Ruiyang Zhao1,2, Diego Hernando1,2, David T Harris1, Louis A Hinshaw3, Ke Li1,2, Lakshmi Ananthakrishnan4, Mustafa R Bashir5,6,7, Xinhui Duan4, Mounes Aliyari Ghasabeh8, Ihab R Kamel8, Carolyn Lowry5, Mahadevappa Mahesh8, Daniele Marin5, Jessica Miller9, Perry J Pickhardt1, Jean Shaffer5,6, Takeshi Yokoo4, Jean H Brittain10, Scott B Reeder1,2,3,11,12. 1. Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA. 2. Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA. 3. Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA. 4. Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA. 5. Department of Radiology, Duke University, Durham, NC, USA. 6. Center for Advanced Magnetic Resonance Development, Duke University, Durham, NC, USA. 7. Division of Gastroenterology, Department of Medicine, Duke University, Durham, NC, USA. 8. Department of Radiology, Johns Hopkins University, Baltimore, MD, USA. 9. Department of Human Oncology, University of Wisconsin-Madison, Madison, WI, USA. 10. Calimetrix, LLC, Madison, WI, USA. 11. Department of Medicine, University of Wisconsin, Madison, WI, USA. 12. Department of Emergency Medicine, University of Wisconsin-Madison, Madison, WI, USA.
Abstract
PURPOSE: Chemical shift-encoded magnetic resonance imaging enables accurate quantification of liver fat content though estimation of proton density fat-fraction (PDFF). Computed tomography (CT) is capable of quantifying fat, based on decreased attenuation with increased fat concentration. Current quantitative fat phantoms do not accurately mimic the CT number of human liver. The purpose of this work was to develop and validate an optimized phantom that simultaneously mimics the MRI and CT signals of fatty liver. METHODS: An agar-based phantom containing 12 vials doped with iodinated contrast, and with a granular range of fat fractions was designed and constructed within a novel CT and MR compatible spherical housing design. A four-site, three-vendor validation study was performed. MRI (1.5T and 3T) and CT images were obtained using each vendor's PDFF and CT reconstruction, respectively. An ROI centered in each vial was placed to measure MRI-PDFF (%) and CT number (HU). Mixed-effects model, linear regression, and Bland-Altman analysis were used for statistical analysis. RESULTS: MRI-PDFF agreed closely with nominal PDFF values across both field strengths and all MRI vendors. A linear relationship (slope = -0.54 ± 0.01%/HU, intercept = 37.15 ± 0.03%) with an R2 of 0.999 was observed between MRI-PDFF and CT number, replicating established in vivo signal behavior. Excellent test-retest repeatability across vendors (MRI: mean = -0.04%, 95% limits of agreement = [-0.24%, 0.16%]; CT: mean = 0.16 HU, 95% limits of agreement = [-0.15HU, 0.47HU]) and good reproducibility using GE scanners (MRI: mean = -0.21%, 95% limits of agreement = [-1.47%, 1.06%]; CT: mean = -0.18HU, 95% limits of agreement = [-1.96HU, 1.6HU]) were demonstrated. CONCLUSIONS: The proposed fat phantom successfully mimicked quantitative liver signal for both MRI and CT. The proposed fat phantom in this study may facilitate broader application and harmonization of liver fat quantification techniques using MRI and CT across institutions, vendors and imaging platforms.
PURPOSE: Chemical shift-encoded magnetic resonance imaging enables accurate quantification of liver fat content though estimation of proton density fat-fraction (PDFF). Computed tomography (CT) is capable of quantifying fat, based on decreased attenuation with increased fat concentration. Current quantitative fat phantoms do not accurately mimic the CT number of human liver. The purpose of this work was to develop and validate an optimized phantom that simultaneously mimics the MRI and CT signals of fatty liver. METHODS: An agar-based phantom containing 12 vials doped with iodinated contrast, and with a granular range of fat fractions was designed and constructed within a novel CT and MR compatible spherical housing design. A four-site, three-vendor validation study was performed. MRI (1.5T and 3T) and CT images were obtained using each vendor's PDFF and CT reconstruction, respectively. An ROI centered in each vial was placed to measure MRI-PDFF (%) and CT number (HU). Mixed-effects model, linear regression, and Bland-Altman analysis were used for statistical analysis. RESULTS: MRI-PDFF agreed closely with nominal PDFF values across both field strengths and all MRI vendors. A linear relationship (slope = -0.54 ± 0.01%/HU, intercept = 37.15 ± 0.03%) with an R2 of 0.999 was observed between MRI-PDFF and CT number, replicating established in vivo signal behavior. Excellent test-retest repeatability across vendors (MRI: mean = -0.04%, 95% limits of agreement = [-0.24%, 0.16%]; CT: mean = 0.16 HU, 95% limits of agreement = [-0.15HU, 0.47HU]) and good reproducibility using GE scanners (MRI: mean = -0.21%, 95% limits of agreement = [-1.47%, 1.06%]; CT: mean = -0.18HU, 95% limits of agreement = [-1.96HU, 1.6HU]) were demonstrated. CONCLUSIONS: The proposed fat phantom successfully mimicked quantitative liver signal for both MRI and CT. The proposed fat phantom in this study may facilitate broader application and harmonization of liver fat quantification techniques using MRI and CT across institutions, vendors and imaging platforms.
Authors: Jennifer L Rehm; Peter M Wolfgram; Diego Hernando; Jens C Eickhoff; David B Allen; Scott B Reeder Journal: Eur Radiol Date: 2015-04-28 Impact factor: 5.315
Authors: Huanzhou Yu; Ann Shimakawa; Charles A McKenzie; Ethan Brodsky; Jean H Brittain; Scott B Reeder Journal: Magn Reson Med Date: 2008-11 Impact factor: 4.668