Related papers: Demo: Generative AI helps Radiotherapy Planning wi…
Purpose: Radiation therapy treatment planning is a trial-and-error, often time-consuming process. An optimal dose distribution based on a specific anatomy can be predicted by pre-trained deep learning (DL) models. However, dose…
Radiotherapy treatment planning currently requires many trail-and-error iterations between the planner and treatment planning system, as well as between the planner and physician for discussion/consultation. The physician's preferences for…
This study investigates the applicability of 3D dose predictions from a model trained on one modality to a cross-modality automated planning workflow. Additionally, we explore the impact of integrating a multi-criteria optimizer on adapting…
In this work, we propose a Machine Learning model that generates an adjustable 3D dose distribution for external beam radiation therapy for head-and-neck cancer treatments. In contrast to existing Machine Learning methods that provide a…
Purpose: This study aimed to use deep learning-based dose prediction to assess head and neck (HN) plan quality and identify suboptimal plans. Methods: A total of 245 VMAT HN plans were created using RapidPlan knowledge-based planning (KBP).…
We demonstrate the application of mixture density networks (MDNs) in the context of automated radiation therapy treatment planning. It is shown that an MDN can produce good predictions of dose distributions as well as reflect uncertain…
The use of neural networks to directly predict three-dimensional dose distributions for automatic planning is becoming popular. However, the existing methods only use patient anatomy as input and assume consistent beam configuration for all…
Purpose: To develop a machine learning-based, 3D dose prediction methodology for Gamma Knife (GK) radiosurgery. The methodology accounts for cases involving targets of any number, size, and shape. Methods: Data from 322 GK treatment plans…
We develop a knowledge-based automated planning (KBAP) pipeline that generates treatment plans using deep neural network architectures for predicting 3D doses. Our pipeline consisted of a generative adversarial network (GAN) to predict dose…
Radiotherapy planning is a critical aspect of cancer treatment, where the optimal selection of beam directions and dose distributions significantly impacts treatment efficacy and patient outcomes. Traditionally, this process involves…
Objective: Radiotherapy treatment planning is a time-consuming and potentially subjective process that requires the iterative adjustment of model parameters to balance multiple conflicting objectives. Recent advancements in frontier…
In the precision medicine era, there is a growing need for precision radiotherapy where the planned radiation dose needs to be optimally determined by considering a myriad of patient-specific information in order to ensure treatment…
Generative AI models differ from traditional machine learning tools in that they allow users to provide as much or as little information as they choose in their inputs. This flexibility often leads users to omit certain details, relying on…
Deep learning (DL) 3D dose prediction has recently gained a lot of attention. However, the variability of plan quality in the training dataset, generated manually by planners with wide range of expertise, can dramatically effect the quality…
Automatic Radiology Report Generation (RRG) is an important topic for alleviating the substantial workload of radiologists. Existing RRG approaches rely on supervised regression based on different architectures or additional knowledge…
Purpose: Proton therapy provides superior dose conformity compared to photon therapy, but its treatment planning is challenged by sensitivity to anatomical changes, setup/range uncertainties, and computational complexity. This review…
We study a fundamental problem in structure-based drug design -- generating molecules that bind to specific protein binding sites. While we have witnessed the great success of deep generative models in drug design, the existing methods are…
As a lay user creates an art piece using an interactive generative art tool, what, if anything, do the choices they make tell us about them and their preferences? These preferences could be in the specific generative art form (e.g., color…
In this study we performed a feasibility investigation on implementing a fast and accurate dose calculation based on a deep learning technique. A two dimensional (2D) fluence map was first converted into a three dimensional (3D) volume…
Radiotherapy (RT) is a critical cancer treatment, with volumetric modulated arc therapy (VMAT) being a commonly used technique that enhances dose conformity by dynamically adjusting multileaf collimator (MLC) positions and monitor units…