An optimization algorithm for 3D real‐time lung tumor tracking during arc therapy using kV projection images. Issue 10 (19th September 2013)

• Record Type: Journal Article
• Authors: Zhuang, Ling; Liang, Jian; Yan, Di; Zhang, Tiezhi; Marina, Ovidiu; Ionascu, Dan
• Is Part Of: Medical physics. Volume 40:Issue 10(2013)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2013-09-19
• Subjects: Dose‐volume analysis -- Computed tomography -- Numerical optimization; computerised tomography -- dosimetry -- lung -- optimisation -- phantoms -- radiation therapy -- tumours; CBCT -- VMAT -- lung tumor tracking -- kV projection images; Computerised tomographs -- Radiation therapy -- Biological material, e.g. blood, urine; Haemocytometers; Medical imaging -- Cancer -- Lungs -- Cone beam computed tomography -- Computed tomography -- Real time information delivery -- Medical image reconstruction -- Image reconstruction -- Digital radiography -- Selected area electron diffraction; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4821545
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: : To develop a real‐time markerless 3D tumor tracking using kilovoltage (kV) cone‐beam CT (CBCT) projection images during volumetric modulated arc therapy (VMAT) treatment of lung tumors. Methods: : The authors have developed a method to identify the position of lung tumors during VMAT treatment, where the current mean 3D position is detected and subsequently the real time 3D position is obtained. The mean position is evaluated by iteratively minimizing an observation error function between the tumor coordinate detected in the imaging plane and the coordinate of the corresponding projection of the estimated mean position. The 3D trajectory is reconstructed using the same optimization formalism, where an observation error function is minimized for tumor positions confined within a predefined amplitude bin as determined from the superior‐inferior tumor motion. Dynamic phantom experiments were performed and image data acquired during patient treatment were analyzed to characterize the reconstruction ability of the proposed method. Results: : The proposed algorithm needs to acquire kV projection data until a certain gantry angle is passed through, termed the black‐out angle, before accurate estimation mean 3D tumor position is possible. The black‐out angle for the mean position method is approximately 20°, while for the 3D trajectory reconstruction an additional ∼15° is required. The mean 3D position and 3D trajectory reconstruction are accurate within ±0.5 mm. Conclusions: : The authors present a real‐time tracking framework to locate lung tumors during VMAT treatment using an optimization algorithm applied to CBCT kV projection images taken concomitantly with the treatment delivery. The authors' technique does not introduce significant additional dose and can be used for real‐time treatment monitoring.