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 is combined based on the 3D distribution of tumor positions in the past 10 seconds 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 was 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 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.
Purpose Currently, four‐dimensional (4D) cone‐beam computed tomography (CBCT) requires a 3–4 min full‐fan scan to ensure usable image quality. Recent advancements in sparse‐view 4D‐CBCT reconstruction have opened the possibility to reduce scan time and dose. The aim of this study is to provide a common framework for systematically evaluating algorithms for 4D‐CBCT reconstruction from a 1‐min scan. Using this framework, the AAPM‐sponsored SPARE Challenge was conducted in 2018 to identify and compare state‐of‐the‐art algorithms. Methods A clinically realistic CBCT dataset was simulated using patient CT volumes from the 4D‐Lung database. The selected patients had multiple 4D‐CT sessions, where the first 4D‐CT was used as the prior CT, and the rest were used as the ground truth volumes for simulating CBCT projections. A GPU‐based Monte Carlo tool was used to simulate the primary, scatter, and quantum noise signals. A total of 32 CBCT scans of nine patients were generated. Additional qualitative analysis was performed on a clinical Varian and clinical Elekta dataset to validate the simulation study. Participants were blinded from the ground truth, and were given 3 months to apply their reconstruction algorithms to the projection data. The submitted reconstructions were analyzed in terms of root‐mean‐squared‐error (RMSE) and structural similarity index (SSIM) with the ground truth within four different region‐of‐interests (ROI) — patient body, lungs, planning target volume (PTV), and bony anatomy. Geometric accuracy was quantified as the alignment error of the PTV. Results Twenty teams participated in the challenge, with five teams completing the challenge. Techniques involved in the five methods included iterative optimization, motion‐compensation, and deformation of the prior 4D‐CT. All five methods rendered significant reduction in noise and streaking artifacts when compared to the conventional Feldkamp–Davis–Kress (FDK) algorithm. The RMS of the three‐dimensional (3D) target registration error of the five methods ranged from 1.79 to 3.00 mm. Qualitative observations from the Varian and Elekta datasets mostly concur with those from the simulation dataset. Each of the methods was found to have its own strengths and weaknesses. Overall, the MA‐ROOSTER method, which utilizes a 4D‐CT motion model for temporal regularization, had the best and most consistent image quality and accuracy. Conclusion The SPARE Challenge represents the first framework for systematically evaluating state‐of‐the‐art algorithms for 4D‐CBCT reconstruction from a 1‐min scan. Results suggest the potential for reducing scan time and dose for 4D‐CBCT. The challenge dataset and analysis framework are publicly available for benchmarking future reconstruction algorithms.
Background and purpose 32To test the hypothesis that 4DCT and 4DCBCT-measured target motion ranges 33 predict target motion ranges during lung cancer SABR. 34 Materials and methods 35Ten lung SABR patients were implanted with Calypso beacons. 4DCBCT was 36 reconstructed for 29 fractions (1-4fx/patient) from a 1min CBCT scan. The beacon 37 centroid motion segmented for all 4DCT and 4DCBCT bins was compared with the 38 real-time imaging and treatment beacon centroid ("target") motion range (4SDs) for 39 each fraction. We tested the hypotheses that 1) 4DCT and 4CBCT predict treatment 40 motion range and 2) there is no difference between 4DCT and 4DCBCT for 41 predicting treatment motion range. Phase-wise root-mean-square errors (RMSEs) 42 between imaging and treatment motion and reconstructed motion (4DCT,4DCBCT) 43 were calculated. Relationships between motion ranges in 4DCT and 4DCBCT and 44 imaging and treatment motion ranges were investigated for the superior-inferior (SI), 45 left-right (LR) and anterior-posterior (AP) directions. Baseline drifts and amplitude 46 variability were investigated as potential factors leading to motion misrepresentation. 47 Results 48SI 4DCT, 4DCBCT, imaging and treatment motion ranges were 6.33.6 mm, 49 7.14.5 mm, 11.17.5 mm and 10.96.9 mm, respectively. Similar 4DCT and 50 4DCBCT under-predictions were observed in the LR and AP directions. Hypothesis 51 1) was rejected (p<0.0001). Treatment target motion range was under-predicted in 52 4DCT by factors of 1.7, 1.9 and 1.7 and in 4DCBCT by factors of 1.5, 1.6 and 1.6 in 53 the SI, LR, and AP directions, respectively. RMSEs were generally lower for end-54 exhale than inhale. 4DCBCT showed higher correlations with the imaging and 55 treatment target motion than 4DCT and testing hypothesis 2) a statistically significant 56 difference between 4DCT and 4DCBCT was shown in the SI direction (p=0.03). 57 Conclusion 58For lung SABR patients both 4DCT and 4DCBCT significantly under-predict 59 treatment target motion ranges. 60 the different 4DCT phases [7] or by determining the mid-ventilation position from 74 4DCT [8]. 75 Nevertheless, a 4DCT scan is only a snapshot in time and several publications have 76 shown that lung tumour motion can vary substantially inter-and intra-fractionally [9-77 11]. Guckenberger et al. [12] performed multiple 4DCT scans of lung cancer patients 78 in one session and observed increased intrafractional variations in patients with poor 79 pulmonary function and tumours located in the lower lobe. They suggested these 80 patients could benefit from more than one 4D planning CT. Purdie et al. [13] have 81 shown that the motion in the 4DCT scan may not be representative for all treatment 82 fractions and discrepancies of up to 10 mm can be observed using a 4D 83 reconstruction of the cone-beam CT (4DCBCT) pioneered by Sonke et al. [14] in 84 2004. 85 Today, the remaining uncertainties are usually compensated for by utilizing sufficient 86 population-based margins. Treatment setup is generally performed based on a...
For the first time, we developed and demonstrated an experimental system that is capable of adapting the MLC aperture to account for tumor deformation. This work provides a potentially widely available management method to effectively account for intrafractional tumor deformation. This proof-of-principle study is the first experimental step toward the development of an image-guided radiotherapy system to treat deforming tumors in real-time.
Structural stability and hydrogen adsorption capacity are two key quantities in evaluating the potential of metal-adatom decorated graphene for hydrogen storage and related devices. We have carried out extensive density functional theory calculations for the adsorption of hydrogen molecules on 12 different adatom (Ag, Au, Ca, Li, Mg, Pd, Pt, Sc, Sr, Ti, Y, and Zr) decorated graphene surfaces where the adatoms are found to be stabilized on double carbon vacancies, thus overcoming the "clustering problem" that occurs for adatoms on pristine graphene. Ca and Sr are predicted to bind the greatest number, namely six, of H 2 molecules. We find an interesting correlation between the hydrogen capacity and the change of charge distribution with increasing H 2 adsorption, where Ca, Li, Mg, Sc, Ti, Y, Sr, and Zr adatoms are partial electron donors and Ag, Au, Pd, and Pt are partial electron acceptors. The "18-electron rule" for predicting maximum hydrogen capacity is found not to be a reliable indicator for these systems.
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