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https://www.selleckchem.com/TGF-beta.html 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 stre mm and less then 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.This paper presents a tendon-driven robotic finger with its inspiration derived from the human extensor mechanism. The analytical model present

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