Ruvini Navaratna1,2, Ruiyang Zhao1,2, Timothy J Colgan2, Houchun Harry Hu3, Mark Bydder4, Takeshi Yokoo5, Mustafa R Bashir6,7,8, Michael S Middleton9, Suraj D Serai10, Dariya Malyarenko11, Thomas Chenevert11, Mark Smith3, Walter Henderson9, Gavin Hamilton9, Yunhong Shu12, Claude B Sirlin9, Jean A Tkach13, Andrew T Trout13,14, Jean H Brittain15, Diego Hernando1,2, Scott B Reeder1,2,16,17,18. 1. Department of Medical Physics, University of Wisconsin - Madison, Madison, Wisconsin, USA. 2. Department of Radiology, University of Wisconsin - Madison, Madison, Wisconsin, USA. 3. Department of Radiology, Nationwide Children's Hospital, Columbus, Ohio, USA. 4. Department of Radiological Sciences, University of California - Los Angeles, Los Angeles, California, USA. 5. Department of Radiology, University of Texas - Southwestern Medical Center, Dallas, Texas, USA. 6. Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA. 7. Division of Hepatology, Department of Medicine, Duke University Medical Center, Durham, North Carolina, USA. 8. Center for Advanced Magnetic Resonance Development, Duke University Medical Center, Durham, North Carolina, USA. 9. Liver Imaging Group, Department of Radiology, University of California - San Diego, San Diego, California, USA. 10. Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA. 11. Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA. 12. Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA. 13. Department of Radiology, Cincinnati Children's Hospital and Medical Center, Cincinnati, Ohio, USA. 14. Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA. 15. Calimetrix, LLC, Madison, Wisconsin, USA. 16. Department of Biomedical Engineering, University of Wisconsin - Madison, Madison, Wisconsin, USA. 17. Department of Medicine, University of Wisconsin - Madison, Madison, Wisconsin, USA. 18. Department of Emergency Medicine, University of Wisconsin - Madison, Madison, Wisconsin, USA.
Abstract
PURPOSE: Chemical shift-encoded MRI (CSE-MRI) is well-established to quantify proton density fat fraction (PDFF) as a quantitative biomarker of hepatic steatosis. However, temperature is known to bias PDFF estimation in phantom studies. In this study, strategies were developed and evaluated to correct for the effects of temperature on PDFF estimation through simulations, temperature-controlled experiments, and a multi-center, multi-vendor phantom study. THEORY AND METHODS: A technical solution that assumes and automatically estimates a uniform, global temperature throughout the phantom is proposed. Computer simulations modeled the effect of temperature on PDFF estimation using magnitude-, complex-, and hybrid-based CSE-MRI methods. Phantom experiments were performed to assess the temperature correction on PDFF estimation at controlled phantom temperatures. To assess the temperature correction method on a larger scale, the proposed method was applied to data acquired as part of a nine-site multi-vendor phantom study and compared to temperature-corrected PDFF estimation using an a priori guess for ambient room temperature. RESULTS: Simulations and temperature-controlled experiments show that as temperature deviates further from the assumed temperature, PDFF bias increases. Using the proposed correction method and a reasonable a priori guess for ambient temperature, PDFF bias and variability were reduced using magnitude-based CSE-MRI, across MRI systems, field strengths, protocols, and varying phantom temperature. Complex and hybrid methods showed little PDFF bias and variability both before and after correction. CONCLUSION: Correction for temperature reduces temperature-related PDFF bias and variability in phantoms across MRI vendors, sites, field strengths, and protocols for magnitude-based CSE-MRI, even without a priori information about the temperature.
PURPOSE: Chemical shift-encoded MRI (CSE-MRI) is well-established to quantify proton density fat fraction (PDFF) as a quantitative biomarker of hepatic steatosis. However, temperature is known to bias PDFF estimation in phantom studies. In this study, strategies were developed and evaluated to correct for the effects of temperature on PDFF estimation through simulations, temperature-controlled experiments, and a multi-center, multi-vendor phantom study. THEORY AND METHODS: A technical solution that assumes and automatically estimates a uniform, global temperature throughout the phantom is proposed. Computer simulations modeled the effect of temperature on PDFF estimation using magnitude-, complex-, and hybrid-based CSE-MRI methods. Phantom experiments were performed to assess the temperature correction on PDFF estimation at controlled phantom temperatures. To assess the temperature correction method on a larger scale, the proposed method was applied to data acquired as part of a nine-site multi-vendor phantom study and compared to temperature-corrected PDFF estimation using an a priori guess for ambient room temperature. RESULTS: Simulations and temperature-controlled experiments show that as temperature deviates further from the assumed temperature, PDFF bias increases. Using the proposed correction method and a reasonable a priori guess for ambient temperature, PDFF bias and variability were reduced using magnitude-based CSE-MRI, across MRI systems, field strengths, protocols, and varying phantom temperature. Complex and hybrid methods showed little PDFF bias and variability both before and after correction. CONCLUSION: Correction for temperature reduces temperature-related PDFF bias and variability in phantoms across MRI vendors, sites, field strengths, and protocols for magnitude-based CSE-MRI, even without a priori information about the temperature.
Authors: Liam A J Young; Carlo D L Ceresa; Ferenc E Mózes; Jane Ellis; Ladislav Valkovič; Richard Colling; Constantin-C Coussios; Peter J Friend; Christopher T Rodgers Journal: Magn Reson Med Date: 2021-07-17 Impact factor: 3.737