AI Medical Compendium Topic:
Radiotherapy Planning, Computer-Assisted

Clear Filters Showing 381 to 390 of 701 articles

Application of deep learning to auto-delineation of target volumes and organs at risk in radiotherapy.

Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
The technological advancement heralded the arrival of precision radiotherapy (RT), thereby increasing the therapeutic ratio and decreasing the side effects from treatment. Contour of target volumes (TV) and organs at risk (OARs) in RT is a complicate...

Deep learning model for automatic contouring of cardiovascular substructures on radiotherapy planning CT images: Dosimetric validation and reader study based clinical acceptability testing.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: Large radiotherapy (RT) planning imaging datasets with consistently contoured cardiovascular structures are essential for robust cardiac radiotoxicity research in thoracic cancers. This study aims to develop and validate a hig...

Few-shot learning for deformable image registration in 4DCT images.

The British journal of radiology
OBJECTIVES: To develop a rapid and accurate 4D deformable image registration (DIR) approach for online adaptive radiotherapy.

Evaluation of deep learning-based autosegmentation in breast cancer radiotherapy.

Radiation oncology (London, England)
PURPOSE: To study the performance of a proposed deep learning-based autocontouring system in delineating organs at risk (OARs) in breast radiotherapy with a group of experts.

Deep learning-based classification and structure name standardization for organ at risk and target delineations in prostate cancer radiotherapy.

Journal of applied clinical medical physics
Radiotherapy (RT) datasets can suffer from variations in annotation of organ at risk (OAR) and target structures. Annotation standards exist, but their description for prostate targets is limited. This restricts the use of such data for supervised ma...

Deep learning-augmented radioluminescence imaging for radiotherapy dose verification.

Medical physics
PURPOSE: We developed a novel dose verification method using a camera-based radioluminescence imaging system (CRIS) combined with a deep learning-based signal processing technique.

Outcome-based multiobjective optimization of lymphoma radiation therapy plans.

The British journal of radiology
At its core, radiation therapy (RT) requires balancing therapeutic effects against risk of adverse events in cancer survivors. The radiation oncologist weighs numerous disease and patient-level factors when considering the expected risk-benefit ratio...

Automatic radiotherapy delineation quality assurance on prostate MRI with deep learning in a multicentre clinical trial.

Physics in medicine and biology
Volume delineation quality assurance (QA) is particularly important in clinical trial settings where consistent protocol implementation is required, as outcomes will affect future as well current patients. Currently, where feasible, this is conducted...

Deep learning-enabled EPID-based 3D dosimetry for dose verification of step-and-shoot radiotherapy.

Medical physics
PURPOSE: The study aims at a novel dosimetry methodology to reconstruct a 3D dose distribution as imparted to a virtual cylindrical phantom using an electronic portal imaging device (EPID).