Many-isocenter optimization for robotic radiotherapy

Quantification of Dosimetry Improvement With or Without Patient Surface Guidance

Purpose

Noncoplanar beams and arcs are routinely used to improve dosimetry for intracranial cases, but their application for extracranial cases has been hampered by the risk of collision. This has led to conservative beam selection whose impact on plan dosimetry has not been previously studied.

Methods and Materials

A full-body 3-dimensional patient surface was acquired using optical cameras for a single lung patient at the time of computed tomography simulation. Eight stereotactic body radiation therapy (SBRT) plans were created for the patient, with varying degrees of noncoplanarity and deliverability. The plans included volumetric modulated arc therapy and intensity modulated radiation therapy (IMRT) plans ranging from simple, coplanar arcs to multiple noncoplanar arcs and IMRT beams. A total of 70 fields were created across the 8 plans, of which 21 fields were undeliverable with a 5-cm buffer. Organs-at-risk (OARs) metrics including R50, Dmax 2 cm from the PTV, lung V20, and chest wall V30 were evaluated. Five expert SBRT dosimetrists from 5 institutions evaluated field deliverability, with or without the guidance of the clearance map.

Results

In the dosimetry evaluation, a clear trend in increasing dosimetric compactness and OAR sparing is observed with increasing plan noncoplanarity. R50, Dmax 2 cm, lung V20, and chest wall V30 decreased 41%, 39%, 43%, and 57%, respectively, from plan 1 (2 coplanar partial arcs) to plan 8 (19 noncoplanar IMRT beams). In the observer tests, the expert dosimetrists' ability to accurately discern beam deliverability because of collision significantly increases with the clearance map. The errors in predicting colliding fields were eliminated using the whole-body surface and clearance map, and the user was able to select fields based on plan quality and patient comfort instead of being overly conservative.

Conclusion

The study shows that incorporating a personalized, whole-body clearance map in the treatment planning workflow can facilitate the adoption of noncoplanar beams or arcs that benefit the SBRT plan dosimetry.

Smirnov A, Verma A, Woods K. Compact X-Band Electron Linac for Radiotherapy and Security Applications

RadiaBeam has developed a 6 MeV accelerator that is compact and light enough to be placed on a robotic arm or light truck. The main drivers of size and weight in conventional accelerators are the power source and the shielding. Small dimensions are enabled by operation at 9.3 GHz frequency (X-band), which allows reducing the size and weight of all accelerator components. Thanks to the robust design of the accelerating structure, the accelerator can be used as a source for novel cargo inspection and radiotherapy techniques. In this paper, we present the linac design and its components, as well the results of the experimental demonstration of beam acceleration.

DeepMC: a deep learning method for efficient Monte Carlo beamlet dose calculation by predictive denoising in magnetic resonance-guided radiotherapy

Emerging magnetic resonance (MR) guided radiotherapy affords significantly improved anatomy visualization and, subsequently, more effective personalized treatment. The new therapy paradigm imposes significant demands on radiation dose calculation quality and speed, creating an unmet need for the acceleration of Monte Carlo (MC) dose calculation. Existing deep learning approaches to denoise the final plan MC dose fail to achieve the accuracy and speed requirements of large-scale beamlet dose calculation in the presence of a strong magnetic field for online adaptive radiotherapy planning. Our deep learning dose calculation method, DeepMC, addresses these needs by predicting low-noise dose from extremely noisy (but fast) MC-simulated dose and anatomical inputs, thus enabling significant acceleration. DeepMC simultaneously reduces MC sampling noise and predicts corrupted dose buildup at tissue-air material interfaces resulting from MR-field induced electron return effects. Here we demonstrate our model's ability to accelerate dose calculation for daily treatment planning by a factor of 38 over traditional low-noise MC simulation with clinically meaningful accuracy in deliverable dose and treatment delivery parameters. As a post-processing approach, DeepMC provides compounded acceleration of large-scale dose calculation when used alongside established MC acceleration techniques in variance reduction and graphics processing unit-based MC simulation.