FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2071302050> ?p ?o ?g. }

• W2071302050 endingPage "2296" @default.
• W2071302050 startingPage "2289" @default.
• W2071302050 abstract "The success of hyperthermia treatments is dependent on thermal dose distribution. However, the three-dimensional temperature distribution remains largely unknown. Without this knowledge, the relationship between thermal dose and outcome is noisy, and therapy cannot be optimized. Accurate computations of thermal distribution can contribute to an optimized therapy. The hyperthermia modeling group in the Department of Radiotherapy, University Medical Center Utrecht devised a Discrete Vasculature [Kotte et al., Phys. Med. Biol. 41, 865-884 (1996)] model that accounts for the presence of vessel trees in the computational domain. The vessel tree geometry is tracked using magnetic resonance (MR) angiograms to a minimum diameter between 0.6 and 1 mm. However, smaller vessels (0.2-0.6 mm) are known to account for significant heat transfer. The hyperthermia group at Duke University Medical Center has proposed using perfusion maps derived from dynamic-enhanced magnetic resonance imaging to account for the tissue perfusion heterogeneity [Craciunescu et al., Int. J. Hyperthermia 17, 221-239 (2001)]. In addition, techniques for noninvasive temperature measurements have been devised to measure temperatures in vivo [Samulski et al., Int. J. Hypertherminal, 819-829 (1992)]. In this work, a patient with high-grade sarcoma has been retrospectively modeled to determine the temperature distribution achieved during a hyperthermia treatment. Available for this model were MR depicted geometry, angiograms, perfusion maps, as necessary for accurate thermal modeling, as well as MR thermometry data for validation purposes. The vasculature assembly through modifiable potential program [Van Leeuwen et al., IEEE Trans. Biomed. Eng. 45, 596-604 (1998)] was used in order to incorporate the traceable large vessels. Temperature simulations were made using different approaches to describe perfusion. The simulated cases were the bioheat equation with constant perfusion rates per tissue type, perfusion maps alone, tracked vessel tree and perfusion maps, and generated vessel tree. The results were compared with MR thermometry data for a single patient data set, concluding that a combination between large traceable vessels and perfusion map yields the best results for this particular patient. The technique has to be repeated on several patients, first with the same type of malignancy, and after that, on patients having malignancies at other different sites." @default.
• W2071302050 created "2016-06-24" @default.
• W2071302050 creator A5015237674 @default.
• W2071302050 creator A5020958460 @default.
• W2071302050 creator A5021726758 @default.
• W2071302050 creator A5038649058 @default.
• W2071302050 creator A5074631503 @default.
• W2071302050 creator A5089967041 @default.
• W2071302050 date "2001-11-01" @default.
• W2071302050 modified "2023-10-08" @default.
• W2071302050 title "Discretizing large traceable vessels and using DE-MRI perfusion maps yields numerical temperature contours that match the MR noninvasive measurements" @default.
• W2071302050 cites W167027689 @default.
• W2071302050 cites W188051004 @default.
• W2071302050 cites W1963540754 @default.
• W2071302050 cites W1971874415 @default.
• W2071302050 cites W1983459809 @default.
• W2071302050 cites W1986066836 @default.
• W2071302050 cites W1986913432 @default.
• W2071302050 cites W1996489819 @default.
• W2071302050 cites W2000705235 @default.
• W2071302050 cites W2011512154 @default.
• W2071302050 cites W2028312424 @default.
• W2071302050 cites W2037235139 @default.
• W2071302050 cites W2038735494 @default.
• W2071302050 cites W2046322851 @default.
• W2071302050 cites W2046938252 @default.
• W2071302050 cites W2062992375 @default.
• W2071302050 cites W2066319604 @default.
• W2071302050 cites W2066720576 @default.
• W2071302050 cites W2070532945 @default.
• W2071302050 cites W2090735319 @default.
• W2071302050 cites W2093966099 @default.
• W2071302050 cites W2099935528 @default.
• W2071302050 cites W2106422602 @default.
• W2071302050 cites W2118204140 @default.
• W2071302050 cites W2123911739 @default.
• W2071302050 cites W2129309475 @default.
• W2071302050 cites W2141739844 @default.
• W2071302050 cites W2163157622 @default.
• W2071302050 cites W4245070842 @default.
• W2071302050 cites W4297845737 @default.
• W2071302050 doi "https://doi.org/10.1118/1.1408619" @default.
• W2071302050 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/11764035" @default.
• W2071302050 hasPublicationYear "2001" @default.
• W2071302050 type Work @default.
• W2071302050 sameAs 2071302050 @default.
• W2071302050 citedByCount "29" @default.
• W2071302050 countsByYear W20713020502012 @default.
• W2071302050 countsByYear W20713020502013 @default.
• W2071302050 countsByYear W20713020502014 @default.
• W2071302050 countsByYear W20713020502015 @default.
• W2071302050 countsByYear W20713020502017 @default.
• W2071302050 countsByYear W20713020502018 @default.
• W2071302050 countsByYear W20713020502021 @default.
• W2071302050 countsByYear W20713020502022 @default.
• W2071302050 countsByYear W20713020502023 @default.
• W2071302050 crossrefType "journal-article" @default.
• W2071302050 hasAuthorship W2071302050A5015237674 @default.
• W2071302050 hasAuthorship W2071302050A5020958460 @default.
• W2071302050 hasAuthorship W2071302050A5021726758 @default.
• W2071302050 hasAuthorship W2071302050A5038649058 @default.
• W2071302050 hasAuthorship W2071302050A5074631503 @default.
• W2071302050 hasAuthorship W2071302050A5089967041 @default.
• W2071302050 hasConcept C110121322 @default.
• W2071302050 hasConcept C121332964 @default.
• W2071302050 hasConcept C126838900 @default.
• W2071302050 hasConcept C134306372 @default.
• W2071302050 hasConcept C136229726 @default.
• W2071302050 hasConcept C143409427 @default.
• W2071302050 hasConcept C146957229 @default.
• W2071302050 hasConcept C153294291 @default.
• W2071302050 hasConcept C192562407 @default.
• W2071302050 hasConcept C2778212899 @default.
• W2071302050 hasConcept C2779949491 @default.
• W2071302050 hasConcept C2989005 @default.
• W2071302050 hasConcept C31601959 @default.
• W2071302050 hasConcept C33923547 @default.
• W2071302050 hasConcept C509974204 @default.
• W2071302050 hasConcept C71924100 @default.
• W2071302050 hasConcept C73000952 @default.
• W2071302050 hasConceptScore W2071302050C110121322 @default.
• W2071302050 hasConceptScore W2071302050C121332964 @default.
• W2071302050 hasConceptScore W2071302050C126838900 @default.
• W2071302050 hasConceptScore W2071302050C134306372 @default.
• W2071302050 hasConceptScore W2071302050C136229726 @default.
• W2071302050 hasConceptScore W2071302050C143409427 @default.
• W2071302050 hasConceptScore W2071302050C146957229 @default.
• W2071302050 hasConceptScore W2071302050C153294291 @default.
• W2071302050 hasConceptScore W2071302050C192562407 @default.
• W2071302050 hasConceptScore W2071302050C2778212899 @default.
• W2071302050 hasConceptScore W2071302050C2779949491 @default.
• W2071302050 hasConceptScore W2071302050C2989005 @default.
• W2071302050 hasConceptScore W2071302050C31601959 @default.
• W2071302050 hasConceptScore W2071302050C33923547 @default.
• W2071302050 hasConceptScore W2071302050C509974204 @default.
• W2071302050 hasConceptScore W2071302050C71924100 @default.
• W2071302050 hasConceptScore W2071302050C73000952 @default.
• W2071302050 hasIssue "11" @default.

• next