Program Information
BEST IN PHYSICS (JOINT IMAGING-THERAPY): An Integrated Model-Based Intrafractional Organ Motion Tracking Approach with Dynamic MRI in Head and Neck Radiotherapy
H Chen1*, S Dolly1 , J Victoria2 , M Anastasio1 , S Ruan3 , D Low4 , H Li1 , H Wooten1 , J Dempsey2 , H Gay1 , S Mutic1 , W Thorstad1 , H Li1 , (1) Washington University School of Medicine, Saint Louis, MO, USA (2) ViewRay incorporated, Oakwood Village, Ohio, USA (3) University of Rouen, QuantIF - EA 4108 LITIS, Rouen, France (4) Deparment of Radiation Oncology, University of California Los Angeles, Los Angeles, CA USA
Presentations
WE-G-BRD-4 (Wednesday, July 15, 2015) 4:30 PM - 6:00 PM Room: Ballroom D
Purpose:
In-treatment dynamic cine images, provided by the first commercially available MRI-guided radiotherapy system, allow physicians to observe intrafractional motion of head and neck (H&N) internal structures. Nevertheless, high anatomical complexity and relatively poor cine image contrast/resolution have complicated automatic intrafractional motion evaluation. We proposed an integrated model-based approach to automatically delineate and analyze moving structures from on-board cine images.
Methods:
The H&N upper airway, a complex and highly deformable region wherein severe internal motion often occurs, was selected as the target-to-be-tracked. To reliably capture its motion, a hierarchical structure model containing three statistical shapes (face, face-jaw, and face-jaw-palate) was first built from a set of manually delineated shapes using principal component analysis. An integrated model-fitting algorithm was then employed to align the statistical shapes to the first to-be-detected cine frame, and multi-feature level-set contour propagation was performed to identify the airway shape change in the remaining frames. Ninety sagittal cine MR image sets, acquired from three H&N cancer patients, were utilized to demonstrate this approach.
Results:
The tracking accuracy was validated by comparing the results to the average of two manual delineations in 20 randomly selected images from each patient. The resulting dice similarity coefficient (93.28+/-1.46 %) and margin error (0.49+/-0.12 mm) showed good agreement with the manual results. Intrafractional displacements of anterior, posterior, inferior, and superior airway boundaries were observed, with values of 2.62+/-2.92, 1.78+/-1.43, 3.51+/-3.99, and 0.68+/-0.89 mm, respectively. The H&N airway motion was found to vary across directions, fractions, and patients, and highly correlated with patients’ respiratory frequency.
Conclusion:
We proposed the integrated computational approach, which for the first time allows to automatically identify the H&N upper airway and quantify in-treatment H&N internal motion in real-time. This approach can be applied to track other structures’ motion, and provide guidance on patient-specific prediction of intra-/inter-fractional structure displacements.
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