Classification of various sources of error in range assessment using proton radiography and neural networks in head and neck cancer patients

This study evaluates the suitability of convolutional neural networks (CNNs) to automatically process proton radiography (PR)-based images. CNNs are used to classify PR images impaired by several sources of error affecting the proton range, more precisely setup and calibration curve errors. PR simul...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	Physics in medicine & biology 2020-11, Vol.65 (23), p.235009
Hauptverfasser:	Oria, Carmen Seller, Marmitt, Gabriel Guterres, Both, Stefan, Langendijk, Johannes A, Knopf, Antje C, Meijers, Arturs
Format:	Artikel
Sprache:	eng
Schlagworte:	Head and Neck Neoplasms - diagnostic imaging Humans neural networks Neural Networks, Computer proton radiography Protons Radiography range probing range uncertainties Retrospective Studies
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!