AIMC Topic: Radiotherapy Dosage
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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.
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 ...
Synthetic CT generation from CBCT and MRI using StarGAN in the Pelvic Region.
Radiation oncology (London, England)
Feb 4, 2025
RATIONALE AND OBJECTIVES: This study evaluated StarGAN, a deep learning model designed to generate synthetic computed tomography (sCT) images from magnetic resonance imaging (MRI) and cone-beam computed tomography (CBCT) data using a single model. Th...
Automating the optimization of proton PBS treatment planning for head and neck cancers using policy gradient-based deep reinforcement learning.
Medical physics
Jan 31, 2025
BACKGROUND: Proton pencil beam scanning (PBS) treatment planning for head and neck (H&N) cancers is a time-consuming and experience-demanding task where a large number of potentially conflicting planning objectives are involved. Deep reinforcement le...
Deep learning-based Monte Carlo dose prediction for heavy-ion online adaptive radiotherapy and fast quality assurance: A feasibility study.
Medical physics
Jan 27, 2025
BACKGROUND: Online adaptive radiotherapy (OART) and rapid quality assurance (QA) are essential for effective heavy ion therapy (HIT). However, there is a shortage of deep learning (DL) models and workflows for predicting Monte Carlo (MC) doses in suc...
Brachytherapy Seed Placement by Robotic Bronchoscopy with Cone Beam Computed Tomography Guidance for Peripheral Lung Cancer: A Human Cadaveric Feasibility Pilot.
International journal of radiation oncology, biology, physics
Jan 25, 2025
PURPOSE: This study evaluates the feasibility of using robotic-assisted bronchoscopy with cone beam computed tomography (RB-CBCT) platform to perform low-dose-rate brachytherapy (LDR-BT) implants in a mechanically ventilated human cadaveric model. Po...
Breast radiation therapy fluence painting with multi-agent deep reinforcement learning.
Medical physics
Jan 23, 2025
BACKGROUND: The electronic compensation (ECOMP) technique for breast radiation therapy provides excellent dose conformity and homogeneity. However, the manual fluence painting process presents a challenge for efficient clinical operation.
Deep learning-based synthetic CT for dosimetric monitoring of combined conventional radiotherapy and lattice boost in large lung tumors.
Radiation oncology (London, England)
Jan 22, 2025
PURPOSE: Conventional radiotherapy (CRT) has limited local control and poses a high risk of severe toxicity in large lung tumors. This study aimed to develop an integrated treatment plan that combines CRT with lattice boost radiotherapy (LRT) and mon...
Evaluation of AI-based auto-contouring tools in radiotherapy: A single-institution study.
Journal of applied clinical medical physics
Jan 21, 2025
BACKGROUND: Accurate delineation of organs at risk (OARs) is crucial yet time-consuming in the radiotherapy treatment planning workflow. Modern artificial intelligence (AI) technologies had made automation of OAR contouring feasible. This report deta...
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