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 ) , , and 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 CTimages. 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.
The authors extend their thanks to Dr. Doug Moseley and Graham Wilson for assistance in MATLAB programming and for technical support. The authors would also like to acknowledge Michael Velec for his assistance in acquiring patient image data and to Dr. Jeffrey H. Siewerdsen and Dr. Anne Martel for their valued guidance on this investigation. This research was supported by the National Cancer Institute of Canada—Terry Fox Foundation, Elekta Oncology Systems, Crawley, U.K, and NIH 5RO1CA124714-02.
II.A. Patient data
II.B. Image data
II.C. MORFEUS: FEM based deformation registration algorithm
II.D. 4D population motion deformation model
II.E. Quantification of motion using navigator channels
II.F. Patient specific motion and the adaptation of the population motion model
III.A. Population model of liver motion
III.B. Accuracy of the motion detection technique
III.C. Accuracy of the adapted-population motion models
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