Dosimetric comparison and evaluation of two computational algorithms in VMAT treatment plans.

PURPOSE: This study aimed to assess the accuracy and dosimetric impact of the Acuros XB (AXB) algorithm compared to the Anisotropic Analytical Algorithm (AAA) in two situations. First, simple phantom geometries were set and analyzed; moreover, volumetric modulated arc therapy (VMAT) clinical plans for Head & Neck and lung cases were calculated and compared. METHODS: First, a phantom study was performed to compare the algorithms with radiochromic EBT3 film doses using one PMMA slab phantom and two others containing foam or air gap. Subsequently, a clinical study was conducted, including 20 Head & Neck and 15 lung cases irradiated with the VMAT technique. The treatment plans calculated by AXB and AAA were evaluated in terms of planning target volume (PTV) coverage (V95% ), dose received by relevant organs at risk (OARs), and the impact of using AXB with a grid size of 1 mm. Finally, patient-specific quality assurance (PSQA) was performed and compared for 17 treatment plans. RESULTS: Phantom dose calculations showed a better agreement of AXB with the film measurements. In the clinical study, AXB plans exhibited lower Conformity Index and PTV V95% , higher maximum PTV dose, and lower mean and minimum PTV doses for all anatomical sites. The most notable differences were detected in regions of intense heterogeneity. AXB predicted lower doses for the OARs, while the calculation time with a grid size of 1 mm was remarkably higher. Regarding PSQA, although AAA was found to exhibit slightly higher gamma passing rates, the difference did not affect the AXB treatment plan quality. CONCLUSIONS: AXB demonstrated higher accuracy than AAA in dose calculations of both phantom and clinical conditions, specifically in interface regions, making it suitable for sites with large heterogeneities. Hence, such dosimetric differences between the two algorithms should always be considered in clinical practice.

Magnetic control of tokamak plasmas through deep reinforcement learning.

Nuclear fusion using magnetic confinement, in particular in the tokamak configuration, is a promising path towards sustainable energy. A core challenge is to shape and maintain a high-temperature plasma within the tokamak vessel. This requires high-dimensional, high-frequency, closed-loop control using magnetic actuator coils, further complicated by the diverse requirements across a wide range of plasma configurations. In this work, we introduce a previously undescribed architecture for tokamak magnetic controller design that autonomously learns to command the full set of control coils. This architecture meets control objectives specified at a high level, at the same time satisfying physical and operational constraints. This approach has unprecedented flexibility and generality in problem specification and yields a notable reduction in design effort to produce new plasma configurations. We successfully produce and control a diverse set of plasma configurations on the Tokamak à Configuration Variable1,2, including elongated, conventional shapes, as well as advanced configurations, such as negative triangularity and 'snowflake' configurations. Our approach achieves accurate tracking of the location, current and shape for these configurations. We also demonstrate sustained 'droplets' on TCV, in which two separate plasmas are maintained simultaneously within the vessel. This represents a notable advance for tokamak feedback control, showing the potential of reinforcement learning to accelerate research in the fusion domain, and is one of the most challenging real-world systems to which reinforcement learning has been applied.