Applying pytorch toolkit to plan optimization for circular cone based robotic radiotherapy

Abstract

Background Robotic linac is ideally suited to deliver hypo-fractionated radiotherapy due to its compact head and flexible positioning. The non-coplanar treatment space improves the delivery versatility but the complexity also leads to prolonged optimization and treatment time. Methods In this study, we attempted to use the deep learning (pytorch) framework for the plan optimization of circular cone based robotic radiotherapy. The optimization problem was topologized into a simple feedforward neural network, thus the treatment plan optimization was transformed into network training. With this transformation, the pytorch toolkit with high-efficiency automatic differentiation (AD) for gradient calculation was used as the optimization solver. To improve the treatment efficiency, plans with fewer nodes and beams were sought. The least absolute shrinkage and selection operator (lasso) and the group lasso were employed to address the “sparsity” issue. Results The AD-S (AD sparse) approach was validated on 6 brain and 6 liver cancer cases and the results were compared with the commercial MultiPlan (MLP) system. It was found that the AD-S plans achieved rapid dose fall-off and satisfactory sparing of organs at risk (OARs). Treatment efficiency was improved by the reduction in the number of nodes (28%) and beams (18%), and monitor unit (MU, 24%), respectively. The computational time was shortened to 47.3 s on average. Conclusions In summary, this first attempt of applying deep learning framework to the robotic radiotherapy plan optimization is promising and has the potential to be used clinically.

Examining The Interplay of Technology Readiness and Behavioural Intentions in Health Detection Safe Entry Station

“…NVIDIA TAO Toolkit was developed using TensorFlow and PyTorch, software for building and training artificial intelligence models (Li et al, 2023). Using transfer learning technology, this Toolkit can simplify the model training process and optimize model performance to run on specific platforms (Liang et al, 2022). The result is a highly efficient workflow, making it easy to use existing models or create your models with original or custom data, then optimize their performance to run on specific platforms.…”

Deep Learning Aided Neuroimaging and Brain Regulation

“…Although there are challenges to overcome such as inter-scanner variability, the need for benchmark datasets, and prospective validations for clinical applicability, there is a significant opportunity for the development of optimal solutions for brain or central nervous system disease stratification. These solutions can provide immediate recommendations for further diagnostic decisions, the guidance of deep brain stimulation target identification and personalized treatment plan optimization [23][24][25]. In a word, using deep learning to assist medical imaging for brain theranostics has the characteristics of objectivity, highaccuracy, and high efficiency beyond the abilities of human judgement from qualitative to quantitative imaging.…”