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Markerless Lung Tumor Tracking Based On Beams Eye View EPID Images

T Chiu

T Chiu1*, T Rozario2 , S Bereg2 , S Klash3 , V Kearney1 , H Liu1 , L Jiang1 , R Foster1 , W Mao1 , (1) UT Southwestern Medical Center, Dallas, Texas, (2) University of Texas at Dallas, Richardson, Texas, (3) Premier Cancer Centers, Dallas, Texas


TH-E-17A-10 Thursday 1:00PM - 2:50PM Room: 17A

Dynamic tumor tracking or motion compensation techniques have proposed to modify beam delivery following lung tumor motion on the flight. Conventional treatment plan QA could be performed in advance since every delivery may be different. Markerless lung tumor tracking using beams eye view EPID images provides a best treatment evaluation mechanism. The purpose of this study is to improve the accuracy of the on-line markerless lung tumor motion tracking method.

The lung tumor could be located on every frame of MV images during radiation therapy treatment by comparing with corresponding digitally reconstructed radiograph (DRR). A kV-MV CT corresponding curve is applied on planning kV CT to generate MV CT images for patients in order to enhance the similarity between DRRs and MV treatment images. This kV-MV CT corresponding curve was obtained by scanning a same CT electron density phantom by a kV CT scanner and MV scanner (Tomotherapy) or MV CBCT. Two sets of MV DRRs were then generated for tumor and anatomy without tumor as the references to tracking the tumor on beams eye view EPID images.

Phantom studies were performed on a Varian TrueBeam linac. MV treatment images were acquired continuously during each treatment beam delivery at 12 gantry angles by iTools. Markerless tumor tracking was applied with DRRs generated from simulated MVCT. Tumors were tracked on every frame of images and compared with expected positions based on programed phantom motion. It was found that the average tracking error were 2.3 mm.

This algorithm is capable of detecting lung tumors at complicated environment without implanting markers. It should be noted that the CT data has a slice thickness of 3 mm. This shows the statistical accuracy is better than the spatial accuracy.

Funding Support, Disclosures, and Conflict of Interest: This project has been supported by a Varian Research Grant.

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