Application of frozen Thiel-embalmed specimens for radiotherapy delineation guideline development: a method to create accurate MRI-enhanced CT datasets
Purpose Thiel embalming followed by freezing in the desired position and acquiring CT + MRI scans is expected to be the ideal approach to obtain accurate, enhanced CT data for delineation guideline development. The effect of Thiel embalming and freezing on MRI image quality is not known. This study...
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| Veröffentlicht in: | Strahlentherapie und Onkologie 2022-06, Vol.198 (6), p.582-592 |
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| creator | Stouthandel, Michael E. J. Pullens, Pim Bogaert, Stephanie Schoepen, Max Vangestel, Carl Achten, Eric Veldeman, Liv Van Hoof, Tom |
| description | Purpose
Thiel embalming followed by freezing in the desired position and acquiring CT + MRI scans is expected to be the ideal approach to obtain accurate, enhanced CT data for delineation guideline development. The effect of Thiel embalming and freezing on MRI image quality is not known. This study evaluates the above-described process to obtain enhanced CT datasets, focusing on the integration of MRI data obtained from frozen, Thiel-embalmed specimens.
Methods
Three Thiel-embalmed specimens were frozen in prone crawl position and MRI scanning protocols were evaluated based on contrast detail and structural conformity between 3D renderings from corresponding structures, segmented on corresponding MRI and CT scans. The measurement error of the dataset registration procedure was also assessed.
Results
Scanning protocol T1 VIBE FS enabled swift differentiation of soft tissues based on contrast detail, even allowing a fully detailed segmentation of the brachial plexus. Structural conformity between the reconstructed structures on CT and MRI was excellent, with nerves and blood vessels imported into the CT scan never intersecting with the bones. The mean measurement error for the image registration procedure was consistently in the submillimeter range (range 0.77–0.94 mm).
Conclusion
Based on the excellent MRI image quality and the submillimeter error margin, the procedure of scanning frozen Thiel-embalmed specimens in the treatment position to obtain enhanced CT scans is recommended. The procedure can be used to support the postulation of delineation guidelines, or for training deep learning algorithms, considering automated segmentations. |
| doi_str_mv | 10.1007/s00066-022-01928-z |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2649251852</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2667952809</sourcerecordid><originalsourceid>FETCH-LOGICAL-c256t-460bf69671cc8626448d9a3db4cad35377db7dd1305ee408fd0636759a9001cb3</originalsourceid><addsrcrecordid>eNp9kc1u1TAQhS1ERW8LL8ACWWLDxnQc_yRmV11BqVSEhC4Su8ixJ72pkjjYSaXeJ2HJs_BkuKSAxIKV7fF3zozmEPKcw2sOUJ4lANCaQVEw4Kao2OER2XApDANjvjwmG-ClYSVX1TE5SekGgGtp5BNyLJQEURm-Id_Op6nvnJ27MNLQ0jaGA450t--wZzg0th_Q0zSh6wYcE21DpNH6Lsx7jHa6ox77bsRVf7106zNXb7EPU5bMb6j98X3AeR88nQN1McNIrXNLvL98-HTJcNzb0eU-2x31drYJ5_SUHLW2T_js4Twln9-93W3fs6uPF5fb8yvmCqVnJjU0rTa65M5VutBSVt5Y4RvprBdKlKVvSu-5AIUooWo9aKFLZazJ63CNOCWvVt8phq8LprkeuuSw7-2IYUl1tjSF4pUqMvryH_QmLHHM02VKl0YVFZhMFSvlYkgpYltPsRtsvKs51Pe51Wtudc6t_pVbfciiFw_WS5MX_kfyO6gMiBVI-Wu8xvi3939sfwK_iqaw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2667952809</pqid></control><display><type>article</type><title>Application of frozen Thiel-embalmed specimens for radiotherapy delineation guideline development: a method to create accurate MRI-enhanced CT datasets</title><source>Springer Nature - Complete Springer Journals</source><creator>Stouthandel, Michael E. J. ; Pullens, Pim ; Bogaert, Stephanie ; Schoepen, Max ; Vangestel, Carl ; Achten, Eric ; Veldeman, Liv ; Van Hoof, Tom</creator><creatorcontrib>Stouthandel, Michael E. J. ; Pullens, Pim ; Bogaert, Stephanie ; Schoepen, Max ; Vangestel, Carl ; Achten, Eric ; Veldeman, Liv ; Van Hoof, Tom</creatorcontrib><description>Purpose
Thiel embalming followed by freezing in the desired position and acquiring CT + MRI scans is expected to be the ideal approach to obtain accurate, enhanced CT data for delineation guideline development. The effect of Thiel embalming and freezing on MRI image quality is not known. This study evaluates the above-described process to obtain enhanced CT datasets, focusing on the integration of MRI data obtained from frozen, Thiel-embalmed specimens.
Methods
Three Thiel-embalmed specimens were frozen in prone crawl position and MRI scanning protocols were evaluated based on contrast detail and structural conformity between 3D renderings from corresponding structures, segmented on corresponding MRI and CT scans. The measurement error of the dataset registration procedure was also assessed.
Results
Scanning protocol T1 VIBE FS enabled swift differentiation of soft tissues based on contrast detail, even allowing a fully detailed segmentation of the brachial plexus. Structural conformity between the reconstructed structures on CT and MRI was excellent, with nerves and blood vessels imported into the CT scan never intersecting with the bones. The mean measurement error for the image registration procedure was consistently in the submillimeter range (range 0.77–0.94 mm).
Conclusion
Based on the excellent MRI image quality and the submillimeter error margin, the procedure of scanning frozen Thiel-embalmed specimens in the treatment position to obtain enhanced CT scans is recommended. The procedure can be used to support the postulation of delineation guidelines, or for training deep learning algorithms, considering automated segmentations.</description><identifier>ISSN: 0179-7158</identifier><identifier>EISSN: 1439-099X</identifier><identifier>DOI: 10.1007/s00066-022-01928-z</identifier><identifier>PMID: 35403891</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Algorithms ; Blood vessels ; Bones ; Computed tomography ; Conformity ; Datasets ; Delineation ; Error analysis ; Evaluation ; Freezing ; Image quality ; Image registration ; Image segmentation ; Machine learning ; Magnetic resonance imaging ; Medical imaging ; Medicine ; Medicine & Public Health ; Oncology ; Original Article ; Radiation therapy ; Radiotherapy ; Scanning ; Soft tissues</subject><ispartof>Strahlentherapie und Onkologie, 2022-06, Vol.198 (6), p.582-592</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany 2022</rights><rights>2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany.</rights><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany 2022.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c256t-460bf69671cc8626448d9a3db4cad35377db7dd1305ee408fd0636759a9001cb3</cites><orcidid>0000-0003-4276-8926</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00066-022-01928-z$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00066-022-01928-z$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,777,781,27905,27906,41469,42538,51300</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35403891$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Stouthandel, Michael E. J.</creatorcontrib><creatorcontrib>Pullens, Pim</creatorcontrib><creatorcontrib>Bogaert, Stephanie</creatorcontrib><creatorcontrib>Schoepen, Max</creatorcontrib><creatorcontrib>Vangestel, Carl</creatorcontrib><creatorcontrib>Achten, Eric</creatorcontrib><creatorcontrib>Veldeman, Liv</creatorcontrib><creatorcontrib>Van Hoof, Tom</creatorcontrib><title>Application of frozen Thiel-embalmed specimens for radiotherapy delineation guideline development: a method to create accurate MRI-enhanced CT datasets</title><title>Strahlentherapie und Onkologie</title><addtitle>Strahlenther Onkol</addtitle><addtitle>Strahlenther Onkol</addtitle><description>Purpose
Thiel embalming followed by freezing in the desired position and acquiring CT + MRI scans is expected to be the ideal approach to obtain accurate, enhanced CT data for delineation guideline development. The effect of Thiel embalming and freezing on MRI image quality is not known. This study evaluates the above-described process to obtain enhanced CT datasets, focusing on the integration of MRI data obtained from frozen, Thiel-embalmed specimens.
Methods
Three Thiel-embalmed specimens were frozen in prone crawl position and MRI scanning protocols were evaluated based on contrast detail and structural conformity between 3D renderings from corresponding structures, segmented on corresponding MRI and CT scans. The measurement error of the dataset registration procedure was also assessed.
Results
Scanning protocol T1 VIBE FS enabled swift differentiation of soft tissues based on contrast detail, even allowing a fully detailed segmentation of the brachial plexus. Structural conformity between the reconstructed structures on CT and MRI was excellent, with nerves and blood vessels imported into the CT scan never intersecting with the bones. The mean measurement error for the image registration procedure was consistently in the submillimeter range (range 0.77–0.94 mm).
Conclusion
Based on the excellent MRI image quality and the submillimeter error margin, the procedure of scanning frozen Thiel-embalmed specimens in the treatment position to obtain enhanced CT scans is recommended. The procedure can be used to support the postulation of delineation guidelines, or for training deep learning algorithms, considering automated segmentations.</description><subject>Algorithms</subject><subject>Blood vessels</subject><subject>Bones</subject><subject>Computed tomography</subject><subject>Conformity</subject><subject>Datasets</subject><subject>Delineation</subject><subject>Error analysis</subject><subject>Evaluation</subject><subject>Freezing</subject><subject>Image quality</subject><subject>Image registration</subject><subject>Image segmentation</subject><subject>Machine learning</subject><subject>Magnetic resonance imaging</subject><subject>Medical imaging</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Oncology</subject><subject>Original Article</subject><subject>Radiation therapy</subject><subject>Radiotherapy</subject><subject>Scanning</subject><subject>Soft tissues</subject><issn>0179-7158</issn><issn>1439-099X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNp9kc1u1TAQhS1ERW8LL8ACWWLDxnQc_yRmV11BqVSEhC4Su8ixJ72pkjjYSaXeJ2HJs_BkuKSAxIKV7fF3zozmEPKcw2sOUJ4lANCaQVEw4Kao2OER2XApDANjvjwmG-ClYSVX1TE5SekGgGtp5BNyLJQEURm-Id_Op6nvnJ27MNLQ0jaGA450t--wZzg0th_Q0zSh6wYcE21DpNH6Lsx7jHa6ox77bsRVf7106zNXb7EPU5bMb6j98X3AeR88nQN1McNIrXNLvL98-HTJcNzb0eU-2x31drYJ5_SUHLW2T_js4Twln9-93W3fs6uPF5fb8yvmCqVnJjU0rTa65M5VutBSVt5Y4RvprBdKlKVvSu-5AIUooWo9aKFLZazJ63CNOCWvVt8phq8LprkeuuSw7-2IYUl1tjSF4pUqMvryH_QmLHHM02VKl0YVFZhMFSvlYkgpYltPsRtsvKs51Pe51Wtudc6t_pVbfciiFw_WS5MX_kfyO6gMiBVI-Wu8xvi3939sfwK_iqaw</recordid><startdate>20220601</startdate><enddate>20220601</enddate><creator>Stouthandel, Michael E. J.</creator><creator>Pullens, Pim</creator><creator>Bogaert, Stephanie</creator><creator>Schoepen, Max</creator><creator>Vangestel, Carl</creator><creator>Achten, Eric</creator><creator>Veldeman, Liv</creator><creator>Van Hoof, Tom</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FE</scope><scope>8FG</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB0</scope><scope>M0S</scope><scope>M1P</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-4276-8926</orcidid></search><sort><creationdate>20220601</creationdate><title>Application of frozen Thiel-embalmed specimens for radiotherapy delineation guideline development: a method to create accurate MRI-enhanced CT datasets</title><author>Stouthandel, Michael E. J. ; Pullens, Pim ; Bogaert, Stephanie ; Schoepen, Max ; Vangestel, Carl ; Achten, Eric ; Veldeman, Liv ; Van Hoof, Tom</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c256t-460bf69671cc8626448d9a3db4cad35377db7dd1305ee408fd0636759a9001cb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Blood vessels</topic><topic>Bones</topic><topic>Computed tomography</topic><topic>Conformity</topic><topic>Datasets</topic><topic>Delineation</topic><topic>Error analysis</topic><topic>Evaluation</topic><topic>Freezing</topic><topic>Image quality</topic><topic>Image registration</topic><topic>Image segmentation</topic><topic>Machine learning</topic><topic>Magnetic resonance imaging</topic><topic>Medical imaging</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Oncology</topic><topic>Original Article</topic><topic>Radiation therapy</topic><topic>Radiotherapy</topic><topic>Scanning</topic><topic>Soft tissues</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Stouthandel, Michael E. J.</creatorcontrib><creatorcontrib>Pullens, Pim</creatorcontrib><creatorcontrib>Bogaert, Stephanie</creatorcontrib><creatorcontrib>Schoepen, Max</creatorcontrib><creatorcontrib>Vangestel, Carl</creatorcontrib><creatorcontrib>Achten, Eric</creatorcontrib><creatorcontrib>Veldeman, Liv</creatorcontrib><creatorcontrib>Van Hoof, Tom</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Nursing & Allied Health Database</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</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>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</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>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</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>MEDLINE - Academic</collection><jtitle>Strahlentherapie und Onkologie</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Stouthandel, Michael E. J.</au><au>Pullens, Pim</au><au>Bogaert, Stephanie</au><au>Schoepen, Max</au><au>Vangestel, Carl</au><au>Achten, Eric</au><au>Veldeman, Liv</au><au>Van Hoof, Tom</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Application of frozen Thiel-embalmed specimens for radiotherapy delineation guideline development: a method to create accurate MRI-enhanced CT datasets</atitle><jtitle>Strahlentherapie und Onkologie</jtitle><stitle>Strahlenther Onkol</stitle><addtitle>Strahlenther Onkol</addtitle><date>2022-06-01</date><risdate>2022</risdate><volume>198</volume><issue>6</issue><spage>582</spage><epage>592</epage><pages>582-592</pages><issn>0179-7158</issn><eissn>1439-099X</eissn><abstract>Purpose
Thiel embalming followed by freezing in the desired position and acquiring CT + MRI scans is expected to be the ideal approach to obtain accurate, enhanced CT data for delineation guideline development. The effect of Thiel embalming and freezing on MRI image quality is not known. This study evaluates the above-described process to obtain enhanced CT datasets, focusing on the integration of MRI data obtained from frozen, Thiel-embalmed specimens.
Methods
Three Thiel-embalmed specimens were frozen in prone crawl position and MRI scanning protocols were evaluated based on contrast detail and structural conformity between 3D renderings from corresponding structures, segmented on corresponding MRI and CT scans. The measurement error of the dataset registration procedure was also assessed.
Results
Scanning protocol T1 VIBE FS enabled swift differentiation of soft tissues based on contrast detail, even allowing a fully detailed segmentation of the brachial plexus. Structural conformity between the reconstructed structures on CT and MRI was excellent, with nerves and blood vessels imported into the CT scan never intersecting with the bones. The mean measurement error for the image registration procedure was consistently in the submillimeter range (range 0.77–0.94 mm).
Conclusion
Based on the excellent MRI image quality and the submillimeter error margin, the procedure of scanning frozen Thiel-embalmed specimens in the treatment position to obtain enhanced CT scans is recommended. The procedure can be used to support the postulation of delineation guidelines, or for training deep learning algorithms, considering automated segmentations.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>35403891</pmid><doi>10.1007/s00066-022-01928-z</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0003-4276-8926</orcidid></addata></record> |
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| ispartof | Strahlentherapie und Onkologie, 2022-06, Vol.198 (6), p.582-592 |
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| source | Springer Nature - Complete Springer Journals |
| subjects | Algorithms Blood vessels Bones Computed tomography Conformity Datasets Delineation Error analysis Evaluation Freezing Image quality Image registration Image segmentation Machine learning Magnetic resonance imaging Medical imaging Medicine Medicine & Public Health Oncology Original Article Radiation therapy Radiotherapy Scanning Soft tissues |
| title | Application of frozen Thiel-embalmed specimens for radiotherapy delineation guideline development: a method to create accurate MRI-enhanced CT datasets |
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