FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2020160353> ?p ?o ?g. }

• W2020160353 endingPage "2009" @default.
• W2020160353 startingPage "1991" @default.
• W2020160353 abstract "Because proton beams activate positron emitters in patients, positron emission tomography (PET) has the potential to play a unique role in the in vivo verification of proton radiotherapy. Unfortunately, the PET image is not directly proportional to the delivered radiation dose distribution. Current treatment verification strategies using PET therefore compare the actual PET image with full-blown Monte Carlo simulations of the PET signal. In this paper, we describe a simpler and more direct way to reconstruct the expected PET signal from the local radiation dose distribution near the distal fall-off region, which is calculated by the treatment planning programme. Under reasonable assumptions, the PET image can be described as a convolution of the dose distribution with a filter function. We develop a formalism to derive the filter function analytically. The main concept is the introduction of 'Q' functions defined as the convolution of a Gaussian with a powerlaw function. Special Q functions are the Gaussian itself and the error function. The convolution of two Q functions is another Q function. By fitting elementary dose distributions and their corresponding PET signals with Q functions, we derive the Q function approximation of the filter. The new filtering method has been validated through comparisons with Monte Carlo calculations and, in one case, with measured data. While the basic concept is developed under idealized conditions assuming that the absorbing medium is homogeneous near the distal fall-off region, a generalization to inhomogeneous situations is also described. As a result, the method can determine the distal fall-off region of the PET signal, and consequently the range of the proton beam, with millimetre accuracy. Quantification of the produced activity is possible. In conclusion, the PET activity resulting from a proton beam treatment can be determined by locally filtering the dose distribution as obtained from the treatment planning system. The filter function can be calculated analytically using convolutions of Gaussians and powerlaw functions." @default.
• W2020160353 created "2016-06-24" @default.
• W2020160353 creator A5000377815 @default.
• W2020160353 creator A5000777470 @default.
• W2020160353 date "2006-03-30" @default.
• W2020160353 modified "2023-10-14" @default.
• W2020160353 title "A filtering approach based on Gaussian–powerlaw convolutions for local PET verification of proton radiotherapy" @default.
• W2020160353 cites W1973617788 @default.
• W2020160353 cites W1984666539 @default.
• W2020160353 cites W2010303401 @default.
• W2020160353 cites W2018826659 @default.
• W2020160353 cites W2022985124 @default.
• W2020160353 cites W2023921468 @default.
• W2020160353 cites W2025109572 @default.
• W2020160353 cites W2043603625 @default.
• W2020160353 cites W2047398208 @default.
• W2020160353 cites W2059300664 @default.
• W2020160353 cites W2059490276 @default.
• W2020160353 cites W2073333316 @default.
• W2020160353 cites W2081893620 @default.
• W2020160353 cites W2087625924 @default.
• W2020160353 cites W2096025547 @default.
• W2020160353 cites W2119936729 @default.
• W2020160353 cites W2128158076 @default.
• W2020160353 cites W2135269192 @default.
• W2020160353 cites W2138213701 @default.
• W2020160353 cites W45489279 @default.
• W2020160353 doi "https://doi.org/10.1088/0031-9155/51/8/003" @default.
• W2020160353 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/16585841" @default.
• W2020160353 hasPublicationYear "2006" @default.
• W2020160353 type Work @default.
• W2020160353 sameAs 2020160353 @default.
• W2020160353 citedByCount "88" @default.
• W2020160353 countsByYear W20201603532012 @default.
• W2020160353 countsByYear W20201603532013 @default.
• W2020160353 countsByYear W20201603532014 @default.
• W2020160353 countsByYear W20201603532015 @default.
• W2020160353 countsByYear W20201603532016 @default.
• W2020160353 countsByYear W20201603532017 @default.
• W2020160353 countsByYear W20201603532018 @default.
• W2020160353 countsByYear W20201603532019 @default.
• W2020160353 countsByYear W20201603532020 @default.
• W2020160353 countsByYear W20201603532021 @default.
• W2020160353 countsByYear W20201603532022 @default.
• W2020160353 countsByYear W20201603532023 @default.
• W2020160353 crossrefType "journal-article" @default.
• W2020160353 hasAuthorship W2020160353A5000377815 @default.
• W2020160353 hasAuthorship W2020160353A5000777470 @default.
• W2020160353 hasConcept C105795698 @default.
• W2020160353 hasConcept C106131492 @default.
• W2020160353 hasConcept C11413529 @default.
• W2020160353 hasConcept C121332964 @default.
• W2020160353 hasConcept C154945302 @default.
• W2020160353 hasConcept C163716315 @default.
• W2020160353 hasConcept C185544564 @default.
• W2020160353 hasConcept C19499675 @default.
• W2020160353 hasConcept C2775842073 @default.
• W2020160353 hasConcept C2779244869 @default.
• W2020160353 hasConcept C2989005 @default.
• W2020160353 hasConcept C31972630 @default.
• W2020160353 hasConcept C33923547 @default.
• W2020160353 hasConcept C41008148 @default.
• W2020160353 hasConcept C45347329 @default.
• W2020160353 hasConcept C50644808 @default.
• W2020160353 hasConcept C54516573 @default.
• W2020160353 hasConcept C62520636 @default.
• W2020160353 hasConcept C71924100 @default.
• W2020160353 hasConcept C7218915 @default.
• W2020160353 hasConceptScore W2020160353C105795698 @default.
• W2020160353 hasConceptScore W2020160353C106131492 @default.
• W2020160353 hasConceptScore W2020160353C11413529 @default.
• W2020160353 hasConceptScore W2020160353C121332964 @default.
• W2020160353 hasConceptScore W2020160353C154945302 @default.
• W2020160353 hasConceptScore W2020160353C163716315 @default.
• W2020160353 hasConceptScore W2020160353C185544564 @default.
• W2020160353 hasConceptScore W2020160353C19499675 @default.
• W2020160353 hasConceptScore W2020160353C2775842073 @default.
• W2020160353 hasConceptScore W2020160353C2779244869 @default.
• W2020160353 hasConceptScore W2020160353C2989005 @default.
• W2020160353 hasConceptScore W2020160353C31972630 @default.
• W2020160353 hasConceptScore W2020160353C33923547 @default.
• W2020160353 hasConceptScore W2020160353C41008148 @default.
• W2020160353 hasConceptScore W2020160353C45347329 @default.
• W2020160353 hasConceptScore W2020160353C50644808 @default.
• W2020160353 hasConceptScore W2020160353C54516573 @default.
• W2020160353 hasConceptScore W2020160353C62520636 @default.
• W2020160353 hasConceptScore W2020160353C71924100 @default.
• W2020160353 hasConceptScore W2020160353C7218915 @default.
• W2020160353 hasIssue "8" @default.
• W2020160353 hasLocation W20201603531 @default.
• W2020160353 hasLocation W20201603532 @default.
• W2020160353 hasOpenAccess W2020160353 @default.
• W2020160353 hasPrimaryLocation W20201603531 @default.
• W2020160353 hasRelatedWork W171666365 @default.
• W2020160353 hasRelatedWork W1972955259 @default.
• W2020160353 hasRelatedWork W2010303401 @default.
• W2020160353 hasRelatedWork W2089894869 @default.
• W2020160353 hasRelatedWork W2098736080 @default.

• next