Technical Note: Proof of concept for radiomics‐based quality assurance for computed tomography
Purpose Routine quality assurance (QA) testing to identify malfunctions in medical imaging devices is a standard practice and plays an important role in meeting quality standards. However, current daily computed tomography (CT) QA techniques have proven to be inadequate for the detection of subtle a...
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| Veröffentlicht in: | Journal of applied clinical medical physics 2019-11, Vol.20 (11), p.199-205 |
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| creator | Branco, Luciano R. F. Ger, Rachel B. Mackin, Dennis S. Zhou, Shouhao Court, Laurence E. Layman, Rick R. |
| description | Purpose
Routine quality assurance (QA) testing to identify malfunctions in medical imaging devices is a standard practice and plays an important role in meeting quality standards. However, current daily computed tomography (CT) QA techniques have proven to be inadequate for the detection of subtle artifacts on scans. Therefore, we investigated the ability of a radiomics phantom to detect subtle artifacts not detected in conventional daily QA.
Methods
An updated credence cartridge radiomics phantom was used in this study, with a focus on two of the cartridges (rubber and cork) in the phantom. The phantom was scanned using a Siemens Definition Flash CT scanner, which was reported to produce a subtle line pattern artifact. Images were then imported into the IBEX software program, and 49 features were extracted from the two cartridges using four different preprocessing techniques. Each feature was then compared with features for the same scanner several months previously and with features from controlled CT scans obtained using 100 scanners.
Results
Of 196 total features for the test scanner, 79 (40%) from the rubber cartridge and 70 (36%) from the cork cartridge were three or more standard deviations away from the mean of the controlled scan population data. Feature values for the artifact‐producing scanner were closer to the population mean when features were preprocessed with Butterworth smoothing. The feature most sensitive to the artifact was co‐occurrence matrix maximum probability. The deviation from the mean for this feature was more than seven times greater when the scanner was malfunctioning (7.56 versus 1.01).
Conclusions
Radiomics features extracted from a texture phantom were able to identify an artifact‐producing scanner as an outlier among 100 CT scanners. This preliminary analysis demonstrated the potential of radiomics in CT QA to identify subtle artifacts not detected using the currently employed daily QA techniques. |
| doi_str_mv | 10.1002/acm2.12750 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2305476306</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2305476306</sourcerecordid><originalsourceid>FETCH-LOGICAL-c4210-7b5499a037f804ee7de4cbb326f623365fb03b2528d6a02c84e259f5e5a8e7853</originalsourceid><addsrcrecordid>eNp90NtKwzAcx_EgipvTGx9ACt6IsJlDk7bejeEJ5uFiXtc0_dd1tE2XtMjufASf0ScxW6eIF0IhhXz4Eb4IHRM8IhjTC6lKOiI04HgH9QmnYhhFxN_99d9DB9YuMCYkZOE-6jEicIQD0UcvM1DzKley8B50A5fek9E689yndKWgbrxMG8_INNdlruzn-0ciLaTespVF3qw8aW1rpJMbp3RZt427bnSpX42s56tDtJfJwsLR9hyg5-ur2eR2OH28uZuMp0PlU4KHQcL9KJKYBVmIfYAgBV8lCaMiE5QxwbMEs4RyGqZCYqpCHyiPMg5chhCEnA3QWbdbG71swTZxmVsFRSEr0K2NKcPcDwTDwtHTP3ShW1O51znlOgrCXacBOu-UMtpaA1lcm7yUZhUTHK-7x-vu8aa7wyfbyTYpIf2h36EdIB14ywtY_TMVjyf3tBv9AuH1jQA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2312761518</pqid></control><display><type>article</type><title>Technical Note: Proof of concept for radiomics‐based quality assurance for computed tomography</title><source>MEDLINE</source><source>DOAJ Directory of Open Access Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>Wiley-Blackwell Open Access Titles</source><source>Wiley Online Library All Journals</source><source>PubMed Central</source><creator>Branco, Luciano R. F. ; Ger, Rachel B. ; Mackin, Dennis S. ; Zhou, Shouhao ; Court, Laurence E. ; Layman, Rick R.</creator><creatorcontrib>Branco, Luciano R. F. ; Ger, Rachel B. ; Mackin, Dennis S. ; Zhou, Shouhao ; Court, Laurence E. ; Layman, Rick R.</creatorcontrib><description>Purpose
Routine quality assurance (QA) testing to identify malfunctions in medical imaging devices is a standard practice and plays an important role in meeting quality standards. However, current daily computed tomography (CT) QA techniques have proven to be inadequate for the detection of subtle artifacts on scans. Therefore, we investigated the ability of a radiomics phantom to detect subtle artifacts not detected in conventional daily QA.
Methods
An updated credence cartridge radiomics phantom was used in this study, with a focus on two of the cartridges (rubber and cork) in the phantom. The phantom was scanned using a Siemens Definition Flash CT scanner, which was reported to produce a subtle line pattern artifact. Images were then imported into the IBEX software program, and 49 features were extracted from the two cartridges using four different preprocessing techniques. Each feature was then compared with features for the same scanner several months previously and with features from controlled CT scans obtained using 100 scanners.
Results
Of 196 total features for the test scanner, 79 (40%) from the rubber cartridge and 70 (36%) from the cork cartridge were three or more standard deviations away from the mean of the controlled scan population data. Feature values for the artifact‐producing scanner were closer to the population mean when features were preprocessed with Butterworth smoothing. The feature most sensitive to the artifact was co‐occurrence matrix maximum probability. The deviation from the mean for this feature was more than seven times greater when the scanner was malfunctioning (7.56 versus 1.01).
Conclusions
Radiomics features extracted from a texture phantom were able to identify an artifact‐producing scanner as an outlier among 100 CT scanners. This preliminary analysis demonstrated the potential of radiomics in CT QA to identify subtle artifacts not detected using the currently employed daily QA techniques.</description><identifier>ISSN: 1526-9914</identifier><identifier>EISSN: 1526-9914</identifier><identifier>DOI: 10.1002/acm2.12750</identifier><identifier>PMID: 31609076</identifier><language>eng</language><publisher>United States: John Wiley & Sons, Inc</publisher><subject>Algorithms ; Anthropomorphism ; Cancer ; Feasibility studies ; Humans ; Image Processing, Computer-Assisted - methods ; Lymphoma ; Lymphoma - diagnostic imaging ; Medical imaging ; Patients ; Phantoms, Imaging ; Population ; Quality Assurance, Health Care - standards ; Quality control ; quantitative imaging ; Radiomics ; Scanners ; Tomography ; Tomography Scanners, X-Ray Computed - standards ; Tomography, X-Ray Computed - instrumentation ; Tomography, X-Ray Computed - methods</subject><ispartof>Journal of applied clinical medical physics, 2019-11, Vol.20 (11), p.199-205</ispartof><rights>2019 The Authors. published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.</rights><rights>2019 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.</rights><rights>2019. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4210-7b5499a037f804ee7de4cbb326f623365fb03b2528d6a02c84e259f5e5a8e7853</citedby><cites>FETCH-LOGICAL-c4210-7b5499a037f804ee7de4cbb326f623365fb03b2528d6a02c84e259f5e5a8e7853</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Facm2.12750$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Facm2.12750$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,864,1416,11561,27923,27924,45573,45574,46051,46475</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31609076$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Branco, Luciano R. F.</creatorcontrib><creatorcontrib>Ger, Rachel B.</creatorcontrib><creatorcontrib>Mackin, Dennis S.</creatorcontrib><creatorcontrib>Zhou, Shouhao</creatorcontrib><creatorcontrib>Court, Laurence E.</creatorcontrib><creatorcontrib>Layman, Rick R.</creatorcontrib><title>Technical Note: Proof of concept for radiomics‐based quality assurance for computed tomography</title><title>Journal of applied clinical medical physics</title><addtitle>J Appl Clin Med Phys</addtitle><description>Purpose
Routine quality assurance (QA) testing to identify malfunctions in medical imaging devices is a standard practice and plays an important role in meeting quality standards. However, current daily computed tomography (CT) QA techniques have proven to be inadequate for the detection of subtle artifacts on scans. Therefore, we investigated the ability of a radiomics phantom to detect subtle artifacts not detected in conventional daily QA.
Methods
An updated credence cartridge radiomics phantom was used in this study, with a focus on two of the cartridges (rubber and cork) in the phantom. The phantom was scanned using a Siemens Definition Flash CT scanner, which was reported to produce a subtle line pattern artifact. Images were then imported into the IBEX software program, and 49 features were extracted from the two cartridges using four different preprocessing techniques. Each feature was then compared with features for the same scanner several months previously and with features from controlled CT scans obtained using 100 scanners.
Results
Of 196 total features for the test scanner, 79 (40%) from the rubber cartridge and 70 (36%) from the cork cartridge were three or more standard deviations away from the mean of the controlled scan population data. Feature values for the artifact‐producing scanner were closer to the population mean when features were preprocessed with Butterworth smoothing. The feature most sensitive to the artifact was co‐occurrence matrix maximum probability. The deviation from the mean for this feature was more than seven times greater when the scanner was malfunctioning (7.56 versus 1.01).
Conclusions
Radiomics features extracted from a texture phantom were able to identify an artifact‐producing scanner as an outlier among 100 CT scanners. This preliminary analysis demonstrated the potential of radiomics in CT QA to identify subtle artifacts not detected using the currently employed daily QA techniques.</description><subject>Algorithms</subject><subject>Anthropomorphism</subject><subject>Cancer</subject><subject>Feasibility studies</subject><subject>Humans</subject><subject>Image Processing, Computer-Assisted - methods</subject><subject>Lymphoma</subject><subject>Lymphoma - diagnostic imaging</subject><subject>Medical imaging</subject><subject>Patients</subject><subject>Phantoms, Imaging</subject><subject>Population</subject><subject>Quality Assurance, Health Care - standards</subject><subject>Quality control</subject><subject>quantitative imaging</subject><subject>Radiomics</subject><subject>Scanners</subject><subject>Tomography</subject><subject>Tomography Scanners, X-Ray Computed - standards</subject><subject>Tomography, X-Ray Computed - instrumentation</subject><subject>Tomography, X-Ray Computed - methods</subject><issn>1526-9914</issn><issn>1526-9914</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>WIN</sourceid><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp90NtKwzAcx_EgipvTGx9ACt6IsJlDk7bejeEJ5uFiXtc0_dd1tE2XtMjufASf0ScxW6eIF0IhhXz4Eb4IHRM8IhjTC6lKOiI04HgH9QmnYhhFxN_99d9DB9YuMCYkZOE-6jEicIQD0UcvM1DzKley8B50A5fek9E689yndKWgbrxMG8_INNdlruzn-0ciLaTespVF3qw8aW1rpJMbp3RZt427bnSpX42s56tDtJfJwsLR9hyg5-ur2eR2OH28uZuMp0PlU4KHQcL9KJKYBVmIfYAgBV8lCaMiE5QxwbMEs4RyGqZCYqpCHyiPMg5chhCEnA3QWbdbG71swTZxmVsFRSEr0K2NKcPcDwTDwtHTP3ShW1O51znlOgrCXacBOu-UMtpaA1lcm7yUZhUTHK-7x-vu8aa7wyfbyTYpIf2h36EdIB14ywtY_TMVjyf3tBv9AuH1jQA</recordid><startdate>201911</startdate><enddate>201911</enddate><creator>Branco, Luciano R. F.</creator><creator>Ger, Rachel B.</creator><creator>Mackin, Dennis S.</creator><creator>Zhou, Shouhao</creator><creator>Court, Laurence E.</creator><creator>Layman, Rick R.</creator><general>John Wiley & Sons, Inc</general><scope>24P</scope><scope>WIN</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>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88I</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>M0S</scope><scope>M2P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope></search><sort><creationdate>201911</creationdate><title>Technical Note: Proof of concept for radiomics‐based quality assurance for computed tomography</title><author>Branco, Luciano R. F. ; Ger, Rachel B. ; Mackin, Dennis S. ; Zhou, Shouhao ; Court, Laurence E. ; Layman, Rick R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4210-7b5499a037f804ee7de4cbb326f623365fb03b2528d6a02c84e259f5e5a8e7853</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Algorithms</topic><topic>Anthropomorphism</topic><topic>Cancer</topic><topic>Feasibility studies</topic><topic>Humans</topic><topic>Image Processing, Computer-Assisted - methods</topic><topic>Lymphoma</topic><topic>Lymphoma - diagnostic imaging</topic><topic>Medical imaging</topic><topic>Patients</topic><topic>Phantoms, Imaging</topic><topic>Population</topic><topic>Quality Assurance, Health Care - standards</topic><topic>Quality control</topic><topic>quantitative imaging</topic><topic>Radiomics</topic><topic>Scanners</topic><topic>Tomography</topic><topic>Tomography Scanners, X-Ray Computed - standards</topic><topic>Tomography, X-Ray Computed - instrumentation</topic><topic>Tomography, X-Ray Computed - methods</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Branco, Luciano R. F.</creatorcontrib><creatorcontrib>Ger, Rachel B.</creatorcontrib><creatorcontrib>Mackin, Dennis S.</creatorcontrib><creatorcontrib>Zhou, Shouhao</creatorcontrib><creatorcontrib>Court, Laurence E.</creatorcontrib><creatorcontrib>Layman, Rick R.</creatorcontrib><collection>Wiley-Blackwell Open Access Titles</collection><collection>Wiley Free Content</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Science Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of applied clinical medical physics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Branco, Luciano R. F.</au><au>Ger, Rachel B.</au><au>Mackin, Dennis S.</au><au>Zhou, Shouhao</au><au>Court, Laurence E.</au><au>Layman, Rick R.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Technical Note: Proof of concept for radiomics‐based quality assurance for computed tomography</atitle><jtitle>Journal of applied clinical medical physics</jtitle><addtitle>J Appl Clin Med Phys</addtitle><date>2019-11</date><risdate>2019</risdate><volume>20</volume><issue>11</issue><spage>199</spage><epage>205</epage><pages>199-205</pages><issn>1526-9914</issn><eissn>1526-9914</eissn><abstract>Purpose
Routine quality assurance (QA) testing to identify malfunctions in medical imaging devices is a standard practice and plays an important role in meeting quality standards. However, current daily computed tomography (CT) QA techniques have proven to be inadequate for the detection of subtle artifacts on scans. Therefore, we investigated the ability of a radiomics phantom to detect subtle artifacts not detected in conventional daily QA.
Methods
An updated credence cartridge radiomics phantom was used in this study, with a focus on two of the cartridges (rubber and cork) in the phantom. The phantom was scanned using a Siemens Definition Flash CT scanner, which was reported to produce a subtle line pattern artifact. Images were then imported into the IBEX software program, and 49 features were extracted from the two cartridges using four different preprocessing techniques. Each feature was then compared with features for the same scanner several months previously and with features from controlled CT scans obtained using 100 scanners.
Results
Of 196 total features for the test scanner, 79 (40%) from the rubber cartridge and 70 (36%) from the cork cartridge were three or more standard deviations away from the mean of the controlled scan population data. Feature values for the artifact‐producing scanner were closer to the population mean when features were preprocessed with Butterworth smoothing. The feature most sensitive to the artifact was co‐occurrence matrix maximum probability. The deviation from the mean for this feature was more than seven times greater when the scanner was malfunctioning (7.56 versus 1.01).
Conclusions
Radiomics features extracted from a texture phantom were able to identify an artifact‐producing scanner as an outlier among 100 CT scanners. This preliminary analysis demonstrated the potential of radiomics in CT QA to identify subtle artifacts not detected using the currently employed daily QA techniques.</abstract><cop>United States</cop><pub>John Wiley & Sons, Inc</pub><pmid>31609076</pmid><doi>10.1002/acm2.12750</doi><tpages>7</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Anthropomorphism Cancer Feasibility studies Humans Image Processing, Computer-Assisted - methods Lymphoma Lymphoma - diagnostic imaging Medical imaging Patients Phantoms, Imaging Population Quality Assurance, Health Care - standards Quality control quantitative imaging Radiomics Scanners Tomography Tomography Scanners, X-Ray Computed - standards Tomography, X-Ray Computed - instrumentation Tomography, X-Ray Computed - methods |
| title | Technical Note: Proof of concept for radiomics‐based quality assurance for computed tomography |
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