TH-E-17A-10: Markerless Lung Tumor Tracking Based On Beams Eye View EPID Images

Journal Article · Sun Jun 15 04:00:00 EDT 2014 · Medical Physics

DOI:https://doi.org/10.1118/1.4889685· OSTI ID:22412522

Chiu, T; Kearney, V; Liu, H; Jiang, L; Foster, R; Mao, W ^[1]; Rozario, T; Bereg, S ^[2]; Klash, S ^[3]

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1. UT Southwestern Medical Center, Dallas, Texas (United States)
2. University of Texas at Dallas, Richardson, Texas (United States)
3. Premier Cancer Centers, Dallas, TX (United States)

Purpose: 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 online markerless lung tumor motion tracking method. Methods: 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. Results: 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. Conclusion: 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. This project has been supported by a Varian Research Grant.

• OSTI ID: 22412522
• Journal Information: Medical Physics, Journal Name: Medical Physics Journal Issue: 6 Vol. 41; ISSN 0094-2405; ISSN MPHYA6
• Country of Publication: United States
• Language: English

Related Subjects

60 APPLIED LIFE SCIENCES
ACCURACY
ANATOMY
CAT SCANNING
CT-GUIDED RADIOTHERAPY
LINEAR ACCELERATORS
LUNGS
NEOPLASMS
PATIENTS
PHANTOMS
PLANNING