3D-DXA: Assessing the Femoral Shape, the Trabecular Macrostructure and the Cortex in 3D from DXA images
The 3D distribution of the cortical and trabecular bone mass in the proximal femur is a critical component in determining fracture resistance that is not taken into account in clinical routine Dual-energy X-ray Absorptiometry (DXA) examination. In this paper, a statistical shape and appearance model...
Saved in:
Published in | IEEE transactions on medical imaging Vol. 36; no. 1; pp. 27 - 39 |
---|---|
Main Authors | , , , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
IEEE
01.01.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | The 3D distribution of the cortical and trabecular bone mass in the proximal femur is a critical component in determining fracture resistance that is not taken into account in clinical routine Dual-energy X-ray Absorptiometry (DXA) examination. In this paper, a statistical shape and appearance model together with a 3D-2D registration approach are used to model the femoral shape and bone density distribution in 3D from an anteroposterior DXA projection. A model-based algorithm is subsequently used to segment the cortex and build a 3D map of the cortical thickness and density. Measurements characterising the geometry and density distribution were computed for various regions of interest in both cortical and trabecular compartments. Models and measurements provided by the "3D-DXA" software algorithm were evaluated using a database of 157 study subjects, by comparing 3D-DXA analyses (using DXA scanners from three manufacturers) with measurements performed by Quantitative Computed Tomography (QCT). The mean point-to-surface distance between 3D-DXA and QCT femoral shapes was 0.93 mm. The mean absolute error between cortical thickness and density estimates measured by 3D-DXA and QCT was 0.33 mm and 72 mg/cm 3 . Correlation coefficients (R) between the 3D-DXA and QCT measurements were 0.86, 0.93, and 0.95 for the volumetric bone mineral density at the trabecular, cortical, and integral compartments respectively, and 0.91 for the mean cortical thickness. 3D-DXA provides a detailed analysis of the proximal femur, including a separate assessment of the cortical layer and trabecular macrostructure, which could potentially improve osteoporosis management while maintaining DXA as the standard routine modality. |
---|---|
AbstractList | The 3D distribution of the cortical and trabecular bone mass in the proximal femur is a critical component in determining fracture resistance that is not taken into account in clinical routine Dual-energy X-ray Absorptiometry (DXA) examination. In this paper, a statistical shape and appearance model together with a 3D-2D registration approach are used to model the femoral shape and bone density distribution in 3D from an anteroposterior DXA projection. A model-based algorithm is subsequently used to segment the cortex and build a 3D map of the cortical thickness and density. Measurements characterising the geometry and density distribution were computed for various regions of interest in both cortical and trabecular compartments. Models and measurements provided by the "3D-DXA" software algorithm were evaluated using a database of 157 study subjects, by comparing 3D-DXA analyses (using DXA scanners from three manufacturers) with measurements performed by Quantitative Computed Tomography (QCT). The mean point-to-surface distance between 3D-DXA and QCT femoral shapes was 0.93 mm. The mean absolute error between cortical thickness and density estimates measured by 3D-DXA and QCT was 0.33 mm and 72 mg/cm
. Correlation coefficients (R) between the 3D-DXA and QCT measurements were 0.86, 0.93, and 0.95 for the volumetric bone mineral density at the trabecular, cortical, and integral compartments respectively, and 0.91 for the mean cortical thickness. 3D-DXA provides a detailed analysis of the proximal femur, including a separate assessment of the cortical layer and trabecular macrostructure, which could potentially improve osteoporosis management while maintaining DXA as the standard routine modality. The 3D distribution of the cortical and trabecular bone mass in the proximal femur is a critical component in determining fracture resistance that is not taken into account in clinical routine Dual-energy X-ray Absorptiometry (DXA) examination. In this paper, a statistical shape and appearance model together with a 3D-2D registration approach are used to model the femoral shape and bone density distribution in 3D from an anteroposterior DXA projection. A model-based algorithm is subsequently used to segment the cortex and build a 3D map of the cortical thickness and density. Measurements characterising the geometry and density distribution were computed for various regions of interest in both cortical and trabecular compartments. Models and measurements provided by the "3D-DXA" software algorithm were evaluated using a database of 157 study subjects, by comparing 3D-DXA analyses (using DXA scanners from three manufacturers) with measurements performed by Quantitative Computed Tomography (QCT). The mean point-to-surface distance between 3D-DXA and QCT femoral shapes was 0.93 mm. The mean absolute error between cortical thickness and density estimates measured by 3D-DXA and QCT was 0.33 mm and 72 mg/cm 3 . Correlation coefficients (R) between the 3D-DXA and QCT measurements were 0.86, 0.93, and 0.95 for the volumetric bone mineral density at the trabecular, cortical, and integral compartments respectively, and 0.91 for the mean cortical thickness. 3D-DXA provides a detailed analysis of the proximal femur, including a separate assessment of the cortical layer and trabecular macrostructure, which could potentially improve osteoporosis management while maintaining DXA as the standard routine modality. The 3D distribution of the cortical and trabecular bone mass in the proximal femur is a critical component in determining fracture resistance that is not taken into account in clinical routine Dual-energy X-ray Absorptiometry (DXA) examination. In this paper, a statistical shape and appearance model together with a 3D-2D registration approach are used to model the femoral shape and bone density distribution in 3D from an anteroposterior DXA projection. A model-based algorithm is subsequently used to segment the cortex and build a 3D map of the cortical thickness and density. Measurements characterising the geometry and density distribution were computed for various regions of interest in both cortical and trabecular compartments. Models and measurements provided by the “3D-DXA” software algorithm were evaluated using a database of 157 study subjects, by comparing 3D-DXA analyses (using DXA scanners from three manufacturers) with measurements performed by Quantitative Computed Tomography (QCT). The mean point-to-surface distance between 3D-DXA and QCT femoral shapes was 0.93 mm. The mean absolute error between cortical thickness and density estimates measured by 3D-DXA and QCT was 0.33 mm and 72 mg/cm3. Correlation coefficients (R) between the 3D-DXA and QCT measurements were 0.86, 0.93, and 0.95 for the volumetric bone mineral density at the trabecular, cortical, and integral compartments respectively, and 0.91 for the mean cortical thickness. 3D-DXA provides a detailed analysis of the proximal femur, including a separate assessment of the cortical layer and trabecular macrostructure, which could potentially improve osteoporosis management while maintaining DXA as the standard routine modality. The 3D distribution of the cortical and trabecular bone mass in the proximal femur is a critical component in determining fracture resistance that is not taken into account in clinical routine Dual-energy X-ray Absorptiometry (DXA) examination. In this paper, a statistical shape and appearance model together with a 3D-2D registration approach are used to model the femoral shape and bone density distribution in 3D from an anteroposterior DXA projection. A model-based algorithm is subsequently used to segment the cortex and build a 3D map of the cortical thickness and density. Measurements characterising the geometry and density distribution were computed for various regions of interest in both cortical and trabecular compartments. Models and measurements provided by the "3D-DXA" software algorithm were evaluated using a database of 157 study subjects, by comparing 3D-DXA analyses (using DXA scanners from three manufacturers) with measurements performed by Quantitative Computed Tomography (QCT). The mean point-to-surface distance between 3D-DXA and QCT femoral shapes was 0.93 mm. The mean absolute error between cortical thickness and density estimates measured by 3D-DXA and QCT was 0.33 mm and 72 mg/cm3. Correlation coefficients (R) between the 3D-DXA and QCT measurements were 0.86, 0.93, and 0.95 for the volumetric bone mineral density at the trabecular, cortical, and integral compartments respectively, and 0.91 for the mean cortical thickness. 3D-DXA provides a detailed analysis of the proximal femur, including a separate assessment of the cortical layer and trabecular macrostructure, which could potentially improve osteoporosis management while maintaining DXA as the standard routine modality.The 3D distribution of the cortical and trabecular bone mass in the proximal femur is a critical component in determining fracture resistance that is not taken into account in clinical routine Dual-energy X-ray Absorptiometry (DXA) examination. In this paper, a statistical shape and appearance model together with a 3D-2D registration approach are used to model the femoral shape and bone density distribution in 3D from an anteroposterior DXA projection. A model-based algorithm is subsequently used to segment the cortex and build a 3D map of the cortical thickness and density. Measurements characterising the geometry and density distribution were computed for various regions of interest in both cortical and trabecular compartments. Models and measurements provided by the "3D-DXA" software algorithm were evaluated using a database of 157 study subjects, by comparing 3D-DXA analyses (using DXA scanners from three manufacturers) with measurements performed by Quantitative Computed Tomography (QCT). The mean point-to-surface distance between 3D-DXA and QCT femoral shapes was 0.93 mm. The mean absolute error between cortical thickness and density estimates measured by 3D-DXA and QCT was 0.33 mm and 72 mg/cm3. Correlation coefficients (R) between the 3D-DXA and QCT measurements were 0.86, 0.93, and 0.95 for the volumetric bone mineral density at the trabecular, cortical, and integral compartments respectively, and 0.91 for the mean cortical thickness. 3D-DXA provides a detailed analysis of the proximal femur, including a separate assessment of the cortical layer and trabecular macrostructure, which could potentially improve osteoporosis management while maintaining DXA as the standard routine modality. |
Author | Steghofer, Martin Di Gregorio, Silvana Malouf, Jorge Romera, Jordi Barquero, Luis Miguel Del Rio Martelli, Yves Fonolla, Roger Humbert, Ludovic |
Author_xml | – sequence: 1 givenname: Ludovic orcidid: 0000-0002-3675-7908 surname: Humbert fullname: Humbert, Ludovic email: ludovic.humbert@galgomedical.com organization: Musculoskeletal Unit, Galgo Medical, Barcelona, Spain – sequence: 2 givenname: Yves surname: Martelli fullname: Martelli, Yves email: yves.martelli@galgomedical.com organization: Musculoskeletal Unit, Galgo Medical, Barcelona, Spain – sequence: 3 givenname: Roger surname: Fonolla fullname: Fonolla, Roger email: roger.fonolla@galgomedical.com organization: Musculoskeletal Unit, Galgo Medical, Barcelona, Spain – sequence: 4 givenname: Martin surname: Steghofer fullname: Steghofer, Martin email: martin.steghofer@galgomedical.com organization: Musculoskeletal Unit, Galgo Medical, Barcelona, Spain – sequence: 5 givenname: Silvana surname: Di Gregorio fullname: Di Gregorio, Silvana email: sgregorio@cetir.es organization: CETIR Grup Mèdic, Barcelona, Spain – sequence: 6 givenname: Jorge surname: Malouf fullname: Malouf, Jorge email: jmalouf@santpau.cat organization: Department of Internal Medicine, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain – sequence: 7 givenname: Jordi surname: Romera fullname: Romera, Jordi email: jordi.romera@galgomedical.com organization: Musculoskeletal Unit, Galgo Medical, Barcelona, Spain – sequence: 8 givenname: Luis Miguel Del Rio surname: Barquero fullname: Barquero, Luis Miguel Del Rio email: ldelrio@cetir.es organization: CETIR Grup Mèdic, Barcelona, Spain |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/27448343$$D View this record in MEDLINE/PubMed |
BookMark | eNp9kc1rGzEQxUVJaZy090KhCHrJoevqW6vejN20gYQc4kBuQtqddTbshyNpof3vI8dODznkMAwMv_eYmXeCjoZxAIQ-UzKnlJgf66uLOSNUzZk0nAv1Ds2olGXBpLg7QjPCdFkQotgxOonxgRAqJDEf0DHTQpRc8Bna8FWxulv8xIsYIcZ22OB0D_gc-jG4Dt_cuy18fx6tg_NQTZ0L-MpVYYwpTFWaAmA31M_EcgwJ_uJ2wHyFmzD2ODvjtncbiB_R-8Z1ET4d-im6Pf-1Xv4pLq9_XywXl0XFhU6Fqb0rOVOeO-N9LXUuSYkTvlHeU8U4r8rGE2NUw3xpBHO1rJRkwpeOcsdP0dnedxvGxwlisn0bK-g6N8A4RUtLpjQXUuqMfnuFPoxTGPJ2mcqA0prQTH09UJPvobbbkA8K_-zLCzOg9sDuJzFAY6s2udSOQwqu7SwldpeVzVnZXVb2kFUWklfCF-83JF_2khYA_uNaUqWM4U9_Xpvu |
CODEN | ITMID4 |
CitedBy_id | crossref_primary_10_1007_s00259_024_06912_6 crossref_primary_10_2215_CJN_0000000000000213 crossref_primary_10_1007_s11926_020_00892_w crossref_primary_10_1007_s00198_024_07244_9 crossref_primary_10_1016_j_cmpb_2020_105484 crossref_primary_10_1016_j_jocd_2021_11_007 crossref_primary_10_3390_ma13010106 crossref_primary_10_1007_s00198_020_05723_3 crossref_primary_10_3390_jcm8091370 crossref_primary_10_1371_journal_pone_0175857 crossref_primary_10_1007_s00198_019_04894_y crossref_primary_10_1007_s11657_019_0680_4 crossref_primary_10_1007_s11657_021_00921_w crossref_primary_10_1016_j_endinu_2020_01_005 crossref_primary_10_1007_s00198_017_4268_9 crossref_primary_10_1007_s10439_021_02787_y crossref_primary_10_3389_fendo_2022_1069224 crossref_primary_10_1093_comjnl_bxz011 crossref_primary_10_1016_j_medcle_2024_05_030 crossref_primary_10_3390_ijms22147318 crossref_primary_10_1016_j_bone_2020_115362 crossref_primary_10_1007_s00223_025_01349_x crossref_primary_10_3389_fmed_2025_1471418 crossref_primary_10_3390_jcm9061732 crossref_primary_10_1016_j_jocd_2018_05_001 crossref_primary_10_1016_j_medcli_2024_05_014 crossref_primary_10_1007_s00223_023_01110_2 crossref_primary_10_1093_jbmr_zjae028 crossref_primary_10_1007_s40200_024_01487_3 crossref_primary_10_1016_j_jocd_2024_101471 crossref_primary_10_3389_fmed_2024_1341077 crossref_primary_10_1530_EJE_22_0687 crossref_primary_10_1007_s00198_020_05806_1 crossref_primary_10_1016_j_medengphy_2020_01_015 crossref_primary_10_1093_jbmrpl_ziae003 crossref_primary_10_2106_JBJS_23_01334 crossref_primary_10_1016_j_bone_2021_115939 crossref_primary_10_1007_s11914_021_00711_w crossref_primary_10_1016_j_bone_2020_115678 crossref_primary_10_1007_s00198_019_05208_y crossref_primary_10_1016_j_bone_2021_115936 crossref_primary_10_1016_j_bone_2021_116005 crossref_primary_10_11124_JBIES_22_00175 crossref_primary_10_1016_j_eprac_2021_08_006 crossref_primary_10_1002_ajhb_24034 crossref_primary_10_1002_rcs_2503 crossref_primary_10_1016_j_jocd_2017_05_012 crossref_primary_10_1210_clinem_dgab259 crossref_primary_10_1002_jbmr_4878 crossref_primary_10_1016_j_jocd_2021_01_010 crossref_primary_10_3389_fbioe_2023_1111020 crossref_primary_10_1007_s00198_018_4624_4 crossref_primary_10_1016_j_endien_2020_01_008 crossref_primary_10_1016_j_bone_2019_01_001 crossref_primary_10_1080_02640414_2018_1483178 crossref_primary_10_1016_j_jocd_2018_11_004 crossref_primary_10_1002_jbm4_10612 crossref_primary_10_1007_s11657_019_0645_7 crossref_primary_10_1055_a_1928_9824 crossref_primary_10_1109_TMI_2018_2845909 crossref_primary_10_1007_s11657_024_01415_1 crossref_primary_10_1016_j_eprac_2024_10_015 crossref_primary_10_1016_j_jbiomech_2021_110315 crossref_primary_10_1007_s00198_020_05641_4 crossref_primary_10_1016_j_jocd_2017_05_002 crossref_primary_10_1093_ndt_gfz195 crossref_primary_10_1109_TMI_2022_3209648 crossref_primary_10_1093_jbmr_zjae202 crossref_primary_10_1016_j_bone_2024_117270 crossref_primary_10_1007_s10237_022_01642_w crossref_primary_10_1016_j_eprac_2024_01_004 crossref_primary_10_1007_s11657_021_00933_6 crossref_primary_10_1016_j_bone_2025_117457 crossref_primary_10_1210_clinem_dgz060 crossref_primary_10_1007_s00198_019_05195_0 crossref_primary_10_1080_10255842_2020_1789863 crossref_primary_10_1186_s12889_020_09607_3 crossref_primary_10_1093_ckj_sfae240 crossref_primary_10_1007_s00198_021_06013_2 crossref_primary_10_3390_jcm10040657 |
Cites_doi | 10.1109/34.24792 10.1046/j.1532-5415.2002.50455.x 10.1002/jbmr.1856 10.1007/s10237-011-0352-9 10.1007/s001980070064 10.1359/jbmr.060506 10.1359/jbmr.040916 10.1007/978-3-642-04271-3_1 10.1016/j.media.2012.02.008 10.1097/00004424-199001000-00004 10.1007/s00198-014-2794-2 10.1109/TMI.2003.812265 10.1016/j.jocd.2013.08.004 10.1117/12.2006389 10.1002/jbmr.2241 10.1007/s00198-006-0074-5 10.1118/1.1521940 10.1359/jbmr.1998.13.12.1915 10.2105/AJPH.87.10.1630 10.1016/j.jocd.2015.06.011 10.1007/s00198-008-0712-1 10.1016/j.jbiomech.2014.06.027 10.1001/archinte.165.15.1762 10.1002/jbmr.5650070902 10.1002/jbmr.140 10.1002/jbmr.270 10.1109/TMI.2011.2163074 10.1118/1.4944501 10.1109/34.927467 10.1007/s10916-015-0266-7 10.1093/qjmed/hcn022 10.1109/TSMC.1979.4310076 10.1007/978-3-642-04271-3_98 10.1016/j.neuroimage.2006.01.015 10.1016/j.media.2014.11.012 10.1359/jbmr.1997.12.1.119 10.1088/0031-9155/43/3/013 10.1016/j.jocd.2007.12.010 10.1007/s00198-008-0665-4 10.1097/BOR.0000000000000183 10.1016/S0140-6736(05)66870-5 10.1007/s10439-010-0196-y 10.1109/TVCG.2013.159 10.1109/TPAMI.2010.46 10.1002/jbmr.1693 10.1016/j.media.2015.06.001 10.1016/j.bone.2012.11.042 10.1093/oxfordjournals.aje.a116756 10.1007/BF02291478 10.1016/j.media.2010.01.003 |
ContentType | Journal Article |
Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2017 |
Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2017 |
DBID | 97E RIA RIE AAYXX CITATION CGR CUY CVF ECM EIF NPM 7QF 7QO 7QQ 7SC 7SE 7SP 7SR 7TA 7TB 7U5 8BQ 8FD F28 FR3 H8D JG9 JQ2 KR7 L7M L~C L~D NAPCQ P64 7X8 |
DOI | 10.1109/TMI.2016.2593346 |
DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Xplore CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Aluminium Industry Abstracts Biotechnology Research Abstracts Ceramic Abstracts Computer and Information Systems Abstracts Corrosion Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts Materials Business File Mechanical & Transportation Engineering Abstracts Solid State and Superconductivity Abstracts METADEX Technology Research Database ANTE: Abstracts in New Technology & Engineering Engineering Research Database Aerospace Database Materials Research Database ProQuest Computer Science Collection Civil Engineering Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Nursing & Allied Health Premium Biotechnology and BioEngineering Abstracts MEDLINE - Academic |
DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Materials Research Database Civil Engineering Abstracts Aluminium Industry Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Ceramic Abstracts Materials Business File METADEX Biotechnology and BioEngineering Abstracts Computer and Information Systems Abstracts Professional Aerospace Database Nursing & Allied Health Premium Engineered Materials Abstracts Biotechnology Research Abstracts Solid State and Superconductivity Abstracts Engineering Research Database Corrosion Abstracts Advanced Technologies Database with Aerospace ANTE: Abstracts in New Technology & Engineering MEDLINE - Academic |
DatabaseTitleList | MEDLINE Materials Research Database MEDLINE - Academic |
Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 3 dbid: RIE name: IEEE Xplore url: https://proxy.k.utb.cz/login?url=https://ieeexplore.ieee.org/ sourceTypes: Publisher |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Medicine Engineering |
EISSN | 1558-254X |
EndPage | 39 |
ExternalDocumentID | 27448343 10_1109_TMI_2016_2593346 7516699 |
Genre | orig-research Journal Article |
GrantInformation_xml | – fundername: Centro para el Desarrollo Tecnológico Industrial, Ministerio de Economía y Competitividad funderid: 10.13039/501100003329 – fundername: Eurostars program grantid: 9 140 – fundername: Programa Estatal de Promoción del Talento y su Empleabilidad - Torres Quevedo, Ministerio de Economía y Competitividad grantid: SPTQ1300X006124XV0 funderid: 10.13039/501100003329 – fundername: Programa Estatal de Investigación, Desarrollo e Innovación Orientada a los Retos de la Sociedad, Ministerio de Economía y Competitividad grantid: RTC-2014-2740-1 funderid: 10.13039/501100003329 |
GroupedDBID | --- -DZ -~X .GJ 0R~ 29I 4.4 53G 5GY 5RE 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACGFS ACIWK ACNCT ACPRK AENEX AETIX AFRAH AGQYO AGSQL AHBIQ AI. AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ASUFR ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD F5P HZ~ H~9 IBMZZ ICLAB IFIPE IFJZH IPLJI JAVBF LAI M43 MS~ O9- OCL P2P PQQKQ RIA RIE RNS RXW TAE TN5 VH1 AAYOK AAYXX CITATION RIG CGR CUY CVF ECM EIF NPM 7QF 7QO 7QQ 7SC 7SE 7SP 7SR 7TA 7TB 7U5 8BQ 8FD F28 FR3 H8D JG9 JQ2 KR7 L7M L~C L~D NAPCQ P64 7X8 |
ID | FETCH-LOGICAL-c347t-9dba8326b3a9bbd57bd5510a4bf6bb16233c8fb0996f2b8942ad5c6524b8a13a3 |
IEDL.DBID | RIE |
ISSN | 0278-0062 1558-254X |
IngestDate | Thu Jul 10 22:40:28 EDT 2025 Sun Jun 29 16:10:49 EDT 2025 Mon Jul 21 06:00:03 EDT 2025 Tue Jul 01 03:15:57 EDT 2025 Thu Apr 24 23:00:07 EDT 2025 Wed Aug 27 05:51:43 EDT 2025 |
IsPeerReviewed | false |
IsScholarly | true |
Issue | 1 |
Language | English |
License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-c347t-9dba8326b3a9bbd57bd5510a4bf6bb16233c8fb0996f2b8942ad5c6524b8a13a3 |
Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
ORCID | 0000-0002-3675-7908 |
PMID | 27448343 |
PQID | 1855767701 |
PQPubID | 85460 |
PageCount | 13 |
ParticipantIDs | proquest_journals_1855767701 pubmed_primary_27448343 crossref_citationtrail_10_1109_TMI_2016_2593346 crossref_primary_10_1109_TMI_2016_2593346 ieee_primary_7516699 proquest_miscellaneous_1826734557 |
ProviderPackageCode | CITATION AAYXX |
PublicationCentury | 2000 |
PublicationDate | 2017-Jan. 2017-1-00 2017-Jan 20170101 |
PublicationDateYYYYMMDD | 2017-01-01 |
PublicationDate_xml | – month: 01 year: 2017 text: 2017-Jan. |
PublicationDecade | 2010 |
PublicationPlace | United States |
PublicationPlace_xml | – name: United States – name: New York |
PublicationTitle | IEEE transactions on medical imaging |
PublicationTitleAbbrev | TMI |
PublicationTitleAlternate | IEEE Trans Med Imaging |
PublicationYear | 2017 |
Publisher | IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher_xml | – name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
References | ref13 ref56 ref12 ref15 ref14 ref53 ref52 ref55 ref11 ref54 ref10 roweis (ref41) 1998 ref17 ref16 ref19 yoo (ref45) 2002; 85 ref51 ref50 ref46 ref47 ref42 ref43 ref49 ref8 ref7 ref9 ref3 ref6 ref5 ref40 ref35 press (ref44) 1992 ref34 ref37 ref36 ref31 ref30 ref33 ref32 ref2 ref1 ref39 ref38 hangartner (ref18) 2007; 7 ref24 ref23 ref26 ref25 ref20 ref22 ref21 ref28 ref27 ref29 kanis (ref4) 2007 bateman (ref48) 2012; 12 |
References_xml | – ident: ref39 doi: 10.1109/34.24792 – ident: ref6 doi: 10.1046/j.1532-5415.2002.50455.x – ident: ref12 doi: 10.1002/jbmr.1856 – ident: ref28 doi: 10.1007/s10237-011-0352-9 – ident: ref1 doi: 10.1007/s001980070064 – ident: ref32 doi: 10.1359/jbmr.060506 – ident: ref33 doi: 10.1359/jbmr.040916 – volume: 7 start-page: 9 year: 2007 ident: ref18 article-title: Thresholding technique for accurate analysis of density and geometry in QCT, pQCT and microCT images publication-title: J Musculoskel Neuron Interact – ident: ref42 doi: 10.1007/978-3-642-04271-3_1 – ident: ref23 doi: 10.1016/j.media.2012.02.008 – ident: ref11 doi: 10.1097/00004424-199001000-00004 – ident: ref9 doi: 10.1007/s00198-014-2794-2 – ident: ref36 doi: 10.1109/TMI.2003.812265 – ident: ref35 doi: 10.1016/j.jocd.2013.08.004 – ident: ref26 doi: 10.1117/12.2006389 – ident: ref56 doi: 10.1002/jbmr.2241 – ident: ref14 doi: 10.1007/s00198-006-0074-5 – ident: ref17 doi: 10.1118/1.1521940 – ident: ref2 doi: 10.1359/jbmr.1998.13.12.1915 – ident: ref7 doi: 10.2105/AJPH.87.10.1630 – ident: ref55 doi: 10.1016/j.jocd.2015.06.011 – ident: ref10 doi: 10.1007/s00198-008-0712-1 – ident: ref13 doi: 10.1016/j.jbiomech.2014.06.027 – volume: 85 start-page: 586 year: 2002 ident: ref45 article-title: Engineering and algorithm design for an image processing API: A technical report on ITK-The insight toolkit publication-title: Studies Health Technol Inf – ident: ref34 doi: 10.1001/archinte.165.15.1762 – ident: ref3 doi: 10.1002/jbmr.5650070902 – ident: ref25 doi: 10.1002/jbmr.140 – ident: ref54 doi: 10.1002/jbmr.270 – ident: ref29 doi: 10.1109/TMI.2011.2163074 – ident: ref20 doi: 10.1118/1.4944501 – ident: ref30 doi: 10.1109/34.927467 – year: 1992 ident: ref44 publication-title: Numerical Recipes in C The Art of Scientific Computing – ident: ref51 doi: 10.1007/s10916-015-0266-7 – start-page: 626 year: 1998 ident: ref41 article-title: EM algorithms for PCA and SPCA publication-title: Proc Conf Adv Neural Inf Process Syst – year: 2007 ident: ref4 article-title: Assessment of osteoporosis at the primary health care level – ident: ref8 doi: 10.1093/qjmed/hcn022 – ident: ref43 doi: 10.1109/TSMC.1979.4310076 – ident: ref52 doi: 10.1007/978-3-642-04271-3_98 – ident: ref37 doi: 10.1016/j.neuroimage.2006.01.015 – ident: ref21 doi: 10.1016/j.media.2014.11.012 – ident: ref50 doi: 10.1359/jbmr.1997.12.1.119 – ident: ref19 doi: 10.1088/0031-9155/43/3/013 – ident: ref24 doi: 10.1016/j.jocd.2007.12.010 – ident: ref27 doi: 10.1007/s00198-008-0665-4 – ident: ref15 doi: 10.1097/BOR.0000000000000183 – volume: 12 start-page: 101 year: 2012 ident: ref48 article-title: Medical management in the acute hip fracture patient: A comprehensive review for the internist publication-title: The Ochsner J – ident: ref16 doi: 10.1016/S0140-6736(05)66870-5 – ident: ref49 doi: 10.1007/s10439-010-0196-y – ident: ref46 doi: 10.1109/TVCG.2013.159 – ident: ref38 doi: 10.1109/TPAMI.2010.46 – ident: ref47 doi: 10.1002/jbmr.1693 – ident: ref31 doi: 10.1016/j.media.2015.06.001 – ident: ref53 doi: 10.1016/j.bone.2012.11.042 – ident: ref5 doi: 10.1093/oxfordjournals.aje.a116756 – ident: ref40 doi: 10.1007/BF02291478 – ident: ref22 doi: 10.1016/j.media.2010.01.003 |
SSID | ssj0014509 |
Score | 2.5137427 |
Snippet | The 3D distribution of the cortical and trabecular bone mass in the proximal femur is a critical component in determining fracture resistance that is not taken... |
SourceID | proquest pubmed crossref ieee |
SourceType | Aggregation Database Index Database Enrichment Source Publisher |
StartPage | 27 |
SubjectTerms | Absorptiometry, Photon Algorithms Biomedical materials Bone Density Bone mass Bone mineral density Bones Brain modeling Cancellous bone Compartments Computational modeling Computed tomography Correlation coefficients Cortical bone cortical thickness Critical components Density distribution Dual energy X-ray absorptiometry DXA Femur Fracture toughness Geographical variations Hip joint Humans image registration Imaging, Three-Dimensional Macrostructure Mathematical models Osteoporosis proximal femur Shape Solid modeling Thickness measurement Three-dimensional displays Tomography, X-Ray Computed Two dimensional models |
Title | 3D-DXA: Assessing the Femoral Shape, the Trabecular Macrostructure and the Cortex in 3D from DXA images |
URI | https://ieeexplore.ieee.org/document/7516699 https://www.ncbi.nlm.nih.gov/pubmed/27448343 https://www.proquest.com/docview/1855767701 https://www.proquest.com/docview/1826734557 |
Volume | 36 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3db9QwDLe2PSB42GAD1m2gIPGCtN61TZo0e5t2nAbS8cIm3VuVr8IE9CZ2JyH-euz0Q4AA8VY1TtrITvxz7NgAL1GFZ8o7m4bAXSpEk6VWaJ8GVSplfRDYj6It3snLa_F2WS634HS8CxNCiMFnYUKP0ZfvV25DR2VTVeZSar0N22i4dXe1Ro-BKLtwjoIyxmayGFySmZ5eLd5QDJecINTnXFDVIsqLV3HBf9FGsbzK35Fm1DjzPVgM_9oFmnyabNZ24r7_lsbxfyfzEHZ76MnOO1l5BFuh3YcHPyUk3Id7i97VfgAf-CydLc_PWOcWxmaGWJHNKTIXR3n_0dyG0_gK1Z3tSuyyhaF5xpS0m6-BmdZHiguK6P3GblrGZ4wutDAcmd18wb3s7jFcz19fXVymfVWG1HGh1qn21uA2IC032lpfIkcRdWVG2EZamyOc4q5qLCJP2RS20qIwvnSyLIStTM4NfwI77aoNh8C8qRrhq0Y7irOTzqCt7myZN7itFFUmEpgO3Kldn7KcKmd8rqPpkukaWVsTa-uetQm8Gnvcduk6_kF7QFwZ6XqGJHAyCEDdr-e7GlENGmZKZXkCL8ZmXInkXjFtWG2IppCKCyRM4GknOOPYg7wd_fmbx3C_ILgQj3ZOYAf5FJ4h2Fnb51HKfwATI_aW |
linkProvider | IEEE |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1Lb9QwEB6VIvE48GgLBFowUi9IzW4SO3bMrep2tYWmF7bS3iK_AlVLtqK7EuLXM3YeAkQRtyh-JNY39nz2jGcA9lGFJ8IaHTtHTcxYncSaSRs7kQuhrWPYzntbnPHZOfuwyBcbcDDchXHOBeczN_KPwZZvl2btj8rGIk85l_IO3EW9n2ftba3BZsDy1qEj8zFjE571RslEjufliffi4iMk-5Qyn7fIR8YrKKO_6aOQYOV2rhl0zvQxlP3ftq4ml6P1So_Mjz8COf7vcJ7Ao458ksNWWp7Chmu24OEvIQm34F7ZGdu34TOdxJPF4XvSGoaxmCBbJFPvm4u9fPqirt1BeIUKT7dJdkmp_DhDUNr1N0dUY0ONI-_T-51cNIROiL_SQrBncvEVV7ObHTifHs-PZnGXlyE2lIlVLK1WuBBwTZXU2uaIKfKuRDFdc61TJFTUFLVG7snrTBeSZcrmhucZ04VKqaLPYLNZNu4FEKuKmtmilsZ72nGjcLdudJ7WuLBkRcIiGPfoVKYLWu5zZ1xVYfOSyAqhrTy0VQdtBO-GFtdtwI5_1N32qAz1OkAi2O0FoOpm9E2FvAa3ZkIkaQRvh2Kci97Aohq3XPs6GRcURVNE8LwVnKHvXt5e_v2bb-D-bF6eVqcnZx9fwYPMk4dw0LMLm4iZ20Pqs9Kvg8T_BBxB-eA |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=3D-DXA%3A+Assessing+the+Femoral+Shape%2C+the+Trabecular+Macrostructure+and+the+Cortex+in+3D+from+DXA+images&rft.jtitle=IEEE+transactions+on+medical+imaging&rft.au=Humbert%2C+Ludovic&rft.au=Martelli%2C+Yves&rft.au=Fonolla%2C+Roger&rft.au=Steghofer%2C+Martin&rft.date=2017-01-01&rft.issn=1558-254X&rft.eissn=1558-254X&rft.volume=36&rft.issue=1&rft.spage=27&rft_id=info:doi/10.1109%2FTMI.2016.2593346&rft.externalDBID=NO_FULL_TEXT |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0278-0062&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0278-0062&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0278-0062&client=summon |