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Patient-specific quality assurance strategies for synthetic computed tomography in magnetic resonance-only radiotherapy of the abdomen
•The use of a separate neural network provided dose equivalence within 2%•The time burden was limited to less than 10 min.•The method was shown to be applicable in presence of air pockets, lung or implants. The superior tissue contrast of magnetic resonance (MR) compared to computed tomography (CT)...
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Published in: | Physics and imaging in radiation oncology 2023-07, Vol.27, p.100464-100464, Article 100464 |
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container_title | Physics and imaging in radiation oncology |
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creator | Dal Bello, Riccardo Lapaeva, Mariia La Greca Saint-Esteven, Agustina Wallimann, Philipp Günther, Manuel Konukoglu, Ender Andratschke, Nicolaus Guckenberger, Matthias Tanadini-Lang, Stephanie |
description | •The use of a separate neural network provided dose equivalence within 2%•The time burden was limited to less than 10 min.•The method was shown to be applicable in presence of air pockets, lung or implants.
The superior tissue contrast of magnetic resonance (MR) compared to computed tomography (CT) led to an increasing interest towards MR-only radiotherapy. For the latter, the dose calculation should be performed on a synthetic CT (sCT). Patient-specific quality assurance (PSQA) methods have not been established yet and this study aimed to assess several software-based solutions.
A retrospective study was performed on 20 patients treated at an MR-Linac, which were selected to evenly cover four subcategories: (i) standard, (ii) air pockets, (iii) lung and (iv) implant cases. The neural network (NN) CycleGAN was adopted to generate a reference sCT, which was then compared to four PSQA methods: (A) water override of body, (B) five tissue classes with bulk densities, (C) sCT generated by a separate NN (pix2pix) and (D) deformed CT.
The evaluation of the dose endpoints demonstrated that while all methods A-D provided statistically equivalent results (p = 0.05) within the 2% level for the standard cases (i), only the methods C-D guaranteed the same result over the whole cohort. The bulk densities override was shown to be a valuable method in absence of lung tissue within the beam path.
The observations of this study suggested that the use of an additional sCT generated by a separate NN was an appropriate tool to perform PSQA of a sCT in an MR-only workflow at an MR-Linac. The time and dose endpoints requirements were respected, namely within 10 min and 2%. |
doi_str_mv | 10.1016/j.phro.2023.100464 |
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The superior tissue contrast of magnetic resonance (MR) compared to computed tomography (CT) led to an increasing interest towards MR-only radiotherapy. For the latter, the dose calculation should be performed on a synthetic CT (sCT). Patient-specific quality assurance (PSQA) methods have not been established yet and this study aimed to assess several software-based solutions.
A retrospective study was performed on 20 patients treated at an MR-Linac, which were selected to evenly cover four subcategories: (i) standard, (ii) air pockets, (iii) lung and (iv) implant cases. The neural network (NN) CycleGAN was adopted to generate a reference sCT, which was then compared to four PSQA methods: (A) water override of body, (B) five tissue classes with bulk densities, (C) sCT generated by a separate NN (pix2pix) and (D) deformed CT.
The evaluation of the dose endpoints demonstrated that while all methods A-D provided statistically equivalent results (p = 0.05) within the 2% level for the standard cases (i), only the methods C-D guaranteed the same result over the whole cohort. The bulk densities override was shown to be a valuable method in absence of lung tissue within the beam path.
The observations of this study suggested that the use of an additional sCT generated by a separate NN was an appropriate tool to perform PSQA of a sCT in an MR-only workflow at an MR-Linac. The time and dose endpoints requirements were respected, namely within 10 min and 2%.</description><identifier>ISSN: 2405-6316</identifier><identifier>EISSN: 2405-6316</identifier><identifier>DOI: 10.1016/j.phro.2023.100464</identifier><identifier>PMID: 37497188</identifier><language>eng</language><publisher>Netherlands: Elsevier B.V</publisher><subject>MR-guided radiotherapy ; MR-Linac ; Neural network ; Original ; PSQA ; Quality assurance ; synthetic CT</subject><ispartof>Physics and imaging in radiation oncology, 2023-07, Vol.27, p.100464-100464, Article 100464</ispartof><rights>2023 The Author(s)</rights><rights>2023 The Author(s).</rights><rights>2023 The Author(s) 2023</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c522t-a56454b667fb3b4b85b5c64e024bb2ff344bb4444d6eef07e4d35b40c55e97e63</citedby><cites>FETCH-LOGICAL-c522t-a56454b667fb3b4b85b5c64e024bb2ff344bb4444d6eef07e4d35b40c55e97e63</cites><orcidid>0000-0003-1489-7448 ; 0000-0002-7110-6617</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10366576/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10366576/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37497188$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Dal Bello, Riccardo</creatorcontrib><creatorcontrib>Lapaeva, Mariia</creatorcontrib><creatorcontrib>La Greca Saint-Esteven, Agustina</creatorcontrib><creatorcontrib>Wallimann, Philipp</creatorcontrib><creatorcontrib>Günther, Manuel</creatorcontrib><creatorcontrib>Konukoglu, Ender</creatorcontrib><creatorcontrib>Andratschke, Nicolaus</creatorcontrib><creatorcontrib>Guckenberger, Matthias</creatorcontrib><creatorcontrib>Tanadini-Lang, Stephanie</creatorcontrib><title>Patient-specific quality assurance strategies for synthetic computed tomography in magnetic resonance-only radiotherapy of the abdomen</title><title>Physics and imaging in radiation oncology</title><addtitle>Phys Imaging Radiat Oncol</addtitle><description>•The use of a separate neural network provided dose equivalence within 2%•The time burden was limited to less than 10 min.•The method was shown to be applicable in presence of air pockets, lung or implants.
The superior tissue contrast of magnetic resonance (MR) compared to computed tomography (CT) led to an increasing interest towards MR-only radiotherapy. For the latter, the dose calculation should be performed on a synthetic CT (sCT). Patient-specific quality assurance (PSQA) methods have not been established yet and this study aimed to assess several software-based solutions.
A retrospective study was performed on 20 patients treated at an MR-Linac, which were selected to evenly cover four subcategories: (i) standard, (ii) air pockets, (iii) lung and (iv) implant cases. The neural network (NN) CycleGAN was adopted to generate a reference sCT, which was then compared to four PSQA methods: (A) water override of body, (B) five tissue classes with bulk densities, (C) sCT generated by a separate NN (pix2pix) and (D) deformed CT.
The evaluation of the dose endpoints demonstrated that while all methods A-D provided statistically equivalent results (p = 0.05) within the 2% level for the standard cases (i), only the methods C-D guaranteed the same result over the whole cohort. The bulk densities override was shown to be a valuable method in absence of lung tissue within the beam path.
The observations of this study suggested that the use of an additional sCT generated by a separate NN was an appropriate tool to perform PSQA of a sCT in an MR-only workflow at an MR-Linac. The time and dose endpoints requirements were respected, namely within 10 min and 2%.</description><subject>MR-guided radiotherapy</subject><subject>MR-Linac</subject><subject>Neural network</subject><subject>Original</subject><subject>PSQA</subject><subject>Quality assurance</subject><subject>synthetic CT</subject><issn>2405-6316</issn><issn>2405-6316</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNp9Ustq3TAQNaWlCWl-oIuiZTe-lfWyLxRKCX0EAu2iXQtJHvnqYkuOJAf8A_3u6sZpSDbVZoaZM0dHo1NVbxu8a3AjPhx38yGGHcGElgJmgr2ozgnDvBa0ES-f5GfVZUpHjDFp95RT_Lo6oy3bt03XnVd_fqrswOc6zWCcdQbdLmp0eUUqpSUqbwClHFWGwUFCNkSUVp8PkAvUhGleMvQohykMUc2HFTmPJjX4-36EFPyJog5-XFFUvQtltABXFCwqKVK6DxP4N9Urq8YElw_xovr99cuvq-_1zY9v11efb2rDCcm14oJxpoVoraaa6Y5rbgQDTJjWxFrKSmTl9ALA4hZYT7lm2HAO-xYEvaiuN94-qKOco5tUXGVQTt4XQhykikX6CFIxozHFlLZUsW5vOtL1ZK-N5bplFmjh-rRxzYueoDdli1GNz0ifd7w7yCHcyQZTIXh7UvP-gSGG2wVSlpNLBsZReQhLkqRjRYDAmBco2aAmhpQi2Md7GixPhpDlOcUQ8mQIuRmiDL17qvBx5N_3F8DHDQBl53cOokymuMFA7yKYXJbi_sf_F8Kiy3k</recordid><startdate>20230701</startdate><enddate>20230701</enddate><creator>Dal Bello, Riccardo</creator><creator>Lapaeva, Mariia</creator><creator>La Greca Saint-Esteven, Agustina</creator><creator>Wallimann, Philipp</creator><creator>Günther, Manuel</creator><creator>Konukoglu, Ender</creator><creator>Andratschke, Nicolaus</creator><creator>Guckenberger, Matthias</creator><creator>Tanadini-Lang, Stephanie</creator><general>Elsevier B.V</general><general>Elsevier</general><scope>6I.</scope><scope>AAFTH</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0003-1489-7448</orcidid><orcidid>https://orcid.org/0000-0002-7110-6617</orcidid></search><sort><creationdate>20230701</creationdate><title>Patient-specific quality assurance strategies for synthetic computed tomography in magnetic resonance-only radiotherapy of the abdomen</title><author>Dal Bello, Riccardo ; Lapaeva, Mariia ; La Greca Saint-Esteven, Agustina ; Wallimann, Philipp ; Günther, Manuel ; Konukoglu, Ender ; Andratschke, Nicolaus ; Guckenberger, Matthias ; Tanadini-Lang, Stephanie</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c522t-a56454b667fb3b4b85b5c64e024bb2ff344bb4444d6eef07e4d35b40c55e97e63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>MR-guided radiotherapy</topic><topic>MR-Linac</topic><topic>Neural network</topic><topic>Original</topic><topic>PSQA</topic><topic>Quality assurance</topic><topic>synthetic CT</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Dal Bello, Riccardo</creatorcontrib><creatorcontrib>Lapaeva, Mariia</creatorcontrib><creatorcontrib>La Greca Saint-Esteven, Agustina</creatorcontrib><creatorcontrib>Wallimann, Philipp</creatorcontrib><creatorcontrib>Günther, Manuel</creatorcontrib><creatorcontrib>Konukoglu, Ender</creatorcontrib><creatorcontrib>Andratschke, Nicolaus</creatorcontrib><creatorcontrib>Guckenberger, Matthias</creatorcontrib><creatorcontrib>Tanadini-Lang, Stephanie</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Physics and imaging in radiation oncology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Dal Bello, Riccardo</au><au>Lapaeva, Mariia</au><au>La Greca Saint-Esteven, Agustina</au><au>Wallimann, Philipp</au><au>Günther, Manuel</au><au>Konukoglu, Ender</au><au>Andratschke, Nicolaus</au><au>Guckenberger, Matthias</au><au>Tanadini-Lang, Stephanie</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Patient-specific quality assurance strategies for synthetic computed tomography in magnetic resonance-only radiotherapy of the abdomen</atitle><jtitle>Physics and imaging in radiation oncology</jtitle><addtitle>Phys Imaging Radiat Oncol</addtitle><date>2023-07-01</date><risdate>2023</risdate><volume>27</volume><spage>100464</spage><epage>100464</epage><pages>100464-100464</pages><artnum>100464</artnum><issn>2405-6316</issn><eissn>2405-6316</eissn><abstract>•The use of a separate neural network provided dose equivalence within 2%•The time burden was limited to less than 10 min.•The method was shown to be applicable in presence of air pockets, lung or implants.
The superior tissue contrast of magnetic resonance (MR) compared to computed tomography (CT) led to an increasing interest towards MR-only radiotherapy. For the latter, the dose calculation should be performed on a synthetic CT (sCT). Patient-specific quality assurance (PSQA) methods have not been established yet and this study aimed to assess several software-based solutions.
A retrospective study was performed on 20 patients treated at an MR-Linac, which were selected to evenly cover four subcategories: (i) standard, (ii) air pockets, (iii) lung and (iv) implant cases. The neural network (NN) CycleGAN was adopted to generate a reference sCT, which was then compared to four PSQA methods: (A) water override of body, (B) five tissue classes with bulk densities, (C) sCT generated by a separate NN (pix2pix) and (D) deformed CT.
The evaluation of the dose endpoints demonstrated that while all methods A-D provided statistically equivalent results (p = 0.05) within the 2% level for the standard cases (i), only the methods C-D guaranteed the same result over the whole cohort. The bulk densities override was shown to be a valuable method in absence of lung tissue within the beam path.
The observations of this study suggested that the use of an additional sCT generated by a separate NN was an appropriate tool to perform PSQA of a sCT in an MR-only workflow at an MR-Linac. The time and dose endpoints requirements were respected, namely within 10 min and 2%.</abstract><cop>Netherlands</cop><pub>Elsevier B.V</pub><pmid>37497188</pmid><doi>10.1016/j.phro.2023.100464</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0003-1489-7448</orcidid><orcidid>https://orcid.org/0000-0002-7110-6617</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | MR-guided radiotherapy MR-Linac Neural network Original PSQA Quality assurance synthetic CT |
title | Patient-specific quality assurance strategies for synthetic computed tomography in magnetic resonance-only radiotherapy of the abdomen |
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