title: Automatic multi-parametric quantification of the proximal femur with quantitative computed tomography.
abstract: BACKGROUND
  Quantitative computed tomography (QCT) imaging is the basis for multiple assessments of bone quality in the proximal femur, including volumetric bone mineral density (vBMD), tissue volume, estimation of bone strength using finite element modeling (FEM), cortical bone thickness, and computational-anatomy-based morphometry assessments.
  
  
  METHODS
  Here, we present an automatic framework to perform a multi-parametric QCT quantification of the proximal femur. In this framework, the proximal femur is cropped from the bilateral hip scans, segmented using a multi-atlas based segmentation approach, and then assigned volumes of interest through the registration of a proximal femoral template. The proximal femur is then subjected to compartmental vBMD, compartmental tissue volume, FEM bone strength, compartmental surface-based cortical bone thickness, compartmental surface-based vBMD, local surface-based cortical bone thickness, and local surface-based cortical vBMD computations. Consequently, the template registrations together with vBMD and surface-based cortical bone parametric maps enable computational anatomy studies. The accuracy of the segmentation was validated against manual segmentations of 80 scans from two clinical facilities, while the multi-parametric reproducibility was evaluated using repeat scans with repositioning from 22 subjects obtained on CT imaging systems from two manufacturers.
  
  
  RESULTS
  Accuracy results yielded a mean dice similarity coefficient of 0.976±0.006, and a modified Haussdorf distance of 0.219±0.071 mm. Reproducibility of QCT-derived parameters yielded root mean square coefficients of variation (CVRMS) between 0.89-1.66% for compartmental vBMD; 0.20-1.82% for compartmental tissue volume; 3.51-3.59% for FEM bone strength; 1.89-2.69% for compartmental surface-based cortical bone thickness; and 1.08-2.19% for compartmental surface-based cortical vBMD. For local surface-based assessments, mean CVRMS were between 3.45-3.91% and 2.74-3.15% for cortical bone thickness and vBMD, respectively.
  
  
  CONCLUSIONS
  The automatic framework presented here enables accurate and reproducible QCT multi-parametric analyses of the proximal femur. Our subjects were elderly, with scans obtained across multiple clinical sites and manufacturers, thus documenting its value for clinical trials and other multi-site studies.
authors.affiliation: 1 Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA ; 2 Department of Endocrinology, Creighton University, Omaha, NE, USA ; 3 Department of Radiological Sciences, Department of Mechanical and Aerospace Engineering, Department of Biomedical Engineering, and Chao Family Comprehensive Cancer Center, University of California, Irvine, Irvine, CA, USA ; 4 Intramural Research Program, National Institute on Aging, Bethesda, Maryland, USA ; 5 Division of Endocrinology, Diabetes, Metabolism and Nutrition, Department of Internal Medicine, College of Medicine, Mayo Clinic, Rochester, MN, USA.
authors.forename: Julio
authors.initials: J
authors.lastname: Carballido-Gamio
authors.affiliation: 1 Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA ; 2 Department of Endocrinology, Creighton University, Omaha, NE, USA ; 3 Department of Radiological Sciences, Department of Mechanical and Aerospace Engineering, Department of Biomedical Engineering, and Chao Family Comprehensive Cancer Center, University of California, Irvine, Irvine, CA, USA ; 4 Intramural Research Program, National Institute on Aging, Bethesda, Maryland, USA ; 5 Division of Endocrinology, Diabetes, Metabolism and Nutrition, Department of Internal Medicine, College of Medicine, Mayo Clinic, Rochester, MN, USA.
authors.forename: Serena
authors.initials: S
authors.lastname: Bonaretti
authors.affiliation: 1 Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA ; 2 Department of Endocrinology, Creighton University, Omaha, NE, USA ; 3 Department of Radiological Sciences, Department of Mechanical and Aerospace Engineering, Department of Biomedical Engineering, and Chao Family Comprehensive Cancer Center, University of California, Irvine, Irvine, CA, USA ; 4 Intramural Research Program, National Institute on Aging, Bethesda, Maryland, USA ; 5 Division of Endocrinology, Diabetes, Metabolism and Nutrition, Department of Internal Medicine, College of Medicine, Mayo Clinic, Rochester, MN, USA.
authors.forename: Isra
authors.initials: I
authors.lastname: Saeed
authors.affiliation: 1 Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA ; 2 Department of Endocrinology, Creighton University, Omaha, NE, USA ; 3 Department of Radiological Sciences, Department of Mechanical and Aerospace Engineering, Department of Biomedical Engineering, and Chao Family Comprehensive Cancer Center, University of California, Irvine, Irvine, CA, USA ; 4 Intramural Research Program, National Institute on Aging, Bethesda, Maryland, USA ; 5 Division of Endocrinology, Diabetes, Metabolism and Nutrition, Department of Internal Medicine, College of Medicine, Mayo Clinic, Rochester, MN, USA.
authors.forename: Roy
authors.initials: R
authors.lastname: Harnish
authors.affiliation: 1 Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA ; 2 Department of Endocrinology, Creighton University, Omaha, NE, USA ; 3 Department of Radiological Sciences, Department of Mechanical and Aerospace Engineering, Department of Biomedical Engineering, and Chao Family Comprehensive Cancer Center, University of California, Irvine, Irvine, CA, USA ; 4 Intramural Research Program, National Institute on Aging, Bethesda, Maryland, USA ; 5 Division of Endocrinology, Diabetes, Metabolism and Nutrition, Department of Internal Medicine, College of Medicine, Mayo Clinic, Rochester, MN, USA.
authors.forename: Robert
authors.initials: R
authors.lastname: Recker
authors.affiliation: 1 Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA ; 2 Department of Endocrinology, Creighton University, Omaha, NE, USA ; 3 Department of Radiological Sciences, Department of Mechanical and Aerospace Engineering, Department of Biomedical Engineering, and Chao Family Comprehensive Cancer Center, University of California, Irvine, Irvine, CA, USA ; 4 Intramural Research Program, National Institute on Aging, Bethesda, Maryland, USA ; 5 Division of Endocrinology, Diabetes, Metabolism and Nutrition, Department of Internal Medicine, College of Medicine, Mayo Clinic, Rochester, MN, USA.
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Burghardt
authors.affiliation: 1 Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA ; 2 Department of Endocrinology, Creighton University, Omaha, NE, USA ; 3 Department of Radiological Sciences, Department of Mechanical and Aerospace Engineering, Department of Biomedical Engineering, and Chao Family Comprehensive Cancer Center, University of California, Irvine, Irvine, CA, USA ; 4 Intramural Research Program, National Institute on Aging, Bethesda, Maryland, USA ; 5 Division of Endocrinology, Diabetes, Metabolism and Nutrition, Department of Internal Medicine, College of Medicine, Mayo Clinic, Rochester, MN, USA.
authors.forename: Joyce H
authors.initials: JH
authors.lastname: Keyak
authors.affiliation: 1 Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA ; 2 Department of Endocrinology, Creighton University, Omaha, NE, USA ; 3 Department of Radiological Sciences, Department of Mechanical and Aerospace Engineering, Department of Biomedical Engineering, and Chao Family Comprehensive Cancer Center, University of California, Irvine, Irvine, CA, USA ; 4 Intramural Research Program, National Institute on Aging, Bethesda, Maryland, USA ; 5 Division of Endocrinology, Diabetes, Metabolism and Nutrition, Department of Internal Medicine, College of Medicine, Mayo Clinic, Rochester, MN, USA.
authors.forename: Tamara
authors.initials: T
authors.lastname: Harris
authors.affiliation: 1 Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA ; 2 Department of Endocrinology, Creighton University, Omaha, NE, USA ; 3 Department of Radiological Sciences, Department of Mechanical and Aerospace Engineering, Department of Biomedical Engineering, and Chao Family Comprehensive Cancer Center, University of California, Irvine, Irvine, CA, USA ; 4 Intramural Research Program, National Institute on Aging, Bethesda, Maryland, USA ; 5 Division of Endocrinology, Diabetes, Metabolism and Nutrition, Department of Internal Medicine, College of Medicine, Mayo Clinic, Rochester, MN, USA.
authors.forename: Sundeep
authors.initials: S
authors.lastname: Khosla
authors.affiliation: 1 Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA ; 2 Department of Endocrinology, Creighton University, Omaha, NE, USA ; 3 Department of Radiological Sciences, Department of Mechanical and Aerospace Engineering, Department of Biomedical Engineering, and Chao Family Comprehensive Cancer Center, University of California, Irvine, Irvine, CA, USA ; 4 Intramural Research Program, National Institute on Aging, Bethesda, Maryland, USA ; 5 Division of Endocrinology, Diabetes, Metabolism and Nutrition, Department of Internal Medicine, College of Medicine, Mayo Clinic, Rochester, MN, USA.
authors.forename: Thomas F
authors.initials: TF
authors.lastname: Lang
keywords: Computed tomography (CT); bone mineral density (BMD); finite element modeling (FEM); reproducibility; segmentation; thickness