Related papers: Long short-term memory networks for proton dose ca…
The recurrent neural network with the long short-term memory cell (LSTM-NN) is employed to simulate the long-time dynamics of open quantum system. The bootstrap method is applied in the LSTM-NN construction and prediction, which provides a…
Purpose: To evaluate reliability of upright CT for proton dose calculation and feasibility of a simplified phantom configuration for accelerated routine QA. Methods: A calibration phantom was scanned on an upright CT following consensus…
Fast and accurate dose predictions are one of the bottlenecks in treatment planning for microbeam radiation therapy (MRT). In this paper, we propose a machine learning (ML) model based on a 3D U-Net. Our approach predicts separately the…
Purpose: The purpose of this work was to provide a flexible platform for FLASH research with protons by adapting a former clinical pencil beam scanning gantry to irradiations with ultrahigh dose rates. Methods: PSI Gantry 1 treated patients…
Dose calculations in proton therapy require the fast and accurate solution of a high-dimensional transport equation for a large number of (pencil) beams with different energies and directions. Deterministically solving this transport…
In order to fully exploit the ballistic potential of particle therapy, we propose an online range monitoring concept based on high-resolution Time-Of-Flight (TOF)-resolved Prompt Gamma (PG) detection in a single proton counting regime. In a…
Purpose: The importance of robust proton treatment planning to mitigate the impact of uncertainty is well understood. However, its computational cost grows with the number of uncertainty scenarios, prolonging the treatment planning process.…
When treating moving tumors, the precise delivery of proton therapy by pencil beam scanning (PBS) is challenged by the interplay effect. Although several 4D-optimization methods have been proposed, what is the most beneficial motion…
External beam X-ray therapy (XRT) and proton therapy (PT) are effective and widely accepted forms of treatment for many types of cancer. However, the procedures require extensive computerized planning. Current planning systems for both XRT…
Purpose: Multiple Coulomb scattering (MCS) poses a challenge in proton CT (pCT) image reconstruction. The assumption of straight paths is replaced with Bayesian models of the most likely path (MLP). Current MLP-based pCT reconstruction…
Conventional radiotherapy dose calculation algorithms are often computationally slow and non-differentiable, creating bottlenecks for online adaptive radiotherapy (ART) and limiting end-to-end automatic planning. Deep learning provides…
Autonomous magnetic catheter systems are emerging as a promising approach for the future of minimally invasive interventions. This study presents a novel approach that begins by modeling the nonlinear and hysteretic dynamics of a…
Due to the inter- and intra- variation of respiratory motion, it is highly desired to provide real-time volumetric images during the treatment delivery of lung stereotactic body radiation therapy (SBRT) for accurate and active motion…
In recent advancements in proton therapy, MR-based treatment planning is gaining momentum to minimize additional radiation exposure compared to traditional CT-based methods. This transition highlights the critical need for accurate MR-to-CT…
In order to drive safely and efficiently on public roads, autonomous vehicles will have to understand the intentions of surrounding vehicles, and adapt their own behavior accordingly. If experienced human drivers are generally good at…
Online adaptive radiation therapy (ART) is an attractive concept that promises the ability to deliver an optimal treatment in response to the inter-fraction variability in patient anatomy. However, it has yet to be realized due to technical…
We introduce a data-driven forecasting method for high-dimensional chaotic systems using long short-term memory (LSTM) recurrent neural networks. The proposed LSTM neural networks perform inference of high-dimensional dynamical systems in…
Purpose: Ultra-fast scintillation imaging has been shown to provide a unique tool for spatio-temporal dosimetry of conventional cyclotron and synchrocyclotron pencil beam scanning (PBS) deliveries, indicating the potential use for…
This study explores the application potential of a deep learning model based on the CNN-LSTM framework in forecasting the sales volume of cancer drugs, with a focus on modeling complex time series data. As advancements in medical technology…
This study presents the applicability of conventional deep recurrent neural networks (RNN) to predict path-dependent plasticity associated with material heterogeneity and anisotropy. Although the architecture of RNN possesses inductive…