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dc.contributor.authorMirzapour, Azam
dc.contributor.authorMazur, Thomas R.
dc.contributor.authorSharp, Gregory
dc.contributor.authorSalari, Ehsan
dc.date.accessioned2019-10-03T14:08:22Z
dc.date.available2019-10-03T14:08:22Z
dc.date.issued2019-09-23
dc.identifier.citationMirzapour, Azam; Mazur, Thomas R.; Sharp, Gregory; Salari, Ehsan. 2019. Intra-fraction motion prediction in MRI-guided radiation therapy using Markov processes. Physics in Medicine & Biology, vol. 64:no. 19:p. 195006en_US
dc.identifier.issn1361-6560
dc.identifier.urihttps://doi.org/10.1088/1361-6560/ab37a9
dc.identifier.urihttp://hdl.handle.net/10057/16732
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractInternal organ motion during radiation delivery may lead to underdosing of cancer cells or overdosing of normal tissue, potentially causing treatment failure or normal-tissue toxicity. Organ motion is of particular concern in the treatment of lung and abdominal cancers, where breathing induces large tumor displacement and organ deformation. A new generation of radiotherapy devices is equipped with on-board MRI scanners to acquire a real-time movie of the patient's anatomy during radiation delivery. The goal of this research is to develop, calibrate, and test motion predictive models that employ real-time MRI images to provide the short-term trajectory of respiration-induced anatomical motion during radiation delivery. A semi-Markov model predicts transitions between the phases of a respiratory cycle, and a Markov model predicts transitions to future respiratory cycles, leading to accurate motion forecasting over longer-term horizons. The intended application for this work is real-time tracking and re-optimization of intensity-modulated radiation delivery.en_US
dc.language.isoen_USen_US
dc.publisherNLM (Medline)en_US
dc.relation.ispartofseriesPhysics in Medicine & Biology;v.64:no.19
dc.titleIntra-fraction motion prediction in MRI-guided radiation therapy using Markov processesen_US
dc.typeArticleen_US
dc.rights.holder© 2019 Institute of Physics and Engineering in Medicineen_US


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