Head Movement Detection from Radial k-Space Lines using Convolutional Neural Networks - A Digital Phantom Study

Maximilian Wattenberg, Jannis Hagenah, Constantin Schareck, Floris Ernst, Martin A. Koch

Abstract

Magnetic resonance imaging-guided linear particle accelerators use reconstructed images to adapt the radiation beam to the tumor location. Image-based approaches are relatively slow, causing healthy tissue to be irradiated upon subject movement. This study targets on the use of con- volutional neural networks to estimate rigid patient movements directly from few acquired radial k-space lines. Thus, abrupt patient movements were simulated in image data of a head. De- pending on the number of acquired spokes after movement, the network quantiﰂed this motion precisely. These ﰂrst results suggest that neural network-based navigators can help accelerating beam guidance in radiotherapy.
Original languageEnglish
Number of pages4
Publication statusPublished - 2019
EventAnnual Meeting of the International Society of Magnetic Resonance in Medicine 2019
- Palais des congrès de Montréal, Montréal, Canada
Duration: 11.05.201916.01.2021

Conference

ConferenceAnnual Meeting of the International Society of Magnetic Resonance in Medicine 2019
Abbreviated titleISMRM 2019
Country/TerritoryCanada
CityMontréal
Period11.05.1916.01.21

Research Areas and Centers

  • Academic Focus: Biomedical Engineering

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