Article

Advanced Deep Learning Methods for MLC Leaf Error Classification in Fluence Maps of Radiation Therapy

2026-01-29

Abstract excerpt

<title>Abstract</title> <p>Background Multi-leaf collimator (MLC) positioning errors are a major source of delivery uncertainty in modern radiation therapy techniques such as volumetric modulated arc therapy (VMAT) and intensity-modulated radiation therapy (IMRT). Traditional quality assurance (QA) methods, particularly gamma analysis, have limited sensitivity for detecting individual leaf errors and provide onl...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: f7b4c73d-6f55-57ac-b848-5d1e1723090e
DOI: 10.21203/rs.3.rs-8429164/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Advanced Deep Learning Methods for MLC Leaf Error Classification in Fluence Maps of Radiation TherapyDOI 10.21203/rs.3.rs-8429164/v1Read

Leaf-Specific Classification of Multi-Leaf Collimator Positioning Errors in Volumetric Modulated Arc Therapy Using a Convolutional Neural NetworkSemantic proximity Read

Deep Learning-Based Multileaf Collimator Error Classification and Quantification in Patient- Specific Intensity Modulated Radiation Therapy Quality AssuranceSemantic proximity Read

A deep learning-driven automated treatment planning framework for patient treated with radiotherapy in cervical cancerSemantic proximity Read

Patient-specific Quality Assurance Failure Prediction with Deep Tabular ModelsSemantic proximity Read

Clinical implementation and evaluation of deep learning-assisted automatic radiotherapy treatment planning for lung cancerSemantic proximity Read

Dose prediction of CyberKnife Monte Carlo plan for lung cancer patients based on deep learning: robust learning of variable beam configurationsSemantic proximity Read

RADIANT: A fully configurable radiotherapy dose prediction frameworkSemantic proximity Read

Dosimetric Impact of Deep Learning-Based CT Auto-Segmentation on IMRT Treatment Planning for Prostate CancerSemantic proximity Read

Deep Learning Method for Prediction of Patient-Specific Dose Distribution in Breast CancerSemantic proximity Read

Enhancing the Spatial Resolution of Dose Measurements Using a Super-Resolution Geometry-Informed Neural NetworkSemantic proximity Read

The Dosimetric Impact of Deep Learning-Based Auto-Segmentation of Organs at Risk on Nasopharyngeal and Rectal CancerSemantic proximity Read

Generalizable Deep Learning Framework for Radiotherapy Dose Prediction Across Cancer Sites, Prescriptions and Treatment ModalitiesSemantic proximity Read