Adapting population liver motion models for individualized online image-guided therapy.
Respiratory motion varies on a daily basis in abdominal cancer patients, affecting the ability to successfully deliver local therapy and requiring increased treatment margins to account for this variation. Deformable registration techniques can accurately describe respiratory motion, however, online application can be limited by long computational times and user intervention. A technique has been developed to quickly quantify patient breathing motion from respiratory-sorted volumetric images by calculating 1D shifts in image intensities between spatially corresponding regions of interest (navigator channels) on patient's images. The 1D motion at the superior, inferior, anterior, and posterior liver edges was detected and applied to adapt a population liver respiratory motion model. For validation, deformable registration was performed for each patient using a validated technique, MORFEUS, for relative validation, and vessel bifurcations, identified on patient's inhale and exhale images, for absolute validation. The accuracy of the adapted-population model to describe the patient respiratory motion was (absolute mean +/- SD) 0.26 +/- 0.11 cm and 0.30 +/- 0.21 cm in the superior-inferior (SI) and anterior-posterior (AP) directions, respectively. The accuracy of predicting the tumor COM motion was 0.30 +/- 0.22 cm, and 0.34 +/- 0.31, while the absolute validation, based on bifurcations was 0.26 +/- 0.16 cm and 0.13 +/- 0.04 cm in the SI and AP directions, respectively. This technique was developed to complement and quickly adapt a full 3D biomechanical based deformable registration technique, MORFEUS, to be applied in the online setting.
Nguyen
Thao-Nguyen
TN
Department of Medical Biophysics, University of Toronto, Ontario, Canada. Thao-Nguyen-Nguyen@rmp.uhn.on.ca
Moseley
Joanne L
JL
Dawson
Laura A
LA
Jaffray
David A
DA
Brock
Kristy K
KK
D000465:Algorithms; D003198:Computer Simulation; D020342:Finite Element Analysis; D006801:Humans; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008113:Liver Neoplasms / Q000175:diagnosis* / Q000503:physiopathology / Q000532:radiotherapy; D015233:Models, Statistical; D009038:Motion; D019047:Phantoms, Imaging; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012984:Software