In this retrospective analysis of motion data, it is demonstrated that the system is capable of determining tumor positions in the plane perpendicular to the beam direction without the aid of fiducial markers, and may hence be suitable as an online verification tool in RGRT. It may be possible to use the tracking information to enable on-the-fly corrections to intra-/inter-beam variations by adapting the gating window by means of a robotic couch.

The Vero linear accelerator delivers dynamic tumor tracking (DTT) treatment using a gimbal motion. However, the availability of treatment planning systems (TPS) to simulate DTT is limited. This study aims to implement and verify the gimbal tracking beam geometry in the dose calculation. Gimbal tracking was implemented by rotating the reference CT outside the TPS according to the ring, gantry, and gimbal tracking position obtained from the tracking log file. The dose was calculated using these rotated CTs. The geometric accuracy was verified by comparing calculated and measured film response using a ball bearing phantom. The dose was verified by comparing calculated 2D dose distributions and film measurements in a ball bearing and a homogeneous phantom using a gamma criterion of 2%/2 mm. The effect of implementing the gimbal tracking beam geometry in a 3D patient data dose calculation was evaluated using dose volume histograms (DVH). Geometrically, the gimbal tracking implementation accuracy was <0.94 mm. The isodose lines agreed with the film measurement. The largest dose difference of 9.4% was observed at maximum tilt positions with an isocenter and target separation of 17.51 mm. Dosimetrically, gamma passing rates were >98.4%. The introduction of the gimbal tracking beam geometry in the dose calculation shifted the DVH curves by 0.05%-1.26% for the phantom geometry and by 5.59% for the patient CT dataset. This study successfully demonstrates a method to incorporate the gimbal tracking beam geometry into dose calculations. By combining CT rotation and MU distribution according to the log file, the TPS was able to simulate the Vero tracking treatment dose delivery. The DVH analysis from the gimbal tracking dose calculation revealed changes in the dose distribution during gimbal DTT that are not visible with static dose calculations.

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.