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Marker Free Lung Tumor Motion Tracking by An Active Contour Model On Cone Beam CT Projections for Stereotactic Body Radiation Therapy of Lung Cancer

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M Chao

M Chao1*, J Wei2 , Y Yuan1 , Y Lo1 , (1) The Mount Sinai Medical Center, New York, NY, (2) City College of New York, New York, NY


SU-G-BRA-10 (Sunday, July 31, 2016) 4:00 PM - 6:00 PM Room: Ballroom A

Purpose: To develop a novel strategy to extract the lung tumor motion from cone beam CT (CBCT) projections by an active contour model with interpolated respiration learned from diaphragm motion.

Methods: Tumor tracking on CBCT projections was accomplished with the templates derived from planning CT (pCT). There are three major steps in the proposed algorithm: 1) The pCT was modified to form two CT sets: a tumor removed pCT and a tumor only pCT, the respective digitally reconstructed radiographs DRRtr and DRRto following the same geometry of the CBCT projections were generated correspondingly. 2) The DRRtr was rigidly registered with the CBCT projections on the frame-by-frame basis. Difference images between CBCT projections and the registered DRRtr were generated where the tumor visibility was appreciably enhanced. 3) An active contour method was applied to track the tumor motion on the tumor enhanced projections with DRRto as templates to initialize the tumor tracking while the respiratory motion was compensated for by interpolating the diaphragm motion estimated by our novel constrained linear regression approach. CBCT and pCT from five patients undergoing stereotactic body radiotherapy were included in addition to scans from a Quasar phantom programmed with known motion. Manual tumor tracking was performed on CBCT projections and was compared to the automatic tracking to evaluate the algorithm accuracy.

Results: The phantom study showed that the error between the automatic tracking and the ground truth was within 0.2mm. For the patients the discrepancy between the calculation and the manual tracking was between 1.4 and 2.2 mm depending on the location and shape of the lung tumor. Similar patterns were observed in the frequency domain.

Conclusion: The new algorithm demonstrated the feasibility to track the lung tumor from noisy CBCT projections, providing a potential solution to better motion management for lung radiation therapy.

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