AIMC Topic: Radiotherapy Planning, Computer-Assisted
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Advanced prediction of multi-leaf collimator leaf position using artificial neural network.
Medical physics
Feb 18, 2025
BACKGROUND: Multi-leaf collimators (MLCs) are crucial for modern radiotherapy as they ensure precise target irradiation through accurate leaf positioning. Accurate prediction of MLC leaf positions is vital for the effectiveness and safety of treatmen...
Enhancing automated right-sided early-stage breast cancer treatments via deep learning model adaptation without additional training.
Medical physics
Feb 18, 2025
BACKGROUND: Input data curation and model training are essential, but time-consuming steps in building a deep-learning (DL) auto-planning model, ensuring high-quality data and optimized performance. Ideally, one would prefer a DL model that exhibits ...
Multi-task learning for automated contouring and dose prediction in radiotherapy.
Physics in medicine and biology
Feb 18, 2025
. Deep learning (DL)-based automated contouring and treatment planning has been proven to improve the efficiency and accuracy of radiotherapy. However, conventional radiotherapy treatment planning process has the automated contouring and treatment pl...
Evaluation and failure analysis of four commercial deep learning-based autosegmentation software for abdominal organs at risk.
Journal of applied clinical medical physics
Feb 13, 2025
PURPOSE: Deep learning-based segmentation of organs-at-risk (OAR) is emerging to become mainstream in clinical practice because of the superior performance over atlas and model-based autocontouring methods. While several commercial deep learning-base...
Real-time adaptive proton therapy: An AI-based spatio-temporal mono-energetic dose calculation model (CC-LSTM).
Computers in biology and medicine
Feb 12, 2025
PURPOSE: To develop a fully AI-based dose estimation model capable of learning and estimating single pencil beam dose distributions, and to verify its performance by testing the model's generalizability on unseen, previously delivered treatment plans...
Deep learning-based quick MLC sequencing for MRI-guided online adaptive radiotherapy: a feasibility study for pancreatic cancer patients.
Physics in medicine and biology
Feb 12, 2025
One bottleneck of magnetic resonance imaging (MRI)-guided online adaptive radiotherapy is the time-consuming daily online replanning process. The current leaf sequencing method takes up to 10 min, with potential dosimetric degradation and small segme...
A deep learning-based peer review method for radiotherapy planning.
Medical physics
Feb 11, 2025
BACKGROUND: Quality control (QC) in radiotherapy planning is crucial for ensuring treatment efficacy and patient safety. Traditionally, QC relies on standard indicators and subjective assessments, which may lead to inconsistencies.
Novel pre-spatial data fusion deep learning approach for multimodal volumetric outcome prediction models in radiotherapy.
Medical physics
Feb 10, 2025
BACKGROUND: Given the recent increased emphasis on multimodal neural networks to solve complex modeling tasks, the problem of outcome prediction for a course of treatment can be framed as fundamentally multimodal in nature. A patient's response to tr...
Assessing multiple MRI sequences in deep learning-based synthetic CT generation for MR-only radiation therapy of head and neck cancers.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Feb 8, 2025
PURPOSE: This study investigated the effect of multiple magnetic resonance (MR) sequences on the quality of deep-learning-based synthetic computed tomography (sCT) generation in the head and neck region.
The implementation of knowledge-based planning with partial OAR contours for prostate radiotherapy.
Journal of applied clinical medical physics
Feb 7, 2025
PURPOSE: Intra- and inter-observer contour uncertainty is a continuous challenge in treatment planning for radiotherapy. Our proposed solution to address this challenge is the use of partial contours for treatment planning, focusing on uninvolved or ...
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