title: Adapting liver motion models using a navigator channel technique.
abstract: Deformable registration can improve the accuracy of tumor targeting; however for online applications, efficiency as well as accuracy is important. A navigator channel technique has been developed to combine a biomechanical model-based deformable registration algorithm with a population motion model and patient specific motion information to perform fast deformable registration for application in image-guided radiation therapy. A respiratory population-based liver motion model was generated from breath-hold CT data sets of ten patients using a finite element model as a framework. The population model provides a biomechanical reference template of the average liver motions, which were found to be (absolute mean +/-SD) 0.12 +/- 0.10, 0.84 +/- 0.13, and 1.24 +/- 0.18 cm in the left-right (LR), anterior-posterior (AP), and superior-inferior (SI) directions, respectively. The population motion model was then adapted to the specific liver motion of 13 patients based on their exhale and inhale CT images. The patient motion was calculated using a navigator channel (a narrow region of interest window) on liver boundaries in the images. The absolute average accuracy of the navigator channel to predict the 1D SI and AP motions of the liver was less than 0.11, which is less than the out-of-plane image voxel size, 0.25 cm. This 1D information was then used to adapt the 4D population motion model in the SI and AP directions to predict the patient specific liver motion. The absolute average residual error of the navigator channel technique to adapt the population motion to the patients' specific motion was verified using three verification methods: (1) vessel bifurcation, (2) tumor center of mass, and (3) MORFEUS deformable algorithm. All three verification methods showed statistically similar results where the technique's accuracy was approximately on the order of the voxel image sizes. This method has potential applications in online assessment of motion at the time of treatment to improve image-guided radiotherapy and monitoring of intrafraction motion.
authors.affiliation: Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5G 3E2, Canada. thao-nguyen.nguyen@rmp.uhn.on.ca
authors.forename: T N
authors.initials: TN
authors.lastname: Nguyen
authors.forename: J L
authors.initials: JL
authors.lastname: Moseley
authors.forename: L A
authors.initials: LA
authors.lastname: Dawson
authors.forename: D A
authors.initials: DA
authors.lastname: Jaffray
authors.forename: K K
authors.initials: KK
authors.lastname: Brock
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D005260:Female; D020342:Finite Element Analysis; D006801:Humans; D008099:Liver / Q000000981:diagnostic imaging* / Q000502:physiology*; D008297:Male; D008875:Middle Aged; D009038:Motion; D011878:Radiotherapy / Q000379:methods; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods; D015203:Reproducibility of Results; D012984:Software; D014057:Tomography, X-Ray Computed / Q000379:methods*