A new algorithm for estimating the rod volume fraction and the trabecular thickness from in vivo computed tomography
Purpose: Existing microstructure parameters are able to predict vertebral in vitro failure load, but for noisy in vivo data more complex algorithms are needed for a robust assessment. Methods: A new algorithm is proposed for the microstructural analysis of trabecular bone under in vivo quantitative...
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| creator | Thomsen, Felix Sebastian Leo Peña, Jaime Andrés Lu, Yongtao Huber, Gerd Morlock, Michael Glüer, Claus-Christian Delrieux, Claudio Augusto |
| description | Purpose:
Existing microstructure parameters are able to predict vertebral in vitro failure load, but for noisy in vivo data more complex algorithms are needed for a robust assessment.
Methods:
A new algorithm is proposed for the microstructural analysis of trabecular bone under in vivo quantitative computed tomography (QCT). Five fractal parameters are computed: (1) the average local fractal dimension FD, (2) its standard deviation FD.SD, (3) the fractal rod volume ratio fRV/BV, (4) the average fractal trabecular thickness fTb.Th, and (5) its coefficient of variation fTb.Th.CV. The algorithm requires neither an explicit skeletonization of the trabecular bone, nor a well-defined transition between bone and marrow phases. Two experiments were conducted to compare the fractal with established microstructural parameters. In the first, 20 volumes-of-interest of embedded vertebrae phantoms were scanned five times under QCT and high-resolution (HR-)QCT and once under peripheral HRQCT (HRpQCT), to derive accuracy and precision. In the second experiment, correlations between in vitro HRQCT structural parameters were obtained from 76 human T
11, T
12, or L
1 vertebrae. In vitro fracture data were available for a subset of 17 human T12 vertebrae so that linear regression models between failure load and microstructural HRQCT parameters could be analyzed.
Results:
The results showed correlations of fTb.Th and fRV/BV with their nonfractal pendants trabecular thickness (Tb.Th) and respective structure model index (SMI) while higher precision and accuracy was observed on the fractal measures. Linear models of bone mineral density with two and three fractal microstructural HRQCT parameters explained 86% and 90% (adjusted R
2) of the failure load and significantly improved the linear models based only on BMD and established standard microstructural parameters (68%–77% adjusted R
2).
Conclusions:
The application of fractal methods may grant further insight into the study of bone quality in vivo when image resolution and quality are less than optimal for current standard methods. |
| doi_str_mv | 10.1118/1.4967479 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_crossref_primary_10_1118_1_4967479</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1845825496</sourcerecordid><originalsourceid>FETCH-LOGICAL-c4289-e8f878dde11c53a3d4a474e5ec3a43ef251c67a707347dce348f9fead2e6a73e3</originalsourceid><addsrcrecordid>eNp90EFPHCEYBmDS1NTV9uAfMBy1ySgMsDBHY7Q20eihPU8QPnbRmWEEZs3--6K7NU2MPZEvPLz5eBE6oOSEUqpO6Qlv5pLL5hOa1Vyyitek-YxmhDS8qjkRu2gvpQdCyJwJ8gXt1rIhigoxQ_kMD_CMdbcI0edlj12IGFL2vc5-WOC8BByDxavQTT1gF7XJPgxYD_b1Lkd9D2bqdCyjN48DpFRU6LEf8MqvAjahH6cMhYc-LKIel-uvaMfpLsG37bmPfl9e_Dq_qq5vf_w8P7uuDK9VU4FySiprgVIjmGaWay45CDBMcwauFtTMpZZEMi6tAcaVaxxoW8NcSwZsHx1tcscYnqbyq7b3yUDX6QHClFqquFC1KOUVeryhJoaUIrh2jKWDuG4paV9Kbmm7LbnYw23sdN-DfZN_Wy2g2oBn38H646T25m4b-H3jk_FZv_T79mYV4j9-tO5_-P2qfwAQq6MF</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1845825496</pqid></control><display><type>article</type><title>A new algorithm for estimating the rod volume fraction and the trabecular thickness from in vivo computed tomography</title><source>MEDLINE</source><source>Wiley Online Library Journals Frontfile Complete</source><source>Alma/SFX Local Collection</source><creator>Thomsen, Felix Sebastian Leo ; Peña, Jaime Andrés ; Lu, Yongtao ; Huber, Gerd ; Morlock, Michael ; Glüer, Claus-Christian ; Delrieux, Claudio Augusto</creator><creatorcontrib>Thomsen, Felix Sebastian Leo ; Peña, Jaime Andrés ; Lu, Yongtao ; Huber, Gerd ; Morlock, Michael ; Glüer, Claus-Christian ; Delrieux, Claudio Augusto</creatorcontrib><description>Purpose:
Existing microstructure parameters are able to predict vertebral in vitro failure load, but for noisy in vivo data more complex algorithms are needed for a robust assessment.
Methods:
A new algorithm is proposed for the microstructural analysis of trabecular bone under in vivo quantitative computed tomography (QCT). Five fractal parameters are computed: (1) the average local fractal dimension FD, (2) its standard deviation FD.SD, (3) the fractal rod volume ratio fRV/BV, (4) the average fractal trabecular thickness fTb.Th, and (5) its coefficient of variation fTb.Th.CV. The algorithm requires neither an explicit skeletonization of the trabecular bone, nor a well-defined transition between bone and marrow phases. Two experiments were conducted to compare the fractal with established microstructural parameters. In the first, 20 volumes-of-interest of embedded vertebrae phantoms were scanned five times under QCT and high-resolution (HR-)QCT and once under peripheral HRQCT (HRpQCT), to derive accuracy and precision. In the second experiment, correlations between in vitro HRQCT structural parameters were obtained from 76 human T
11, T
12, or L
1 vertebrae. In vitro fracture data were available for a subset of 17 human T12 vertebrae so that linear regression models between failure load and microstructural HRQCT parameters could be analyzed.
Results:
The results showed correlations of fTb.Th and fRV/BV with their nonfractal pendants trabecular thickness (Tb.Th) and respective structure model index (SMI) while higher precision and accuracy was observed on the fractal measures. Linear models of bone mineral density with two and three fractal microstructural HRQCT parameters explained 86% and 90% (adjusted R
2) of the failure load and significantly improved the linear models based only on BMD and established standard microstructural parameters (68%–77% adjusted R
2).
Conclusions:
The application of fractal methods may grant further insight into the study of bone quality in vivo when image resolution and quality are less than optimal for current standard methods.</description><identifier>ISSN: 0094-2405</identifier><identifier>EISSN: 2473-4209</identifier><identifier>DOI: 10.1118/1.4967479</identifier><identifier>PMID: 27908155</identifier><identifier>CODEN: MPHYA6</identifier><language>eng</language><publisher>United States: American Association of Physicists in Medicine</publisher><subject>Algorithms ; Biological material, e.g. blood, urine; Haemocytometers ; bone ; Bone Density ; Calibration ; Cancellous Bone - anatomy & histology ; Cancellous Bone - diagnostic imaging ; Cancellous Bone - physiology ; Computed tomography ; Computer modeling ; Computerised tomographs ; computerised tomography ; Data analysis ; Digital computing or data processing equipment or methods, specially adapted for specific applications ; Erosion or dilatation, e.g. thinning ; Failure analysis ; failure load ; Fractals ; Humans ; Image data processing or generation, in general ; Image Processing, Computer-Assisted - methods ; image resolution ; image thinning ; Linear regression ; local fractal dimension ; Medical image noise ; medical image processing ; phantoms ; Probability theory, stochastic processes, and statistics ; QCT ; Regression Analysis ; rod volume ratio ; Spatial resolution ; Spine - anatomy & histology ; Spine - diagnostic imaging ; Spine - physiology ; Structural failure ; Tomography, X-Ray Computed ; trabecular thickness ; Weight-Bearing</subject><ispartof>Medical physics (Lancaster), 2016-12, Vol.43 (12), p.6598-6607</ispartof><rights>American Association of Physicists in Medicine</rights><rights>2016 American Association of Physicists in Medicine</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4289-e8f878dde11c53a3d4a474e5ec3a43ef251c67a707347dce348f9fead2e6a73e3</citedby><cites>FETCH-LOGICAL-c4289-e8f878dde11c53a3d4a474e5ec3a43ef251c67a707347dce348f9fead2e6a73e3</cites><orcidid>0000-0002-2781-1765</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1118%2F1.4967479$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1118%2F1.4967479$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1416,27915,27916,45565,45566</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/27908155$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Thomsen, Felix Sebastian Leo</creatorcontrib><creatorcontrib>Peña, Jaime Andrés</creatorcontrib><creatorcontrib>Lu, Yongtao</creatorcontrib><creatorcontrib>Huber, Gerd</creatorcontrib><creatorcontrib>Morlock, Michael</creatorcontrib><creatorcontrib>Glüer, Claus-Christian</creatorcontrib><creatorcontrib>Delrieux, Claudio Augusto</creatorcontrib><title>A new algorithm for estimating the rod volume fraction and the trabecular thickness from in vivo computed tomography</title><title>Medical physics (Lancaster)</title><addtitle>Med Phys</addtitle><description>Purpose:
Existing microstructure parameters are able to predict vertebral in vitro failure load, but for noisy in vivo data more complex algorithms are needed for a robust assessment.
Methods:
A new algorithm is proposed for the microstructural analysis of trabecular bone under in vivo quantitative computed tomography (QCT). Five fractal parameters are computed: (1) the average local fractal dimension FD, (2) its standard deviation FD.SD, (3) the fractal rod volume ratio fRV/BV, (4) the average fractal trabecular thickness fTb.Th, and (5) its coefficient of variation fTb.Th.CV. The algorithm requires neither an explicit skeletonization of the trabecular bone, nor a well-defined transition between bone and marrow phases. Two experiments were conducted to compare the fractal with established microstructural parameters. In the first, 20 volumes-of-interest of embedded vertebrae phantoms were scanned five times under QCT and high-resolution (HR-)QCT and once under peripheral HRQCT (HRpQCT), to derive accuracy and precision. In the second experiment, correlations between in vitro HRQCT structural parameters were obtained from 76 human T
11, T
12, or L
1 vertebrae. In vitro fracture data were available for a subset of 17 human T12 vertebrae so that linear regression models between failure load and microstructural HRQCT parameters could be analyzed.
Results:
The results showed correlations of fTb.Th and fRV/BV with their nonfractal pendants trabecular thickness (Tb.Th) and respective structure model index (SMI) while higher precision and accuracy was observed on the fractal measures. Linear models of bone mineral density with two and three fractal microstructural HRQCT parameters explained 86% and 90% (adjusted R
2) of the failure load and significantly improved the linear models based only on BMD and established standard microstructural parameters (68%–77% adjusted R
2).
Conclusions:
The application of fractal methods may grant further insight into the study of bone quality in vivo when image resolution and quality are less than optimal for current standard methods.</description><subject>Algorithms</subject><subject>Biological material, e.g. blood, urine; Haemocytometers</subject><subject>bone</subject><subject>Bone Density</subject><subject>Calibration</subject><subject>Cancellous Bone - anatomy & histology</subject><subject>Cancellous Bone - diagnostic imaging</subject><subject>Cancellous Bone - physiology</subject><subject>Computed tomography</subject><subject>Computer modeling</subject><subject>Computerised tomographs</subject><subject>computerised tomography</subject><subject>Data analysis</subject><subject>Digital computing or data processing equipment or methods, specially adapted for specific applications</subject><subject>Erosion or dilatation, e.g. thinning</subject><subject>Failure analysis</subject><subject>failure load</subject><subject>Fractals</subject><subject>Humans</subject><subject>Image data processing or generation, in general</subject><subject>Image Processing, Computer-Assisted - methods</subject><subject>image resolution</subject><subject>image thinning</subject><subject>Linear regression</subject><subject>local fractal dimension</subject><subject>Medical image noise</subject><subject>medical image processing</subject><subject>phantoms</subject><subject>Probability theory, stochastic processes, and statistics</subject><subject>QCT</subject><subject>Regression Analysis</subject><subject>rod volume ratio</subject><subject>Spatial resolution</subject><subject>Spine - anatomy & histology</subject><subject>Spine - diagnostic imaging</subject><subject>Spine - physiology</subject><subject>Structural failure</subject><subject>Tomography, X-Ray Computed</subject><subject>trabecular thickness</subject><subject>Weight-Bearing</subject><issn>0094-2405</issn><issn>2473-4209</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp90EFPHCEYBmDS1NTV9uAfMBy1ySgMsDBHY7Q20eihPU8QPnbRmWEEZs3--6K7NU2MPZEvPLz5eBE6oOSEUqpO6Qlv5pLL5hOa1Vyyitek-YxmhDS8qjkRu2gvpQdCyJwJ8gXt1rIhigoxQ_kMD_CMdbcI0edlj12IGFL2vc5-WOC8BByDxavQTT1gF7XJPgxYD_b1Lkd9D2bqdCyjN48DpFRU6LEf8MqvAjahH6cMhYc-LKIel-uvaMfpLsG37bmPfl9e_Dq_qq5vf_w8P7uuDK9VU4FySiprgVIjmGaWay45CDBMcwauFtTMpZZEMi6tAcaVaxxoW8NcSwZsHx1tcscYnqbyq7b3yUDX6QHClFqquFC1KOUVeryhJoaUIrh2jKWDuG4paV9Kbmm7LbnYw23sdN-DfZN_Wy2g2oBn38H646T25m4b-H3jk_FZv_T79mYV4j9-tO5_-P2qfwAQq6MF</recordid><startdate>201612</startdate><enddate>201612</enddate><creator>Thomsen, Felix Sebastian Leo</creator><creator>Peña, Jaime Andrés</creator><creator>Lu, Yongtao</creator><creator>Huber, Gerd</creator><creator>Morlock, Michael</creator><creator>Glüer, Claus-Christian</creator><creator>Delrieux, Claudio Augusto</creator><general>American Association of Physicists in Medicine</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-2781-1765</orcidid></search><sort><creationdate>201612</creationdate><title>A new algorithm for estimating the rod volume fraction and the trabecular thickness from in vivo computed tomography</title><author>Thomsen, Felix Sebastian Leo ; Peña, Jaime Andrés ; Lu, Yongtao ; Huber, Gerd ; Morlock, Michael ; Glüer, Claus-Christian ; Delrieux, Claudio Augusto</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4289-e8f878dde11c53a3d4a474e5ec3a43ef251c67a707347dce348f9fead2e6a73e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Algorithms</topic><topic>Biological material, e.g. blood, urine; Haemocytometers</topic><topic>bone</topic><topic>Bone Density</topic><topic>Calibration</topic><topic>Cancellous Bone - anatomy & histology</topic><topic>Cancellous Bone - diagnostic imaging</topic><topic>Cancellous Bone - physiology</topic><topic>Computed tomography</topic><topic>Computer modeling</topic><topic>Computerised tomographs</topic><topic>computerised tomography</topic><topic>Data analysis</topic><topic>Digital computing or data processing equipment or methods, specially adapted for specific applications</topic><topic>Erosion or dilatation, e.g. thinning</topic><topic>Failure analysis</topic><topic>failure load</topic><topic>Fractals</topic><topic>Humans</topic><topic>Image data processing or generation, in general</topic><topic>Image Processing, Computer-Assisted - methods</topic><topic>image resolution</topic><topic>image thinning</topic><topic>Linear regression</topic><topic>local fractal dimension</topic><topic>Medical image noise</topic><topic>medical image processing</topic><topic>phantoms</topic><topic>Probability theory, stochastic processes, and statistics</topic><topic>QCT</topic><topic>Regression Analysis</topic><topic>rod volume ratio</topic><topic>Spatial resolution</topic><topic>Spine - anatomy & histology</topic><topic>Spine - diagnostic imaging</topic><topic>Spine - physiology</topic><topic>Structural failure</topic><topic>Tomography, X-Ray Computed</topic><topic>trabecular thickness</topic><topic>Weight-Bearing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Thomsen, Felix Sebastian Leo</creatorcontrib><creatorcontrib>Peña, Jaime Andrés</creatorcontrib><creatorcontrib>Lu, Yongtao</creatorcontrib><creatorcontrib>Huber, Gerd</creatorcontrib><creatorcontrib>Morlock, Michael</creatorcontrib><creatorcontrib>Glüer, Claus-Christian</creatorcontrib><creatorcontrib>Delrieux, Claudio Augusto</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Medical physics (Lancaster)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Thomsen, Felix Sebastian Leo</au><au>Peña, Jaime Andrés</au><au>Lu, Yongtao</au><au>Huber, Gerd</au><au>Morlock, Michael</au><au>Glüer, Claus-Christian</au><au>Delrieux, Claudio Augusto</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A new algorithm for estimating the rod volume fraction and the trabecular thickness from in vivo computed tomography</atitle><jtitle>Medical physics (Lancaster)</jtitle><addtitle>Med Phys</addtitle><date>2016-12</date><risdate>2016</risdate><volume>43</volume><issue>12</issue><spage>6598</spage><epage>6607</epage><pages>6598-6607</pages><issn>0094-2405</issn><eissn>2473-4209</eissn><coden>MPHYA6</coden><abstract>Purpose:
Existing microstructure parameters are able to predict vertebral in vitro failure load, but for noisy in vivo data more complex algorithms are needed for a robust assessment.
Methods:
A new algorithm is proposed for the microstructural analysis of trabecular bone under in vivo quantitative computed tomography (QCT). Five fractal parameters are computed: (1) the average local fractal dimension FD, (2) its standard deviation FD.SD, (3) the fractal rod volume ratio fRV/BV, (4) the average fractal trabecular thickness fTb.Th, and (5) its coefficient of variation fTb.Th.CV. The algorithm requires neither an explicit skeletonization of the trabecular bone, nor a well-defined transition between bone and marrow phases. Two experiments were conducted to compare the fractal with established microstructural parameters. In the first, 20 volumes-of-interest of embedded vertebrae phantoms were scanned five times under QCT and high-resolution (HR-)QCT and once under peripheral HRQCT (HRpQCT), to derive accuracy and precision. In the second experiment, correlations between in vitro HRQCT structural parameters were obtained from 76 human T
11, T
12, or L
1 vertebrae. In vitro fracture data were available for a subset of 17 human T12 vertebrae so that linear regression models between failure load and microstructural HRQCT parameters could be analyzed.
Results:
The results showed correlations of fTb.Th and fRV/BV with their nonfractal pendants trabecular thickness (Tb.Th) and respective structure model index (SMI) while higher precision and accuracy was observed on the fractal measures. Linear models of bone mineral density with two and three fractal microstructural HRQCT parameters explained 86% and 90% (adjusted R
2) of the failure load and significantly improved the linear models based only on BMD and established standard microstructural parameters (68%–77% adjusted R
2).
Conclusions:
The application of fractal methods may grant further insight into the study of bone quality in vivo when image resolution and quality are less than optimal for current standard methods.</abstract><cop>United States</cop><pub>American Association of Physicists in Medicine</pub><pmid>27908155</pmid><doi>10.1118/1.4967479</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0002-2781-1765</orcidid><oa>free_for_read</oa></addata></record> |
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| source | MEDLINE; Wiley Online Library Journals Frontfile Complete; Alma/SFX Local Collection |
| subjects | Algorithms Biological material, e.g. blood, urine Haemocytometers bone Bone Density Calibration Cancellous Bone - anatomy & histology Cancellous Bone - diagnostic imaging Cancellous Bone - physiology Computed tomography Computer modeling Computerised tomographs computerised tomography Data analysis Digital computing or data processing equipment or methods, specially adapted for specific applications Erosion or dilatation, e.g. thinning Failure analysis failure load Fractals Humans Image data processing or generation, in general Image Processing, Computer-Assisted - methods image resolution image thinning Linear regression local fractal dimension Medical image noise medical image processing phantoms Probability theory, stochastic processes, and statistics QCT Regression Analysis rod volume ratio Spatial resolution Spine - anatomy & histology Spine - diagnostic imaging Spine - physiology Structural failure Tomography, X-Ray Computed trabecular thickness Weight-Bearing |
| title | A new algorithm for estimating the rod volume fraction and the trabecular thickness from in vivo computed tomography |
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