Monitoring tumor motion with on-line mega-voltage cone-beam computed tomography imaging in a cine mode.

Department of Radiation Oncology, Allegheny General Hospital, Pittsburgh, PA 15212, USA.
Physics in Medicine and Biology (Impact Factor: 2.7). 03/2008; 53(4):823-36. DOI: 10.1088/0031-9155/53/4/001
Source: PubMed

ABSTRACT Accurate daily patient localization is becoming increasingly important in external-beam radiotherapy (RT). Mega-voltage cone-beam computed tomography (MV-CBCT) utilizing a therapy beam and an on-board electronic portal imager can be used to localize tumor volumes and verify the patient's position prior to treatment. MV-CBCT produces a static volumetric image and therefore can only account for inter-fractional changes. In this work, the feasibility of using the MV-CBCT raw data as a fluoroscopic series of portal images to monitor tumor changes due to e.g. respiratory motion was investigated. A method was developed to read and convert the CB raw data into a cine. To improve the contrast-to-noise ratio on the MV-CB projection data, image post-processing with filtering techniques was investigated. Volumes of interest from the planning CT were projected onto the MV-cine. Because of the small exposure and the varying thickness of the patient depending on the projection angle, soft-tissue contrast was limited. Tumor visibility as a function of tumor size and projection angle was studied. The method was well suited in the upper chest, where motion of the tumor as well as of the diaphragm could be clearly seen. In the cases of patients with non-small cell lung cancer with medium or large tumor masses, we verified that the tumor mass was always located within the PTV despite respiratory motion. However for small tumors the method is less applicable, because the visibility of those targets becomes marginal. Evaluation of motion in non-superior-inferior directions might also be limited for small tumor masses. Viewing MV-CBCT data in a cine mode adds to the utility of MV-CBCT for verification of tumor motion and for deriving individualized treatment margins.

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