Review of Real-Time 3-Dimensional Image Guided Radiation Therapy on Standard-Equipped Cancer Radiation Therapy Systems: Are We at the Tipping Point for the Era of Real-Time Radiation Therapy?

Keall, Paul J.; Nguyen, Doan Trang; O’Brien, Ricky; Zhang, Pengpeng; Happersett, Laura; Bertholet, Jenny; Poulsen, Per Rugaard

doi:10.1016/j.ijrobp.2018.04.016

Cited by 51 publications

(50 citation statements)

References 65 publications

(53 reference statements)

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“…Several real‐time image guidance techniques that have been implemented using kV and MV imaging tools on linear accelerators could be used for real‐time targeting . We have previously demonstrated the efficacy of one of these techniques (Kilovoltage Intrafraction Monitoring with MLC tracking) for IMRT treatments in fixed‐beam geometry .…”

Section: Discussionmentioning

confidence: 99%

Development and commissioning of a full‐size prototype fixed‐beam radiotherapy system with horizontal patient rotation

et al. 2019

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Purpose Compared to conventional linacs with rotating gantries, a fixed‐beam radiotherapy system could be smaller, more robust and more cost‐effective. In this work, we developed and commissioned a prototype x‐ray radiotherapy system utilizing a fixed vertical radiation beam and horizontal patient rotation. Methods The prototype system consists of an Elekta Synergy linac with gantry fixed at 0° and a custom‐built patient rotation system (PRS). The PRS was designed to immobilize patients and safely rotate them about the horizontal axis. The interlocks and emergency stops of the linac and PRS were connected. Custom software was developed to monitor the system status, control the motion of the PRS and modify treatment plans for the fixed‐beam configuration. Following installation, the prototype system was commissioned for three‐dimensional (3D) conformal therapy based on guidelines specified in AAPM TG‐45 and TG‐142, with modifications for the fixed‐beam geometry made where necessary. Results The system and control software was tested in a variety of machine states and executed motion, stop and beam gating commands as expected. Interlocks and emergency stops of the linac and PRS were found to correctly stop PRS motion and both kV and MV radiation beams when triggered. For 3D conformal treatments, the prototype system met all AAPM TG‐45 and TG‐142 specifications for geometric and dosimetric accuracy. Motion of the PRS was within 0.6 ± 0.3 mm and 0.10° ± 0.07° of input values for translation and rotation respectively. The axis of rotation of the PRS was coincident with the radiation beam axis to less than 1 mm. End‐to‐end treatment verification for 6 MV conformal treatments showed less than 2% difference between planned and delivered dose for all fields. Conclusion In this work, we have developed and commissioned a radiotherapy system that utilizes a fixed vertical radiation beam and horizontal patient rotation. This system is a proof‐of‐concept prototype for a fixed‐beam treatment system without a rotating gantry. Fixed‐beam systems that are smaller and more cost‐effective could help in improving global access to radiotherapy.

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Section: Discussionmentioning

confidence: 99%

Development and commissioning of a full‐size prototype fixed‐beam radiotherapy system with horizontal patient rotation

et al. 2019

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“…More elaborate real‐time analysis will be implemented as part of a future study. DoseTracker is directly applicable in the clinic as long as the tumor motion is known, e.g., via electromagnetic transponders, fluoroscopic images of implanted markers, cine MR on an MR linac or similar. DoseTracker was recently applied in our clinic for the world’s first clinical real‐time motion‐including tumor dose reconstruction performed during radiotherapy delivery .…”

Section: Discussionmentioning

confidence: 99%

Simulated real‐time dose reconstruction for moving tumors in stereotactic liver radiotherapy

Skouboe¹,

Poulsen²,

Muurholm³

et al. 2019

Medical Physics

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Purpose: In radiotherapy, tumor motion may deteriorate the planned dose distribution. However, the dosimetric consequences of the motion are normally unknown for individual treatments. We here present a method for real-time motion-including tumor dose reconstruction and demonstrate its use for simulated stereotactic body radiotherapy (SBRT) of patients with liver cancer previously treated with Calypso-guided gating. Methods: Real-time motion-including dose reconstruction was performed using in-house developed software, DoseTracker, on offline replays of previous clinical treatments. The patient cohort consisted of fifteen patients previously treated in our clinic with three-fraction SBRT to the liver using conformal or IMRT plans. The tumor motion at treatment was monitored with implanted electromagnetic transponders. The dose reconstruction was performed for both the actual gated treatments and simulated nongated treatments using a 21 Hz data stream containing accelerator parameters and the recorded motion. The dose was reconstructed in the same calculation points within the planning target volume (PTV) as used by the treatment planning system (TPS). The reconstructed doses were compared with calculations performed in the TPS, in which the motion was modeled as a series of isocenter shifts. The comparison included point doses as a function of treatment time and the dose volume histogram (DVH) for the clinical target volume (CTV). The motion-induced reduction in the dose to 95% of the CTV, DD 95% , and in the mean CTV dose, DD Mean , was compared between DoseTracker and the TPS for each simulated fraction. DoseTracker currently assumes water density within the patient contour, so for comparison, the TPS calculations were performed with both CT density and water density. The calculation times were additionally analyzed. Results: Dose reconstruction was carried out for ninety SBRT sessions with calculation volumes ranging from 9.9 to 366.4 cm 3 and median calculation times of 55-155 ms (equivalent to 18.2-6.5 Hz). Time-resolved trends of doses to a single calculation point in the patient were well replicated and dose differences between actual and planned calculations matched well. DD Mean had a range of À0.1%-30.7%-points and was estimated by DoseTracker with a root-mean-square deviation (RMSD) to the TPS calculations of 0.43%-points (water density) and 0.79%-points (CT density). Similarly, DD 95% had a range of 0.0%-35.2%-points and was estimated by Dose-Tracker with an RMSD of 0.80%-points (water density) and 1.33%-points (CT density). Dose-Tracker predicted losses in tumor dose coverage above 5%-points with high sensitivity (91.7%) and specificity (97.6%). Conclusions: Real-time dose reconstruction to moving tumors was demonstrated on offline replays of previous clinical treatments. DVHs of actually delivered dose are made available immediately after the end of treatment fractions. It shows promising results for liver SBRT with accurate estimation of

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“…In addition, recent advancements of real-time imaging techniques also call for such a technique, 26 which will make possible on-the-fly dose monitoring and intervention through real-time plan re-optimization and adaptation.…”

Section: Introductionmentioning

confidence: 99%

Boosting radiotherapy dose calculation accuracy with deep learning

Xing

Zhang

Nguyen

et al. 2020

J Applied Clin Med Phys

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In radiotherapy, a trade-off exists between computational workload/speed and dose calculation accuracy. Calculation methods like pencil-beam convolution can be much faster than Monte-Carlo methods, but less accurate. The dose difference, mostly caused by inhomogeneities and electronic disequilibrium, is highly correlated with the dose distribution and the underlying anatomical tissue density. We hypothesize that a conversion scheme can be established to boost low-accuracy doses to highaccuracy, using intensity information obtained from computed tomography (CT) images. A deep learning-driven framework was developed to test the hypothesis by converting between two commercially available dose calculation methods: Anisotropic analytic algorithm (AAA) and Acuros XB (AXB). A hierarchically dense U-Net model was developed to boost the accuracy of AAA dose toward the AXB level. The network contained multiple layers of varying feature sizes to learn their dose differences, in relationship to CT, both locally and globally. Anisotropic analytic algorithm and AXB doses were calculated in pairs for 120 lung radiotherapy plans covering various treatment techniques, beam energies, tumor locations, and dose levels. For each case, the CT and the AAA dose were used as the input and the AXB dose as the "ground-truth" output, to train and test the model. The mean squared errors (MSEs) and gamma passing rates (2 mm/2% & 1 mm/1%) were calculated between the boosted AAA doses and the "ground-truth" AXB doses. The boosted AAA doses demonstrated substantially improved match to the "ground-truth" AXB doses, with average (± s.d.) gamma passing rate (1 mm/1%) 97.6% (±2.4%) compared to 87.8% (±9.0%) of the original AAA doses. The corresponding average MSE was 0.11(±0.05) vs 0.31(±0.21). Deep learning is able to capture the differences between dose calculation algorithms to boost the low-accuracy algorithms. By combining a less accurate dose calculation algorithm with a trained deep learning model, dose calculation can potentially achieve both high accuracy and efficiency.

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Review of Real-Time 3-Dimensional Image Guided Radiation Therapy on Standard-Equipped Cancer Radiation Therapy Systems: Are We at the Tipping Point for the Era of Real-Time Radiation Therapy?

Cited by 51 publications

References 65 publications

Development and commissioning of a full‐size prototype fixed‐beam radiotherapy system with horizontal patient rotation

Development and commissioning of a full‐size prototype fixed‐beam radiotherapy system with horizontal patient rotation

Simulated real‐time dose reconstruction for moving tumors in stereotactic liver radiotherapy

Boosting radiotherapy dose calculation accuracy with deep learning

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