A Bayesian approach for three-dimensional markerless tumor tracking using kV imaging during lung radiotherapy.

ABSTRACT: The ability to monitor tumor motion without implanted markers can potentially enable broad access to more accurate and precise lung radiotherapy. A major challenge is that kilovoltage (kV) imaging based methods are rarely able to continuously track the tumor due to the inferior tumor visibility on 2D kV images. Another challenge is the estimation of 3D tumor position based on only 2D imaging information. The aim of this work is to address both challenges by proposing a Bayesian approach for markerless tumor tracking for the first time. The proposed approach adopts the framework of the extended Kalman filter, which combines a prediction and measurement steps to make the optimal tumor position update. For each imaging frame, the tumor position is first predicted by a respiratory-correlated m

SUBMITTER: Shieh CC 

PROVIDER: S-EPMC5729104 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature