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Purpose: The purpose of this work was to report on the geometric uncertainty for patients treated with multi-leaf collimator (MLC) tracking for lung SABR to verify the accuracy of the system., Methods: Seventeen patients were treated as part of the MLC tracking for lung SABR clinical trial using electromagnetic beacons implanted around the tumor acting as a surrogate for target motion. Sources of uncertainties evaluated in the study included the surrogate-target positional uncertainty, the beam-surrogate tracking uncertainty, the surrogate localization uncertainty, and the target delineation uncertainty. Probability density functions (PDFs) for each source of uncertainty were constructed for the cohort and each patient. The total PDFs was computed using a convolution approach. The 95% confidence interval (CI) was used to quantify these uncertainties., Results: For the cohort, the surrogate-target positional uncertainty 95% CIs were ±2.5 mm (-2.0/3.0 mm) in left-right (LR), ±3.0 mm (-1.6/4.5 mm) in superior-inferior (SI) and ±2.0 mm (-1.8/2.1 mm) in anterior-posterior (AP). The beam-surrogate tracking uncertainty 95% CIs were ±2.1 mm (-2.1/2.1 mm) in LR, ±2.8 mm (-2.8/2.7 mm) in SI and ±2.1 mm (-2.1/2.0 mm) in AP directions. The surrogate localization uncertainty minimally impacted the total PDF with a width of ±0.6 mm. The target delineation uncertainty distribution 95% CIs were ±5.4 mm. For the total PDF, the 95% CIs were ±5.9 mm (-5.8/6.0 mm) in LR, ±6.7 mm (-5.8/7.5 mm) in SI and ±6.0 mm (-5.5/6.5 mm) in AP., Conclusion: This work reports the geometric uncertainty of MLC tracking for lung SABR by accounting for the main sources of uncertainties that occurred during treatment. The overall geometric uncertainty is within ±6.0 mm in LR and AP directions and ±6.7 mm in SI. The dominant uncertainty was the target delineation uncertainty. This geometric analysis helps put into context the range of uncertainties that may be expected during MLC tracking for lung SABR (ClinicalTrials.gov registration number: NCT02514512).

In this study we develop and characterise a six degree-of-freedom (6 DoF) robotic motion system for quality assurance of real-time image-guided radiotherapy techniques. The system consists of a commercially available robotic arm, an acrylic phantom with embedded Calypso markers, a custom base plate to mount the robot to the treatment couch, and control software implementing the appropriate sequence of transformations to reproduce measured tumour motion traces. The robotic motion system was evaluated in terms of the set-up and motion trace repeatability, static localization accuracy and dynamic localization accuracy. Four prostate, two liver and three lung motion traces, representing a range of tumor motion trajectories recorded in real patient treatments, were executed using the robotic motion system and compared with motion measurements from the clinical Calypso motion tracking system. System set-up and motion trace repeatability was better than 0.5 deg and 0.3 mm for rotation and translation, respectively. The static localization accuracy of the robotic motion system in the LR, SI and AP directions was 0.09 mm, 0.08 mm and 0.02 mm for translations, respectively, and 0.2°, 0.06° and 0.06° for rotations, respectively. The dynamic localization accuracy of the robotic motion system was  <0.2 mm and  <0.6° for translations and rotations, respectively. Thus, we have demonstrated the ability to accurately mimic rigid-body tumor motion using a robotically controlled phantom to provide precise geometric QA for advanced radiotherapy delivery approaches.

Increasing evidence shows that intrafraction tumour motion monitoring must include both six degrees of freedom (6DoF): 3D translations and 3D rotations. Existing real-time algorithms for 6DoF target motion estimation require continuous intrafraction fluoroscopic imaging at high frequency, thereby exposing patients to additional high imaging dose. This paper presents the first method capable of 6DoF motion monitoring using intermittent 2D kV imaging and a continuous external respiratory signal. Our approach is to optimise a state-augmented linear correlation model between an external signal and internal 6DoF motion. In standard treatments, the model can be built using information obtained during pre-treatment cone beam CT (CBCT). Real-time 6DoF tumor motion can then be estimated using just the external signal. Intermittent intrafraction kV images are used to update the model parameters, accounting for changes in correlation and baseline shifts. The method was evaluated in silico using data from 6 lung SABR patients, with the internal tumour motion recorded with electromagnetic beacons and the external signal from a bellows belt. Projection images from CBCT (10 Hz) and intermittent kV images were simulated by projecting the 3D Calypso beacon positions onto an imager. IMRT and VMAT treatments were simulated with increasing imaging update intervals: 0.1 s, 1 s, 3 s, 10 s and 30 s. For all the tested clinical scenarios, translational motion estimates with our method had sub-mm accuracy (mean) and precision (standard deviation) while rotational motion estimates were accurate to  <[Formula: see text] and precise to [Formula: see text]. Motion estimation errors increased as the imaging update interval increased. With the largest imaging update interval (30 s), the errors were [Formula: see text] mm, [Formula: see text] mm and [Formula: see text] mm for translation in the left-right, superior-inferior and anterior-posterior directions, respectively, and [Formula: see text], [Formula: see text] and [Formula: see text] for rotation around the aforementioned axes for both VMAT and IMRT treatments. In conclusion, we developed and evaluated a novel method for highly accurate real-time 6DoF motion monitoring on a standard linear accelerator without requiring continuous kV imaging. The proposed method achieved sub-mm and sub-degree accuracy on a lung cancer patient dataset.

Stereotactic arrhythmia radioablation (STAR) is an emerging treatment option for atrial fibrillation (AF). However, it faces possibly the most challenging motion compensation scenario: both respiratory and cardiac motion. Multi-leaf collimator (MLC) tracking is clinically used for lung cancer treatments but its capabilities with intracardiac targets is unknown. We report the first experimental results of MLC tracking for intracardiac targets. Five AF STAR plans of varying complexity were created. All delivered 5  ×  10 Gy to both pulmonary vein antra. Three healthy human target motion trajectories were acquired with ultrasound and programmed into a motion platform. Plans were delivered with a linac to a dosimeter placed on the motion platform. For each motion trace, each plan was delivered with no MLC tracking and with MLC tracking with and without motion prediction. Dosimetric accuracy was assessed with γ-tests and dose metrics. MLC tracking improved the dosimetric accuracy in all measurements compared to non-tracking experiments. The average 2%/2 mm γ-failure rate was improved from 13.1% with no MLC tracking to 5.9% with MLC tracking (p  <  0.001) and 7.2% with MLC tracking and no motion prediction (p  <  0.001). MLC tracking significantly improved the consistency between planned and delivered target dose coverage. The 95% target coverage with the prescription dose (V100) was improved from 60% of deliveries with no MLC tracking to 80% of deliveries with MLC tracking (p  =  0.03). MLC tracking was successfully implemented for the first time for intracardiac motion compensation. MLC tracking provided significant dosimetric accuracy improvements in AF STAR experiments, even with challenging cardiac and respiratory-induced target motion and complex treatment plans. These results warrant further investigation and optimisation of MLC tracking for intracardiac target motion compensation.

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 model. The 2D tumor position on the kV image is then measured by template matching. Finally, the prediction and 2D measurement are combined based on the 3D distribution of tumor positions in the past 10 s and the estimated uncertainty of template matching. To investigate the clinical feasibility of the proposed method, a total of 13 lung cancer patient datasets were used for retrospective validation, including 11 cone-beam CT scan pairs and two stereotactic ablative body radiotherapy cases. The ground truths for tumor motion were generated from the the 3D trajectories of implanted markers or beacons. The mean, standard deviation, and 95th percentile of the 3D tracking error were found to range from 1.6-2.9 mm, 0.6-1.5 mm, and 2.6-5.8 mm, respectively. Markerless tumor tracking always resulted in smaller errors compared to the standard of care. The improvement was the most pronounced in the superior-inferior (SI) direction, with up to 9.5 mm reduction in the 95th-percentile SI error for patients with  >10 mm 5th-to-95th percentile SI tumor motion. The percentage of errors with 3D magnitude  <5 mm was 96.5% for markerless tumor tracking and 84.1% for the standard of care. The feasibility of 3D markerless tumor tracking has been demonstrated on realistic clinical scenarios for the first time. The clinical implementation of the proposed method will enable more accurate and precise lung radiotherapy using existing hardware and workflow. Future work is focused on the clinical and real-time implementation of this method.