An automated approach for the optimised estimation of breast density with Dixon methods
Objective: To present and evaluate an automated method to correct scaling between Dixon water/fat images used in breast density (BD) assessments. Methods: Dixon images were acquired in 14 subjects with different T-1 weightings (flip angles, FA, 4 degrees/16 degrees). Our method corrects intensity di...
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| Veröffentlicht in: | British journal of radiology 2020-02, Vol.93 (1106), p.20190639, Article 20190639 |
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| container_issue | 1106 |
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| container_title | British journal of radiology |
| container_volume | 93 |
| creator | Goodburn, Rosie Kousi, Evanthia Macdonald, Alison Morgan, Veronica Scurr, Erica Reddy, Mamatha Wilkinson, Louise O'Flynn, Elizabeth Pope, Romney Allen, Steven Schmidt, Maria Angelica |
| description | Objective: To present and evaluate an automated method to correct scaling between Dixon water/fat images used in breast density (BD) assessments.
Methods: Dixon images were acquired in 14 subjects with different T-1 weightings (flip angles, FA, 4 degrees/16 degrees). Our method corrects intensity differences between water (W) and fat (F) images via the application of a uniform scaling factor (SF), determined subject-by-subject. Based on the postulation that optimal SFs yield relatively featureless summed fat/scaled-water (F + W-SF) images, each SF was chosen as that which generated the lowest 95th-percentile in the absolute spatial-gradient image-volume of F + W-SF Water-fraction maps were calculated for data acquired with low/high FAs, and BD (%) was the total percentage water within each breast volume.
Results: Corrected/uncorrected BD ranged from, respectively, 10.9-71.8%/8.9-66.7% for low-FA data to 8.1-74.3%/5.6-54.3% for high-FA data. Corrected metrics had an average absolute increase in BD of 6.4% for low-FA data and 18.4% for high-FA data. BD values estimated from low- and high-FA data were closer following SF-correction.
Conclusion: Our results demonstrate need for scaling in such BD assessments, where our method brought high-FA arid low-FA data into closer agreement.
Advances in knowledge: We demonstrated a feasible method to address a main source of inaccuracy in Dixon-based BD measurements. |
| doi_str_mv | 10.1259/bjr.20190639 |
| format | Article |
| fullrecord | <record><control><sourceid>pubmed_cross</sourceid><recordid>TN_cdi_crossref_primary_10_1259_bjr_20190639</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>31674798</sourcerecordid><originalsourceid>FETCH-LOGICAL-c351t-14f727670658e66f6dfdbf361c4fd9e570c85155b545649a887543dbc265bb723</originalsourceid><addsrcrecordid>eNqNkUlPAyEYhonRaF1ung13HYWZYbuYNHVNTLxo9DYBBhxMO0yAWv33UquN3jzxkXf5yAMAhxid4pKIM_UaTkuEBaKV2AAjzGpecI6eN8EIIcQKXHKyA3ZjfF1eiUDbYKfClNVM8BF4GvdQzpOfyWRaKIcheKk7aH2AqTPQD8nNXMySiXmSyfkeegtVMDIm2Jo-uvQBFy518MK9Z3FmUufbuA-2rJxGc_B97oHHq8uHyU1xd399OxnfFboiOBW4tqxklCFKuKHU0ta2ylYU69q2whCGNCeYEEVqQmshOWekrlqlS0qUYmW1B85XvcNczUyrTZ-CnDZDyI8NH42Xrvmr9K5rXvxbwxDJTSgXnKwKdPAxBmPXWYyaJeAmA25-AGf70e99a_MP0WzgK8PCKG-jdqbXZm3LX0CQqCkTeUJi4tIX0omf9ylHj_8frT4BjlKZgA</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>An automated approach for the optimised estimation of breast density with Dixon methods</title><source>MEDLINE</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>Oxford University Press Journals All Titles (1996-Current)</source><source>Web of Science - Science Citation Index Expanded - 2020<img src="https://exlibris-pub.s3.amazonaws.com/fromwos-v2.jpg" /></source><creator>Goodburn, Rosie ; Kousi, Evanthia ; Macdonald, Alison ; Morgan, Veronica ; Scurr, Erica ; Reddy, Mamatha ; Wilkinson, Louise ; O'Flynn, Elizabeth ; Pope, Romney ; Allen, Steven ; Schmidt, Maria Angelica</creator><creatorcontrib>Goodburn, Rosie ; Kousi, Evanthia ; Macdonald, Alison ; Morgan, Veronica ; Scurr, Erica ; Reddy, Mamatha ; Wilkinson, Louise ; O'Flynn, Elizabeth ; Pope, Romney ; Allen, Steven ; Schmidt, Maria Angelica</creatorcontrib><description>Objective: To present and evaluate an automated method to correct scaling between Dixon water/fat images used in breast density (BD) assessments.
Methods: Dixon images were acquired in 14 subjects with different T-1 weightings (flip angles, FA, 4 degrees/16 degrees). Our method corrects intensity differences between water (W) and fat (F) images via the application of a uniform scaling factor (SF), determined subject-by-subject. Based on the postulation that optimal SFs yield relatively featureless summed fat/scaled-water (F + W-SF) images, each SF was chosen as that which generated the lowest 95th-percentile in the absolute spatial-gradient image-volume of F + W-SF Water-fraction maps were calculated for data acquired with low/high FAs, and BD (%) was the total percentage water within each breast volume.
Results: Corrected/uncorrected BD ranged from, respectively, 10.9-71.8%/8.9-66.7% for low-FA data to 8.1-74.3%/5.6-54.3% for high-FA data. Corrected metrics had an average absolute increase in BD of 6.4% for low-FA data and 18.4% for high-FA data. BD values estimated from low- and high-FA data were closer following SF-correction.
Conclusion: Our results demonstrate need for scaling in such BD assessments, where our method brought high-FA arid low-FA data into closer agreement.
Advances in knowledge: We demonstrated a feasible method to address a main source of inaccuracy in Dixon-based BD measurements.</description><identifier>ISSN: 0007-1285</identifier><identifier>EISSN: 1748-880X</identifier><identifier>DOI: 10.1259/bjr.20190639</identifier><identifier>PMID: 31674798</identifier><language>eng</language><publisher>LONDON: British Inst Radiology</publisher><subject>Adipose Tissue ; Breast Density ; Breast Neoplasms - pathology ; Female ; Humans ; Life Sciences & Biomedicine ; Magnetic Resonance Imaging - methods ; Radiology, Nuclear Medicine & Medical Imaging ; Science & Technology ; Short Communication ; Water</subject><ispartof>British journal of radiology, 2020-02, Vol.93 (1106), p.20190639, Article 20190639</ispartof><rights>2020 The Authors. Published by the British Institute of Radiology 2020 The Authors</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>true</woscitedreferencessubscribed><woscitedreferencescount>2</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000509467900009</woscitedreferencesoriginalsourcerecordid><citedby>FETCH-LOGICAL-c351t-14f727670658e66f6dfdbf361c4fd9e570c85155b545649a887543dbc265bb723</citedby><cites>FETCH-LOGICAL-c351t-14f727670658e66f6dfdbf361c4fd9e570c85155b545649a887543dbc265bb723</cites><orcidid>0000-0001-5109-4738 ; 0000-0002-3735-7711</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,315,782,786,887,27931,27932,28255</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31674798$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Goodburn, Rosie</creatorcontrib><creatorcontrib>Kousi, Evanthia</creatorcontrib><creatorcontrib>Macdonald, Alison</creatorcontrib><creatorcontrib>Morgan, Veronica</creatorcontrib><creatorcontrib>Scurr, Erica</creatorcontrib><creatorcontrib>Reddy, Mamatha</creatorcontrib><creatorcontrib>Wilkinson, Louise</creatorcontrib><creatorcontrib>O'Flynn, Elizabeth</creatorcontrib><creatorcontrib>Pope, Romney</creatorcontrib><creatorcontrib>Allen, Steven</creatorcontrib><creatorcontrib>Schmidt, Maria Angelica</creatorcontrib><title>An automated approach for the optimised estimation of breast density with Dixon methods</title><title>British journal of radiology</title><addtitle>BRIT J RADIOL</addtitle><addtitle>Br J Radiol</addtitle><description>Objective: To present and evaluate an automated method to correct scaling between Dixon water/fat images used in breast density (BD) assessments.
Methods: Dixon images were acquired in 14 subjects with different T-1 weightings (flip angles, FA, 4 degrees/16 degrees). Our method corrects intensity differences between water (W) and fat (F) images via the application of a uniform scaling factor (SF), determined subject-by-subject. Based on the postulation that optimal SFs yield relatively featureless summed fat/scaled-water (F + W-SF) images, each SF was chosen as that which generated the lowest 95th-percentile in the absolute spatial-gradient image-volume of F + W-SF Water-fraction maps were calculated for data acquired with low/high FAs, and BD (%) was the total percentage water within each breast volume.
Results: Corrected/uncorrected BD ranged from, respectively, 10.9-71.8%/8.9-66.7% for low-FA data to 8.1-74.3%/5.6-54.3% for high-FA data. Corrected metrics had an average absolute increase in BD of 6.4% for low-FA data and 18.4% for high-FA data. BD values estimated from low- and high-FA data were closer following SF-correction.
Conclusion: Our results demonstrate need for scaling in such BD assessments, where our method brought high-FA arid low-FA data into closer agreement.
Advances in knowledge: We demonstrated a feasible method to address a main source of inaccuracy in Dixon-based BD measurements.</description><subject>Adipose Tissue</subject><subject>Breast Density</subject><subject>Breast Neoplasms - pathology</subject><subject>Female</subject><subject>Humans</subject><subject>Life Sciences & Biomedicine</subject><subject>Magnetic Resonance Imaging - methods</subject><subject>Radiology, Nuclear Medicine & Medical Imaging</subject><subject>Science & Technology</subject><subject>Short Communication</subject><subject>Water</subject><issn>0007-1285</issn><issn>1748-880X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>AOWDO</sourceid><sourceid>EIF</sourceid><recordid>eNqNkUlPAyEYhonRaF1ung13HYWZYbuYNHVNTLxo9DYBBhxMO0yAWv33UquN3jzxkXf5yAMAhxid4pKIM_UaTkuEBaKV2AAjzGpecI6eN8EIIcQKXHKyA3ZjfF1eiUDbYKfClNVM8BF4GvdQzpOfyWRaKIcheKk7aH2AqTPQD8nNXMySiXmSyfkeegtVMDIm2Jo-uvQBFy518MK9Z3FmUufbuA-2rJxGc_B97oHHq8uHyU1xd399OxnfFboiOBW4tqxklCFKuKHU0ta2ylYU69q2whCGNCeYEEVqQmshOWekrlqlS0qUYmW1B85XvcNczUyrTZ-CnDZDyI8NH42Xrvmr9K5rXvxbwxDJTSgXnKwKdPAxBmPXWYyaJeAmA25-AGf70e99a_MP0WzgK8PCKG-jdqbXZm3LX0CQqCkTeUJi4tIX0omf9ylHj_8frT4BjlKZgA</recordid><startdate>20200201</startdate><enddate>20200201</enddate><creator>Goodburn, Rosie</creator><creator>Kousi, Evanthia</creator><creator>Macdonald, Alison</creator><creator>Morgan, Veronica</creator><creator>Scurr, Erica</creator><creator>Reddy, Mamatha</creator><creator>Wilkinson, Louise</creator><creator>O'Flynn, Elizabeth</creator><creator>Pope, Romney</creator><creator>Allen, Steven</creator><creator>Schmidt, Maria Angelica</creator><general>British Inst Radiology</general><general>The British Institute of Radiology</general><scope>AOWDO</scope><scope>BLEPL</scope><scope>DTL</scope><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>5PM</scope><orcidid>https://orcid.org/0000-0001-5109-4738</orcidid><orcidid>https://orcid.org/0000-0002-3735-7711</orcidid></search><sort><creationdate>20200201</creationdate><title>An automated approach for the optimised estimation of breast density with Dixon methods</title><author>Goodburn, Rosie ; Kousi, Evanthia ; Macdonald, Alison ; Morgan, Veronica ; Scurr, Erica ; Reddy, Mamatha ; Wilkinson, Louise ; O'Flynn, Elizabeth ; Pope, Romney ; Allen, Steven ; Schmidt, Maria Angelica</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c351t-14f727670658e66f6dfdbf361c4fd9e570c85155b545649a887543dbc265bb723</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Adipose Tissue</topic><topic>Breast Density</topic><topic>Breast Neoplasms - pathology</topic><topic>Female</topic><topic>Humans</topic><topic>Life Sciences & Biomedicine</topic><topic>Magnetic Resonance Imaging - methods</topic><topic>Radiology, Nuclear Medicine & Medical Imaging</topic><topic>Science & Technology</topic><topic>Short Communication</topic><topic>Water</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Goodburn, Rosie</creatorcontrib><creatorcontrib>Kousi, Evanthia</creatorcontrib><creatorcontrib>Macdonald, Alison</creatorcontrib><creatorcontrib>Morgan, Veronica</creatorcontrib><creatorcontrib>Scurr, Erica</creatorcontrib><creatorcontrib>Reddy, Mamatha</creatorcontrib><creatorcontrib>Wilkinson, Louise</creatorcontrib><creatorcontrib>O'Flynn, Elizabeth</creatorcontrib><creatorcontrib>Pope, Romney</creatorcontrib><creatorcontrib>Allen, Steven</creatorcontrib><creatorcontrib>Schmidt, Maria Angelica</creatorcontrib><collection>Web of Science - Science Citation Index Expanded - 2020</collection><collection>Web of Science Core Collection</collection><collection>Science Citation Index Expanded</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>British journal of radiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Goodburn, Rosie</au><au>Kousi, Evanthia</au><au>Macdonald, Alison</au><au>Morgan, Veronica</au><au>Scurr, Erica</au><au>Reddy, Mamatha</au><au>Wilkinson, Louise</au><au>O'Flynn, Elizabeth</au><au>Pope, Romney</au><au>Allen, Steven</au><au>Schmidt, Maria Angelica</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An automated approach for the optimised estimation of breast density with Dixon methods</atitle><jtitle>British journal of radiology</jtitle><stitle>BRIT J RADIOL</stitle><addtitle>Br J Radiol</addtitle><date>2020-02-01</date><risdate>2020</risdate><volume>93</volume><issue>1106</issue><spage>20190639</spage><pages>20190639-</pages><artnum>20190639</artnum><issn>0007-1285</issn><eissn>1748-880X</eissn><abstract>Objective: To present and evaluate an automated method to correct scaling between Dixon water/fat images used in breast density (BD) assessments.
Methods: Dixon images were acquired in 14 subjects with different T-1 weightings (flip angles, FA, 4 degrees/16 degrees). Our method corrects intensity differences between water (W) and fat (F) images via the application of a uniform scaling factor (SF), determined subject-by-subject. Based on the postulation that optimal SFs yield relatively featureless summed fat/scaled-water (F + W-SF) images, each SF was chosen as that which generated the lowest 95th-percentile in the absolute spatial-gradient image-volume of F + W-SF Water-fraction maps were calculated for data acquired with low/high FAs, and BD (%) was the total percentage water within each breast volume.
Results: Corrected/uncorrected BD ranged from, respectively, 10.9-71.8%/8.9-66.7% for low-FA data to 8.1-74.3%/5.6-54.3% for high-FA data. Corrected metrics had an average absolute increase in BD of 6.4% for low-FA data and 18.4% for high-FA data. BD values estimated from low- and high-FA data were closer following SF-correction.
Conclusion: Our results demonstrate need for scaling in such BD assessments, where our method brought high-FA arid low-FA data into closer agreement.
Advances in knowledge: We demonstrated a feasible method to address a main source of inaccuracy in Dixon-based BD measurements.</abstract><cop>LONDON</cop><pub>British Inst Radiology</pub><pmid>31674798</pmid><doi>10.1259/bjr.20190639</doi><tpages>7</tpages><orcidid>https://orcid.org/0000-0001-5109-4738</orcidid><orcidid>https://orcid.org/0000-0002-3735-7711</orcidid><oa>free_for_read</oa></addata></record> |
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| source | MEDLINE; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Oxford University Press Journals All Titles (1996-Current); Web of Science - Science Citation Index Expanded - 2020<img src="https://exlibris-pub.s3.amazonaws.com/fromwos-v2.jpg" /> |
| subjects | Adipose Tissue Breast Density Breast Neoplasms - pathology Female Humans Life Sciences & Biomedicine Magnetic Resonance Imaging - methods Radiology, Nuclear Medicine & Medical Imaging Science & Technology Short Communication Water |
| title | An automated approach for the optimised estimation of breast density with Dixon methods |
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