Dense feature-based motion estimation in MV fluoroscopy during dynamic tumor tracking treatment: preliminary study on...

Quality assurance solutions to complement available motion compensation technologies are central for their safe routine implementation and success of treatment. This work presents a dense feature-based method for soft-tissue tumor motion estimation in megavoltage (MV) beam's-eye-view (BEV) projections for potential intra-treatment monitoring during dynamic tumor tracking (DTT). Dense sampling and matching principles were employed to track a gridded set of features landmarks (FLs) in MV-BEV projections and estimate tumor motion, capable to overcome reduced field aperture and partial occlusion challenges. The algorithm's performance was evaluated by retrospectively applying it to fluoroscopic sequences acquired at ∼2 frames s^-1 (fps) for a dynamic phantom and two lung stereotactic body radiation therapy (SBRT) patients treated with DTT on the Vero SBRT system. First, a field-specific train image is initialized by sampling the tumor region at, S, pixel intervals on a grid using a representative frame from a stream of query frames. Sampled FLs are locally characterized in the form of descriptor vectors and geometric attributes representing the target. For motion tracking, subsequent query frames are likewise sampled, corresponding feature descriptors determined, and then patch-wise matched to the training set based on their descriptors and geometric relationships. FLs with high correspondence are pruned and used to estimate tumor displacement. In scenarios of partial occlusions, position is estimated from the set of correctly (visible) FLs on past observations. Reconstructed trajectories were benchmarked against ground-truth manual tracking using the root-mean-square (RMS) as a metric of positional accuracy. A total of 19 fluoroscopy sequences were analyzed. This included scenarios of field aperture obstruction during three-dimensional conformal, as well as step-and-shoot intensity modulated radiotherapy (IMRT) delivery assisted with DTT. The algorithm resolved target motion satisfactorily. The RMS was <1.2 mm and <1.8 mm for the phantom and the clinical dataset, respectively. Dense tracking showed promising results to overcome localization challenges at the field penumbra and partial obstruction by multi-leaf collimator (MLC). Motion retrieval was possible in ∼66% of the control points studied. In addition to MLC obstruction, changes in the external/internal breathing dynamics and baseline drifts were a major source of estimation bias. Dense feature-based tracking is a viable alternative. The algorithm is rotation-/scale-invariant and robust to photometric changes. Tracking multiple features may help overcome partial occlusion challenges by the MLC. This in turn opens up new possibilities for motion detection and intra-treatment monitoring during IMRT and potentially VMAT.