Ontology type: schema:ScholarlyArticle Open Access: True
2015-12
AUTHORSAlex Dunlop, Dualta McQuaid, Simeon Nill, Julia Murray, Gavin Poludniowski, Vibeke N. Hansen, Shreerang Bhide, Christopher Nutting, Kevin Harrington, Kate Newbold, Uwe Oelfke
ABSTRACTPURPOSE: The aim of this work was to compare and validate various computed tomography (CT) number calibration techniques with respect to cone beam CT (CBCT) dose calculation accuracy. METHODS: CBCT dose calculation accuracy was assessed for pelvic, lung, and head and neck (H&N) treatment sites for two approaches: (1) physics-based scatter correction methods (CBCTr); (2) density override approaches including assigning water density to the entire CBCT (W), assignment of either water or bone density (WB), and assignment of either water or lung density (WL). Methods for CBCT density assignment within a commercially available treatment planning system (RSauto), where CBCT voxels are binned into six density levels, were assessed and validated. Dose-difference maps and dose-volume statistics were used to compare the CBCT dose distributions with the ground truth of a planning CT acquired the same day as the CBCT. RESULTS: For pelvic cases, all CTN calibration methods resulted in average dose-volume deviations below 1.5 %. RSauto provided larger than average errors for pelvic treatments for patients with large amounts of adipose tissue. For H&N cases, all CTN calibration methods resulted in average dose-volume differences below 1.0 % with CBCTr (0.5 %) and RSauto (0.6 %) performing best. For lung cases, WL and RSauto methods generated dose distributions most similar to the ground truth. CONCLUSION: The RSauto density override approach is an attractive option for CTN adjustments for a variety of anatomical sites. RSauto methods were validated, resulting in dose calculations that were consistent with those calculated on diagnostic-quality CT images, for CBCT images acquired of the lung, for patients receiving pelvic RT in cases without excess adipose tissue, and for H&N cases. More... »
PAGES970-978
http://scigraph.springernature.com/pub.10.1007/s00066-015-0890-7
DOIhttp://dx.doi.org/10.1007/s00066-015-0890-7
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PUBMEDhttps://www.ncbi.nlm.nih.gov/pubmed/26403913
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RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00066-015-0890-7'
This table displays all metadata directly associated to this object as RDF triples.
314 TRIPLES
21 PREDICATES
76 URIs
36 LITERALS
24 BLANK NODES