Intelligent 4D CT sequence scanning (i4DCT): First scanner prototype implementation and phantom measurements of automated breathing signal‐guided 4D CT
Purpose Four‐dimensional (4D) computed tomography (CT) imaging is an essential part of current 4D radiotherapy treatment planning workflows, but clinical 4D CT images are often affected by artifacts. The artifacts are mainly caused by breathing irregularity during data acquisition, which leads to pr...
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| Veröffentlicht in: | Medical physics (Lancaster) 2020-06, Vol.47 (6), p.2408-2412 |
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| creator | Werner, René Sentker, Thilo Madesta, Frederic Schwarz, Annette Vornehm, Marc Gauer, Tobias Hofmann, Christian |
| description | Purpose
Four‐dimensional (4D) computed tomography (CT) imaging is an essential part of current 4D radiotherapy treatment planning workflows, but clinical 4D CT images are often affected by artifacts. The artifacts are mainly caused by breathing irregularity during data acquisition, which leads to projection data coverage issues for currently available commercial 4D CT protocols. It was proposed to improve projection data coverage by online respiratory signal analysis and signal‐guided CT tube control, but related work was always theoretical and presented as pure in silico studies. The present work demonstrates a first CT prototype implementation along with respective phantom measurements for the recently introduced intelligent 4D CT (i4DCT) sequence scanning concept (https://doi.org/10.1002/mp.13632).
Methods
Intelligent 4D CT was implemented on the Siemens SOMATOM go platform. Four‐dimensional CT measurements were performed using the CIRS motion phantom. Motion curves were programmed to systematically vary from regular to very irregular, covering typical irregular patterns that are known to result in image artifacts using standard 4D CT imaging protocols. Corresponding measurements were performed using i4DCT and routine spiral 4D CT with similar imaging parameters (e.g., mAs setting and gantry rotation time, retrospective ten‐phase reconstruction) to allow for a direct comparison of the image data.
Results
Following technological implementation of i4DCT on the clinical CT scanner platform, 4D CT motion artifacts were significantly reduced for all investigated levels of breathing irregularity when compared to routine spiral 4D CT scanning.
Conclusions
The present study confirms feasibility of fully automated respiratory signal‐guided 4D CT scanning by means of a first implementation of i4DCT on a CT scanner. The measurements thereby support the conclusions of respective in silico studies and demonstrate that respiratory signal‐guided 4D CT (here: i4DCT) is ready for integration into clinical CT scanners. |
| doi_str_mv | 10.1002/mp.14106 |
| format | Article |
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Four‐dimensional (4D) computed tomography (CT) imaging is an essential part of current 4D radiotherapy treatment planning workflows, but clinical 4D CT images are often affected by artifacts. The artifacts are mainly caused by breathing irregularity during data acquisition, which leads to projection data coverage issues for currently available commercial 4D CT protocols. It was proposed to improve projection data coverage by online respiratory signal analysis and signal‐guided CT tube control, but related work was always theoretical and presented as pure in silico studies. The present work demonstrates a first CT prototype implementation along with respective phantom measurements for the recently introduced intelligent 4D CT (i4DCT) sequence scanning concept (https://doi.org/10.1002/mp.13632).
Methods
Intelligent 4D CT was implemented on the Siemens SOMATOM go platform. Four‐dimensional CT measurements were performed using the CIRS motion phantom. Motion curves were programmed to systematically vary from regular to very irregular, covering typical irregular patterns that are known to result in image artifacts using standard 4D CT imaging protocols. Corresponding measurements were performed using i4DCT and routine spiral 4D CT with similar imaging parameters (e.g., mAs setting and gantry rotation time, retrospective ten‐phase reconstruction) to allow for a direct comparison of the image data.
Results
Following technological implementation of i4DCT on the clinical CT scanner platform, 4D CT motion artifacts were significantly reduced for all investigated levels of breathing irregularity when compared to routine spiral 4D CT scanning.
Conclusions
The present study confirms feasibility of fully automated respiratory signal‐guided 4D CT scanning by means of a first implementation of i4DCT on a CT scanner. The measurements thereby support the conclusions of respective in silico studies and demonstrate that respiratory signal‐guided 4D CT (here: i4DCT) is ready for integration into clinical CT scanners.</description><identifier>ISSN: 0094-2405</identifier><identifier>EISSN: 2473-4209</identifier><identifier>DOI: 10.1002/mp.14106</identifier><identifier>PMID: 32115724</identifier><language>eng</language><publisher>United States</publisher><subject>4D CT ; Four-Dimensional Computed Tomography ; Humans ; Lung Neoplasms - diagnostic imaging ; motion artifacts ; Phantoms, Imaging ; radiotherapy ; Respiration ; Retrospective Studies</subject><ispartof>Medical physics (Lancaster), 2020-06, Vol.47 (6), p.2408-2412</ispartof><rights>2020 The Authors. Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3556-c9878fc797ef23a22a69fd001ae410c3ebdbf8f94c16b40bc8a14c5b5433b6f03</citedby><cites>FETCH-LOGICAL-c3556-c9878fc797ef23a22a69fd001ae410c3ebdbf8f94c16b40bc8a14c5b5433b6f03</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%2Fmp.14106$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fmp.14106$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,1417,27924,27925,45574,45575</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32115724$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Werner, René</creatorcontrib><creatorcontrib>Sentker, Thilo</creatorcontrib><creatorcontrib>Madesta, Frederic</creatorcontrib><creatorcontrib>Schwarz, Annette</creatorcontrib><creatorcontrib>Vornehm, Marc</creatorcontrib><creatorcontrib>Gauer, Tobias</creatorcontrib><creatorcontrib>Hofmann, Christian</creatorcontrib><title>Intelligent 4D CT sequence scanning (i4DCT): First scanner prototype implementation and phantom measurements of automated breathing signal‐guided 4D CT</title><title>Medical physics (Lancaster)</title><addtitle>Med Phys</addtitle><description>Purpose
Four‐dimensional (4D) computed tomography (CT) imaging is an essential part of current 4D radiotherapy treatment planning workflows, but clinical 4D CT images are often affected by artifacts. The artifacts are mainly caused by breathing irregularity during data acquisition, which leads to projection data coverage issues for currently available commercial 4D CT protocols. It was proposed to improve projection data coverage by online respiratory signal analysis and signal‐guided CT tube control, but related work was always theoretical and presented as pure in silico studies. The present work demonstrates a first CT prototype implementation along with respective phantom measurements for the recently introduced intelligent 4D CT (i4DCT) sequence scanning concept (https://doi.org/10.1002/mp.13632).
Methods
Intelligent 4D CT was implemented on the Siemens SOMATOM go platform. Four‐dimensional CT measurements were performed using the CIRS motion phantom. Motion curves were programmed to systematically vary from regular to very irregular, covering typical irregular patterns that are known to result in image artifacts using standard 4D CT imaging protocols. Corresponding measurements were performed using i4DCT and routine spiral 4D CT with similar imaging parameters (e.g., mAs setting and gantry rotation time, retrospective ten‐phase reconstruction) to allow for a direct comparison of the image data.
Results
Following technological implementation of i4DCT on the clinical CT scanner platform, 4D CT motion artifacts were significantly reduced for all investigated levels of breathing irregularity when compared to routine spiral 4D CT scanning.
Conclusions
The present study confirms feasibility of fully automated respiratory signal‐guided 4D CT scanning by means of a first implementation of i4DCT on a CT scanner. The measurements thereby support the conclusions of respective in silico studies and demonstrate that respiratory signal‐guided 4D CT (here: i4DCT) is ready for integration into clinical CT scanners.</description><subject>4D CT</subject><subject>Four-Dimensional Computed Tomography</subject><subject>Humans</subject><subject>Lung Neoplasms - diagnostic imaging</subject><subject>motion artifacts</subject><subject>Phantoms, Imaging</subject><subject>radiotherapy</subject><subject>Respiration</subject><subject>Retrospective Studies</subject><issn>0094-2405</issn><issn>2473-4209</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>WIN</sourceid><sourceid>EIF</sourceid><recordid>eNp1kc1u1DAQxy0EokuL1CdAPpZDyvgjyYYb2n5KRfSwnCPHGW9dxU6wHaG98Qhc-3o8Sd3dtjdOlmZ--o3nP4QcMzhlAPyLm06ZZFC9IQsua1FIDs1bsgBoZMEllAfkQ4z3AFCJEt6TA8EZK2suF-Th2iccBrtBn6g8o6s1jfhrRq-RRq28t35DT6w8W60_f6UXNsS0r2OgUxjTmLYTUuumAV1WqGRHT5Xv6XSnfBoddajiHHbNSEdD1ZyrKmFPu4Aq3T35o914Nfz783cz2z53dv84Iu-MGiJ-fH4Pyc-L8_Xqqrj5cXm9-nZTaFGWVaGbZb00um5qNFwozlXVmB6AKcyJaIFd35mlaaRmVSeh00vFpC67UgrRVQbEITnZe_M6efGYWmejzpkoj-McWy6qPKJqcq6vqA5jjAFNOwXrVNi2DNqnQ7RuaneHyOinZ-vcOexfwZfkM1Dsgd92wO1_Re33273wEdMSk-o</recordid><startdate>202006</startdate><enddate>202006</enddate><creator>Werner, René</creator><creator>Sentker, Thilo</creator><creator>Madesta, Frederic</creator><creator>Schwarz, Annette</creator><creator>Vornehm, Marc</creator><creator>Gauer, Tobias</creator><creator>Hofmann, Christian</creator><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>7X8</scope></search><sort><creationdate>202006</creationdate><title>Intelligent 4D CT sequence scanning (i4DCT): First scanner prototype implementation and phantom measurements of automated breathing signal‐guided 4D CT</title><author>Werner, René ; Sentker, Thilo ; Madesta, Frederic ; Schwarz, Annette ; Vornehm, Marc ; Gauer, Tobias ; Hofmann, Christian</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3556-c9878fc797ef23a22a69fd001ae410c3ebdbf8f94c16b40bc8a14c5b5433b6f03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>4D CT</topic><topic>Four-Dimensional Computed Tomography</topic><topic>Humans</topic><topic>Lung Neoplasms - diagnostic imaging</topic><topic>motion artifacts</topic><topic>Phantoms, Imaging</topic><topic>radiotherapy</topic><topic>Respiration</topic><topic>Retrospective Studies</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Werner, René</creatorcontrib><creatorcontrib>Sentker, Thilo</creatorcontrib><creatorcontrib>Madesta, Frederic</creatorcontrib><creatorcontrib>Schwarz, Annette</creatorcontrib><creatorcontrib>Vornehm, Marc</creatorcontrib><creatorcontrib>Gauer, Tobias</creatorcontrib><creatorcontrib>Hofmann, Christian</creatorcontrib><collection>Wiley Online Library Open Access</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>MEDLINE - Academic</collection><jtitle>Medical physics (Lancaster)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Werner, René</au><au>Sentker, Thilo</au><au>Madesta, Frederic</au><au>Schwarz, Annette</au><au>Vornehm, Marc</au><au>Gauer, Tobias</au><au>Hofmann, Christian</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Intelligent 4D CT sequence scanning (i4DCT): First scanner prototype implementation and phantom measurements of automated breathing signal‐guided 4D CT</atitle><jtitle>Medical physics (Lancaster)</jtitle><addtitle>Med Phys</addtitle><date>2020-06</date><risdate>2020</risdate><volume>47</volume><issue>6</issue><spage>2408</spage><epage>2412</epage><pages>2408-2412</pages><issn>0094-2405</issn><eissn>2473-4209</eissn><abstract>Purpose
Four‐dimensional (4D) computed tomography (CT) imaging is an essential part of current 4D radiotherapy treatment planning workflows, but clinical 4D CT images are often affected by artifacts. The artifacts are mainly caused by breathing irregularity during data acquisition, which leads to projection data coverage issues for currently available commercial 4D CT protocols. It was proposed to improve projection data coverage by online respiratory signal analysis and signal‐guided CT tube control, but related work was always theoretical and presented as pure in silico studies. The present work demonstrates a first CT prototype implementation along with respective phantom measurements for the recently introduced intelligent 4D CT (i4DCT) sequence scanning concept (https://doi.org/10.1002/mp.13632).
Methods
Intelligent 4D CT was implemented on the Siemens SOMATOM go platform. Four‐dimensional CT measurements were performed using the CIRS motion phantom. Motion curves were programmed to systematically vary from regular to very irregular, covering typical irregular patterns that are known to result in image artifacts using standard 4D CT imaging protocols. Corresponding measurements were performed using i4DCT and routine spiral 4D CT with similar imaging parameters (e.g., mAs setting and gantry rotation time, retrospective ten‐phase reconstruction) to allow for a direct comparison of the image data.
Results
Following technological implementation of i4DCT on the clinical CT scanner platform, 4D CT motion artifacts were significantly reduced for all investigated levels of breathing irregularity when compared to routine spiral 4D CT scanning.
Conclusions
The present study confirms feasibility of fully automated respiratory signal‐guided 4D CT scanning by means of a first implementation of i4DCT on a CT scanner. The measurements thereby support the conclusions of respective in silico studies and demonstrate that respiratory signal‐guided 4D CT (here: i4DCT) is ready for integration into clinical CT scanners.</abstract><cop>United States</cop><pmid>32115724</pmid><doi>10.1002/mp.14106</doi><tpages>5</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | 4D CT Four-Dimensional Computed Tomography Humans Lung Neoplasms - diagnostic imaging motion artifacts Phantoms, Imaging radiotherapy Respiration Retrospective Studies |
| title | Intelligent 4D CT sequence scanning (i4DCT): First scanner prototype implementation and phantom measurements of automated breathing signal‐guided 4D CT |
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