AIMC Topic: Radiotherapy Dosage
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Performance assessment of a new optimization system for robotic SBRT MLC-based plans.
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Feb 20, 2020
PURPOSE: To assess the performance of a new optimization system, VOLO, for CyberKnife MLC-based SBRT plans in comparison with the existing Sequential optimizer.
Many-isocenter optimization for robotic radiotherapy.
Physics in medicine and biology
Feb 10, 2020
Despite significant dosimetric gains, clinical implementation of the 4π non-coplanar radiotherapy on the widely available C-arm gantry system is hindered by limited clearance, and the need to perform complex coordinated gantry and couch motion. A rob...
Deep DoseNet: a deep neural network for accurate dosimetric transformation between different spatial resolutions and/or different dose calculation algorithms for precision radiation therapy.
Physics in medicine and biology
Feb 4, 2020
The purpose of this work is to introduce a novel deep learning strategy to obtain highly accurate dose plan by transforming from a dose distribution calculated using a low-cost algorithm (or algorithmic settings). 25 168 slices of dose distribution a...
User-controlled pipelines for feature integration and head and neck radiation therapy outcome predictions.
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Feb 2, 2020
PURPOSE: Precision cancer medicine is dependent on accurate prediction of disease and treatment outcome, requiring integration of clinical, imaging and interventional knowledge. User controlled pipelines are capable of feature integration with varied...
Integration of the M6 Cyberknife in the Moderato Monte Carlo platform and prediction of beam parameters using machine learning.
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Jan 30, 2020
PURPOSE: This work describes the integration of the M6 Cyberknife in the Moderato Monte Carlo platform, and introduces a machine learning method to accelerate the modelling of a linac.
Knowledge-Based Tradeoff Hyperplanes for Head and Neck Treatment Planning.
International journal of radiation oncology, biology, physics
Jan 24, 2020
PURPOSE: To develop a tradeoff hyperplane model to facilitate tradeoff decision-making before inverse planning.
Incorporating human and learned domain knowledge into training deep neural networks: A differentiable dose-volume histogram and adversarial inspired framework for generating Pareto optimal dose distributions in radiation therapy.
Medical physics
Dec 29, 2019
PURPOSE: We propose a novel domain-specific loss, which is a differentiable loss function based on the dose-volume histogram (DVH), and combine it with an adversarial loss for the training of deep neural networks. In this study, we trained a neural n...
Technical Note: A feasibility study on deep learning-based radiotherapy dose calculation.
Medical physics
Dec 25, 2019
PURPOSE: Various dose calculation algorithms are available for radiation therapy for cancer patients. However, these algorithms are faced with the tradeoff between efficiency and accuracy. The fast algorithms are generally less accurate, while the ac...
A hybrid automated treatment planning solution for esophageal cancer.
Radiation oncology (London, England)
Dec 19, 2019
OBJECTIVE: This study aims to investigate a hybrid automated treatment planning (HAP) solution that combines knowledge-based planning (KBP) and script-based planning for esophageal cancer.
Evaluation of complexity and deliverability of prostate cancer treatment plans designed with a knowledge-based VMAT planning technique.
Journal of applied clinical medical physics
Dec 9, 2019
PURPOSE: Knowledge-based planning (KBP) techniques have been reported to improve plan quality, efficiency, and consistency in radiation therapy. However, plan complexity and deliverability have not been addressed previously for treatment plans guided...
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