(Monday, 7/15/2019) 7:30 AM - 9:30 AM
Room: 221AB
Purpose: As the predominant driver of respiratory motion, the diaphragm represents a key surrogate for motion management during lung cancer irradiation. Existing approaches to diaphragm tracking often produce phase-based estimates, suffer from lateral side failures or are not executable in real-time. This work aims to develop an algorithm that continuously produces real-time estimates of absolute diaphragm position during lung cancer radiotherapy.
Methods: Patient-specific 3D diaphragm models were generated by automatically segmenting end-exhale 4DCT images. The trajectory of diaphragmatic motion was estimated by registering end-exhale to end-inhale 4DCT images. 2D diaphragm masks were generated by forward-projecting 3D models over the complement of angles spanned during kV image acquisition. For each kV image, absolute diaphragm position was determined using a maximal gradient search. This was achieved by shifting angle-matched 2D masks along the estimated motion trajectory and selecting the position of highest contrast on a vertical difference image. To demonstrate the efficacy of this algorithm for real-time motion management, diaphragm-target motion models were built by retrospectively correlating tracked diaphragm positions with target positions determined using either fiducial markers or electromagnetic transponder beacons. The algorithm was evaluated using kV images acquired on a standard linear accelerator before and during treatment. This included 22 CBCT image sequences and 3 intrafraction sequences for 6 lung cancer patients. Root-mean-squared errors (RMSEs) in the 3D target positions were computed for each sequence and computational latency was recorded for each image.
Results: For the CBCT sequences, RMSEs for the diaphragm-target motion models ranged over 0.70-3.12mm. For the intrafraction sequences, containing MV scatter and acquired over a constrained field-of-view, the RMSEs ranged over 3.94-4.27mm. Computational latency ranged over 80-120ms.
Conclusion: Our algorithm produces continuous, real-time estimates of absolute diaphragm position that enable 3D target tracking. By monitoring the diaphragm directly, this work facilitates motion management during lung cancer radiotherapy.
Funding Support, Disclosures, and Conflict of Interest: This work was supported by the Australian Government National Health and Medical Research Council Early Career Fellowship APP1120333 and Cancer Institute New South Wales Early Career Fellowship CS00481.
Keywords
Respiration, Image-guided Therapy, Fluoroscopy
Taxonomy
IM/TH- RT X-ray Imaging: CBCT imaging/therapy implementation
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