Comparison of T1-weighted 2D TSE, 3D SPGR, and two-point 3D Dixon MRI for automated segmentation of visceral adipose tissue at 3 Tesla.

Objectives To evaluate and compare conventional T1-weighted 2D turbo spin echo (TSE), T1-weighted 3D volumetric interpolated breath-hold examination (VIBE), and two-point 3D Dixon-VIBE sequences for automatic segmentation of visceral adipose tissue (VAT) volume at 3 Tesla by measuring and compensating for errors arising from intensity nonuniformity (INU) and partial volume effects (PVE).

[1]  Faezeh Fallah,et al.  Rf and Coil Inhomogeneity Correction in 2d Leg Images: A New Method Comparing With Lems , 2015 .

[2]  Michael R. Aro,et al.  Fat-suppression techniques for 3-T MR imaging of the musculoskeletal system. , 2014, Radiographics : a review publication of the Radiological Society of North America, Inc.

[3]  Jingfei Ma Dixon techniques for water and fat imaging , 2008, Journal of magnetic resonance imaging : JMRI.

[4]  T Heimann,et al.  Automatic Generation of 3D Statistical Shape Models with Optimal Landmark Distributions , 2007, Methods of Information in Medicine.

[5]  Mohamed-Jalal Fadili,et al.  Brain tissue classification of magnetic resonance images using partial volume modeling , 2000, IEEE Transactions on Medical Imaging.

[6]  F. Schick,et al.  Standardized assessment of whole body adipose tissue topography by MRI , 2005, Journal of magnetic resonance imaging : JMRI.

[7]  Scott K Holland,et al.  Using a phantom to compare MR techniques for determining the ratio of intraabdominal to subcutaneous adipose tissue. , 2003, AJR. American journal of roentgenology.

[8]  Robin M Heidemann,et al.  Generalized autocalibrating partially parallel acquisitions (GRAPPA) , 2002, Magnetic resonance in medicine.

[9]  Hans Hauner,et al.  Accuracy and Reproducibility of Adipose Tissue Measurements in Young Infants by Whole Body Magnetic Resonance Imaging , 2015, PloS one.

[10]  J. Linseisen,et al.  Whole-Body MR Imaging in the German National Cohort: Rationale, Design, and Technical Background. , 2015, Radiology.

[11]  Dipti Prasad Mukherjee,et al.  Fuzzy c-Means Approach to Tissue Classification in Multimodal Medical Imaging , 1999, Inf. Sci..

[12]  Ian Law,et al.  Impact of incorrect tissue classification in Dixon-based MR-AC: fat-water tissue inversion , 2014, EJNMMI Physics.

[13]  Michael Jerosch-Herold,et al.  Visceral adiposity and the risk of metabolic syndrome across body mass index: the MESA Study. , 2014, JACC. Cardiovascular imaging.

[14]  Scott B Reeder,et al.  Improving chemical shift encoded water–fat separation using object‐based information of the magnetic field inhomogeneity , 2015, Magnetic resonance in medicine.

[15]  Jan Kassubek,et al.  Quantification of human body fat tissue percentage by MRI , 2011, NMR in biomedicine.

[16]  Qi Peng,et al.  Impact of partial volume effects on visceral adipose tissue quantification using MRI , 2011, Journal of magnetic resonance imaging : JMRI.

[17]  Krishna S Nayak,et al.  Automatic intra‐subject registration‐based segmentation of abdominal fat from water–fat MRI , 2013, Journal of magnetic resonance imaging : JMRI.

[18]  A Tremblay,et al.  Estimation of deep abdominal adipose-tissue accumulation from simple anthropometric measurements in men. , 1991, The American journal of clinical nutrition.

[19]  Jing Yuan,et al.  Max‐IDEAL: A max‐flow based approach for IDEAL water/fat separation , 2014, Magnetic resonance in medicine.

[20]  R. Leahy,et al.  Magnetic Resonance Image Tissue Classification Using a Partial Volume Model , 2001, NeuroImage.

[21]  Jorma Rissanen,et al.  The Minimum Description Length Principle in Coding and Modeling , 1998, IEEE Trans. Inf. Theory.

[22]  Charles A McKenzie,et al.  Validation of volumetric and single‐slice MRI adipose analysis using a novel fully automated segmentation method , 2015, Journal of magnetic resonance imaging : JMRI.

[23]  David H. Laidlaw,et al.  Partial-volume Bayesian classification of material mixtures in MR volume data using voxel histograms , 1997, IEEE Transactions on Medical Imaging.

[24]  Suresh Anand Sadananthan,et al.  Automated segmentation of visceral and subcutaneous (deep and superficial) adipose tissues in normal and overweight men , 2015, Journal of magnetic resonance imaging : JMRI.

[25]  Yi Lu,et al.  Message passing for in-vivo field map estimation in MRI , 2010, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[26]  Y. Matsuzawa,et al.  Correlation of intraabdominal fat accumulation and left ventricular performance in obesity. , 1989, The American journal of cardiology.

[27]  Bostjan Likar,et al.  A Review of Methods for Correction of Intensity Inhomogeneity in MRI , 2007, IEEE Transactions on Medical Imaging.

[28]  A. Davenel,et al.  Muscle and fat quantification in MRI gradient echo images using a partial volume detection method. Application to the characterization of pig belly tissue. , 2005, Magnetic resonance imaging.

[29]  C. D. Claussen,et al.  Age and gender related effects on adipose tissue compartments of subjects with increased risk for type 2 diabetes: a whole body MRI / MRS study , 2005, Magnetic Resonance Materials in Physics, Biology and Medicine.

[30]  Diego Hernando,et al.  Robust water/fat separation in the presence of large field inhomogeneities using a graph cut algorithm , 2009, Magnetic resonance in medicine.

[31]  Wilson Fong Handbook of MRI Pulse Sequences , 2005 .

[32]  R Holle,et al.  KORA - A Research Platform for Population Based Health Research , 2005, Gesundheitswesen (Bundesverband der Arzte des Offentlichen Gesundheitsdienstes (Germany)).

[33]  James F Meaney,et al.  An interactive taxonomy of MR imaging sequences. , 2006, Radiographics : a review publication of the Radiological Society of North America, Inc.

[34]  J. Seidell,et al.  Techniques for the measurement of visceral fat: a practical guide. , 1993, International journal of obesity and related metabolic disorders : journal of the International Association for the Study of Obesity.

[35]  Rachel A. Burton,et al.  Prospecting for Energy-Rich Renewable Raw Materials: Agave Leaf Case Study , 2015, PloS one.

[36]  Thomas Kahn,et al.  Software for automated MRI‐based quantification of abdominal fat and preliminary evaluation in morbidly obese patients , 2013, Journal of magnetic resonance imaging : JMRI.

[37]  Warren S Warren,et al.  Enhanced refocusing of fat signals using optimized multipulse echo sequences , 2013, Magnetic resonance in medicine.

[38]  J. Després,et al.  The insulin resistance-dyslipidemic syndrome of visceral obesity: effect on patients' risk. , 1998, Obesity research.

[39]  R. Henkelman,et al.  Why fat is bright in rare and fast spin‐echo imaging , 1992, Journal of magnetic resonance imaging : JMRI.

[40]  Diana Wald,et al.  Automatic quantification of subcutaneous and visceral adipose tissue from whole‐body magnetic resonance images suitable for large cohort studies , 2012, Journal of magnetic resonance imaging : JMRI.

[41]  Jerry L. Prince,et al.  Gradient vector flow: a new external force for snakes , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[42]  Fritz Schick,et al.  Topography mapping of whole body adipose tissue using A fully automated and standardized procedure , 2010, Journal of magnetic resonance imaging : JMRI.

[43]  Hans-Peter Meinzer,et al.  A Shape-Guided Deformable Model with Evolutionary Algorithm Initialization for 3D Soft Tissue Segmentation , 2007, IPMI.

[44]  Brian B. Avants,et al.  N4ITK: Improved N3 Bias Correction , 2010, IEEE Transactions on Medical Imaging.

[45]  Charles A McKenzie,et al.  Evaluation of adipose tissue volume quantification with IDEAL fat–water separation , 2011, Journal of magnetic resonance imaging : JMRI.

[46]  Olivier Salvado,et al.  Partial Volume Reduction by Interpolation with Reverse Diffusion , 2006, Int. J. Biomed. Imaging.

[47]  G. Glover Multipoint dixon technique for water and fat proton and susceptibility imaging , 1991, Journal of magnetic resonance imaging : JMRI.

[48]  Fritz Schick,et al.  Compensation of RF field and receiver coil induced inhomogeneity effects in abdominal MR images by a priori knowledge on the human adipose tissue distribution , 2011, Journal of magnetic resonance imaging : JMRI.

[49]  Örjan Smedby,et al.  Quantitative abdominal fat estimation using MRI , 2008, 2008 19th International Conference on Pattern Recognition.

[50]  Fritz Schick,et al.  Intrahepatic lipids are predicted by visceral adipose tissue mass in healthy subjects. , 2004, Diabetes care.

[51]  Joel Kullberg,et al.  Model‐based mapping of fat unsaturation and chain length by chemical shift imaging—phantom validation and in vivo feasibility , 2012, Magnetic resonance in medicine.

[52]  Marco Nolden,et al.  The Medical Imaging Interaction Toolkit , 2005, Medical Image Anal..

[53]  Abu-Bakr Al-Mehdi,et al.  Increased Depth of Cellular Imaging in the Intact Lung Using Far-Red and Near-Infrared Fluorescent Probes , 2006, Int. J. Biomed. Imaging.

[54]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.