TY - JOUR
T1 - Intra-fraction motion prediction in MRI-guided radiation therapy using Markov processes
AU - Ali Mirzapour, Seyed
AU - Mazur, Thomas
AU - Sharp, Gregory
AU - Salari, Ehsan
N1 - Publisher Copyright:
© 2019 Institute of Physics and Engineering in Medicine.
PY - 2019/9/23
Y1 - 2019/9/23
N2 - Internal 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.
AB - Internal 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.
KW - MRI-guided radiotherapy
KW - Markov processes
KW - intra-fraction motion
KW - motion predictive model
UR - http://www.scopus.com/inward/record.url?scp=85072629008&partnerID=8YFLogxK
U2 - 10.1088/1361-6560/ab37a9
DO - 10.1088/1361-6560/ab37a9
M3 - Article
C2 - 31370053
AN - SCOPUS:85072629008
SN - 0031-9155
VL - 64
JO - Physics in medicine and biology
JF - Physics in medicine and biology
IS - 19
M1 - 195006
ER -