Lung diaphragm tracking in CBCT images using spatio-temporal MRF. (October 2016) - Sundarapandian, Manivannan; Kalpathi, Ramakrishnan; Siochi, R. Alfredo C.; Kadam, Amrut S.
Abstract : Highlights: We propose a method for tracking the lung diaphragm position on CBCT projection images. The diaphragm state is modeled as a spatio-temporal Markov Random Field. The associated energy minimization problem is solved using graph-cuts. On clinical datasets, our method outperforms the full search method in terms of accuracy. A GPU based implementation of our method achieves 16% acceleration over the benchmark. Abstract: In EBRT in order to monitor the intra fraction motion of thoracic and abdominal tumors, one of the standard approaches is to use the lung diaphragm apex as an internal marker. However, tracking the position of the apex from image based observations is a challenging problem, as it undergoes both position and shape variation. The purpose of this paper is to propose an alternative method for tracking the ipsi-lateral hemidiaphragm apex (IHDA) position on Cone Beam Computed Tomography (CBCT) projection images. A hierarchical method is proposed to track the IHDA position across the frames. The diaphragm state is modeled as a spatio-temporal Markov Random Field (MRF). The likelihood function is derived from the votes based on 4D-Hough space. The optimal state of the diaphragm is obtained by solving the associated energy minimization problem using graph-cuts. A heterogeneous GPU implementation is provided for the method using CUDA framework and the performance is compared with that of CPU implementation. The method was tested using 15 clinical CBCT images. The results demonstrate that the MRF formulation outperforms the full search method in terms of accuracy. The GPU based heterogeneous implementation of the proposed algorithm takes about 25 s, which is 16% improvement over the existing benchmark. The proposed MRF formulation considers all the possible combinations from the 4D-Hough space and therefore results in better tracking accuracy. The GPU based implementation exploits the inherent parallelism in our algorithm to accelerate the performance thereby increasing the viability of the approach for clinical use.