X).


QCT and CSE-MRI measurements of goose liver fat were accurate and reliable compared with biochemical measurement.

Key Points

QCT and CSE-MRI can measure liver fat content accurately and reliably

Histological grading of hepatic steatosis has larger sampling variability

QCT and CSE-MRI have potential in the clinical setting


Hepatic steatosis Quantitative computed tomography Chemical shift encoded magnetic resonance imaging Proton density fat fraction Hepatic triglyceride analysis 



Non-Alcoholic Fatty Liver Disease


Quantitative Computed Tomography


Bone Mineral Density


peripheral Quantitative Computed Tomography


Proton Density Fat Fraction


Chemical Shift Encoded Magnetic Resonance Imaging


modified Dixon



We thank our study participants for contributing with their time and efforts. We wish to thank Mindways Software and Philips Healthcare for their technical support. We also thank Chao Wang for his professional suggestions on statistics. The authors acknowledge the support of National Natural Science Foundation of China (81401407).


This study has received funding by National Natural Science Foundation of China (81401407).

Compliance with ethical standards


The scientific guarantor of this publication is Li Xu.

Conflict of interest

The authors of this manuscript declare relationships with the following companies. Keenan Brown is the employee and stock owner of Mindways Software. There was no involvement by this company in the design, execution, analysis, or publication of this manuscript. Xiaoqi Wang is employed by Philips Healthcare. There was no involvement by this company in the design, execution, analysis, or publication of this manuscript. All the other authors declare no potential conflict of interest.

Statistics and biometry

Chao Wang kindly provided statistical advice for this manuscript.

One of the authors has significant statistical expertise.

Ethical approval

Institutional Review Board approval was obtained.

Approval from the institutional animal care committee of Beijing Jishuitan Hospital was obtained.


• prospective

• diagnostic or prognostic study

• performed at one institution


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Copyright information

© European Society of Radiology 2017

Authors and Affiliations

  • Li Xu
    • 1
  • Yangyang Duanmu
    • 1
  • Glen M. Blake
    • 2
  • Chenxin Zhang
    • 1
  • Yong Zhang
    • 1
  • Keenan Brown
    • 3
  • Xiaoqi Wang
    • 4
  • Peng Wang
    • 5
  • Xingang Zhou
    • 5
  • Manling Zhang
    • 6
  • Chao Wang
    • 7
  • Zhe Guo
    • 1
  • Giuseppe Guglielmi
    • 8
  • Xiaoguang Cheng
    • 1
    Email author
  1. 1.Department of RadiologyBeijing Jishuitan HospitalBeijingChina
  2. 2.Biomedical Engineering DepartmentKing’s College LondonLondonUK
  3. 3.Mindways SoftwareAustinUSA
  4. 4.Philips HealthcareBeijingChina
  5. 5.Pathology DepartmentCapital Medical University Affiliated Beijing Ditan HospitalBeijingChina
  6. 6.China National Food & Safety Supervision and Inspection CentreBeijingChina
  7. 7.Statistics DepartmentBeijing Jishuitan HospitalBeijingChina
  8. 8.Department of RadiologyScientific Institute HospitalSan Giovanni RotondoItaly

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