arXiv subjects

Esa Kuusela

Publications and source records attributed to Esa Kuusela.

3 recordsLinked to original sources

arXiv2026

Dynamic Modulated Arc Therapy (DMAT): A Time Aware, Modulation Steered Optimization Framework for Next Generation Radiotherapy Delivery

Background: Conventional VMAT optimization treats delivery time and deliverability as emergent properties of control-point-centric models that ignore finite acceleration and other dynamic limits. As linacs gain axis speed and dose rate, the plan quality-time trade-off must become explicit and steerable. Purpose: To introduce Dynamic Modulated Arc Therapy (DMAT), a time-aware, modulation-steered framework that jointly optimizes dosimetric quality, delivery time, and modulation complexity. Methods: DMAT couples direct machine emulation (axis synchronization, finite acceleration), dynamic modulation control, and clinical metrics used directly as cost functions. A user-selected modulation level (-3 to +3) governs leaf-travel allowance, total MU, aperture complexity, and control-point (CP) density. Plans are generated by progressive-resolution optimization alternating dosimetric with sequencing/deliverability updates, with non-uniform CP redistribution and complexity-reducing post-processing. DMAT was evaluated on head-and-neck, lung SBRT, and prostate SBRT cases using a hypothetical accelerated system (2.5 RPM gantry, 6.25 cm/s MLC, 3000 MU/min). Results: Increasing modulation level raised modulation surrogates (MU/Gy, aperture complexity) and delivery time, with additional CPs concentrated in arc sectors where finer angular resolution was most beneficial. The trade-off was site dependent: head-and-neck gained substantial plan quality, whereas prostate and lung SBRT gained little beyond baseline. Negative levels predictably shortened delivery time at a fixed CP budget, with quantifiable quality loss. Conclusions: DMAT co-optimizes plan quality and modulation complexity under machine-aware timing and explicit user control, making quality-time trade-offs transparent and navigable and supporting time-constrained workflows such as motion management and adaptive radiotherapy.

physics.med-ph↗

arXiv2025

A Beam's Eye View to Fluence Maps 3D Network for Ultra Fast VMAT Radiotherapy Planning

Volumetric Modulated Arc Therapy (VMAT) revolutionizes cancer treatment by precisely delivering radiation while sparing healthy tissues. Fluence maps generation, crucial in VMAT planning, traditionally involves complex and iterative, and thus time consuming processes. These fluence maps are subsequently leveraged for leaf-sequence. The deep-learning approach presented in this article aims to expedite this by directly predicting fluence maps from patient data. We developed a 3D network which we trained in a supervised way using a combination of L1 and L2 losses, and RT plans generated by Eclipse and from the REQUITE dataset, taking the RT dose map as input and the fluence maps computed from the corresponding RT plans as target. Our network predicts jointly the 180 fluence maps corresponding to the 180 control points (CP) of single arc VMAT plans. In order to help the network, we pre-process the input dose by computing the projections of the 3D dose map to the beam's eye view (BEV) of the 180 CPs, in the same coordinate system as the fluence maps. We generated over 2000 VMAT plans using Eclipse to scale up the dataset size. Additionally, we evaluated various network architectures and analyzed the impact of increasing the dataset size. We are measuring the performance in the 2D fluence maps domain using image metrics (PSNR, SSIM), as well as in the 3D dose domain using the dose-volume histogram (DVH) on a validation dataset. The network inference, which does not include the data loading and processing, is less than 20ms. Using our proposed 3D network architecture as well as increasing the dataset size using Eclipse improved the fluence map reconstruction performance by approximately 8 dB in PSNR compared to a U-Net architecture trained on the original REQUITE dataset. The resulting DVHs are very close to the one of the input target dose.

eess.IV↗

arXiv2024

Multi-Agent Reinforcement Learning Meets Leaf Sequencing in Radiotherapy

In contemporary radiotherapy planning (RTP), a key module leaf sequencing is predominantly addressed by optimization-based approaches. In this paper, we propose a novel deep reinforcement learning (DRL) model termed as Reinforced Leaf Sequencer (RLS) in a multi-agent framework for leaf sequencing. The RLS model offers improvements to time-consuming iterative optimization steps via large-scale training and can control movement patterns through the design of reward mechanisms. We have conducted experiments on four datasets with four metrics and compared our model with a leading optimization sequencer. Our findings reveal that the proposed RLS model can achieve reduced fluence reconstruction errors, and potential faster convergence when integrated in an optimization planner. Additionally, RLS has shown promising results in a full artificial intelligence RTP pipeline. We hope this pioneer multi-agent RL leaf sequencer can foster future research on machine learning for RTP.

cs.LG↗