Detecting and quantifying spatial misalignment between longitudinal kilovoltage computed tomography (kVCT) scans of the head and neck by using convolutional neural networks (CNNs).

Date publication

février 2023

Journal

Technology and health care : official journal of the European Society for Engineering and Medicine

Auteurs

Membres identifiés du Cancéropôle Est :
Pr ANTONI Delphine , Dr NOBLET Vincent

Tous les auteurs :
Lallement A, Noblet V, Antoni D, Meyer P

Lien Pubmed

https://www.ncbi.nlm.nih.gov/pubmed/36776082

Résumé

Adaptive radiotherapy (ART) aims to address anatomical modifications appearing during the treatment of patients by modifying the planning treatment according to the daily positioning image. Clinical implementation of ART relies on the quality of the deformable image registration (DIR) algorithms included in the ART workflow. To translate ART into clinical practice, automatic DIR assessment is needed.

Mots clés

DIR in radiotherapy, DIR validation, Registration error, adaptive radiotherapy, convolutional neural networks, deep learning, supervised learning

Référence

Technol Health Care. 2023 02 2;: