Christoph Mahlke1, Diego Hernando2, Christina Jahn1, Antonio Cigliano3, Till Ittermann4, Anne Mössler5, Marie-Luise Kromrey1, Grazyna Domaska6, Scott B Reeder2,7, Jens-Peter Kühn1. 1. Department of Radiology and Neuroradiology, University of Greifswald, Greifswald, Germany. 2. Department of Radiology, University of Wisconsin, Madison, Wisconsin, USA. 3. Department of Pathology, University of Greifswald, Greifswald, Germany. 4. Institute of Community Medicine, University of Greifswald, Greifswald, Germany. 5. Institute of Animal Nutrition, University of Veterinary Medicine, Hannover, Germany. 6. Department of Immunology, University of Greifswald, Greifswald, Germany. 7. Departments of Medical Physics, Biomedical Engineering, Medicine and Emergency Medicine, University of Wisconsin, Madison, Wisconsin, USA.
Abstract
PURPOSE: To investigate the feasibility of estimating the proton-density fat fraction (PDFF) using a 7.1T magnetic resonance imaging (MRI) system and to compare the accuracy of liver fat quantification using different fitting approaches. MATERIALS AND METHODS: Fourteen leptin-deficient ob/ob mice and eight intact controls were examined in a 7.1T animal scanner using a 3D six-echo chemical shift-encoded pulse sequence. Confounder-corrected PDFF was calculated using magnitude (magnitude data alone) and combined fitting (complex and magnitude data). Differences between fitting techniques were compared using Bland-Altman analysis. In addition, PDFFs derived with both reconstructions were correlated with histopathological fat content and triglyceride mass fraction using linear regression analysis. RESULTS: The PDFFs determined with the use of both reconstructions correlated very strongly (r = 0.91). However, small mean bias between reconstructions demonstrated divergent results (3.9%; confidence interval [CI] 2.7-5.1%). For both reconstructions, there was linear correlation with histopathology (combined fitting: r = 0.61; magnitude fitting: r = 0.64) and triglyceride content (combined fitting: r = 0.79; magnitude fitting: r = 0.70). CONCLUSION: Liver fat quantification using the PDFF derived from MRI performed at 7.1T is feasible. PDFF has strong correlations with histopathologically determined fat and with triglyceride content. However, small differences between PDFF reconstruction techniques may impair the robustness and reliability of the biomarker at 7.1T. J. Magn. Reson. Imaging 2016;44:1425-1431.
PURPOSE: To investigate the feasibility of estimating the proton-density fat fraction (PDFF) using a 7.1T magnetic resonance imaging (MRI) system and to compare the accuracy of liver fat quantification using different fitting approaches. MATERIALS AND METHODS: Fourteen leptin-deficient ob/ob mice and eight intact controls were examined in a 7.1T animal scanner using a 3D six-echo chemical shift-encoded pulse sequence. Confounder-corrected PDFF was calculated using magnitude (magnitude data alone) and combined fitting (complex and magnitude data). Differences between fitting techniques were compared using Bland-Altman analysis. In addition, PDFFs derived with both reconstructions were correlated with histopathological fat content and triglyceride mass fraction using linear regression analysis. RESULTS: The PDFFs determined with the use of both reconstructions correlated very strongly (r = 0.91). However, small mean bias between reconstructions demonstrated divergent results (3.9%; confidence interval [CI] 2.7-5.1%). For both reconstructions, there was linear correlation with histopathology (combined fitting: r = 0.61; magnitude fitting: r = 0.64) and triglyceride content (combined fitting: r = 0.79; magnitude fitting: r = 0.70). CONCLUSION: Liver fat quantification using the PDFF derived from MRI performed at 7.1T is feasible. PDFF has strong correlations with histopathologically determined fat and with triglyceride content. However, small differences between PDFF reconstruction techniques may impair the robustness and reliability of the biomarker at 7.1T. J. Magn. Reson. Imaging 2016;44:1425-1431.
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