Use of Diffusion Tensor Images in Glioma Growth Modeling for Radiotherapy Target Delineation

Abstract : In radiotherapy of gliomas, a precise de nition of the treat- ment volume is problematic, because current imaging modalities reveal only the central part of the tumor with a high cellular density, but fail to detect all regions of microscopic tumor cell spread in the adjacent brain parenchyma. Mathematical models can be used to integrate known growth characteristics of gliomas into the target delineation process. In this paper, we demonstrate the use of di usion tensor imaging (DTI) for simulating anisotropic cell migration in a glioma growth model that is based on the Fisher-Kolmogorov equation. For a clinical application of the model, it is crucial to develop a detailed understanding of its behavior, capabilities, and limitations. For that purpose, we perform a retrospective analysis of glioblastoma patients treated at our institution. We analyze the impact of di usion anisotropy on model-derived target volumes, and interpret the results in the context of the underlying im- ages. It was found that, depending on the location of the tumor relative to major ber tracts, DTI can have signi cant in uence on the shape of the radiotherapy target volume.
Type de document :
Communication dans un congrès
Multimodal Brain Image Analysis, Sep 2013, Nagoya, Japan. 8159, pp.63-73, 2013, Lecture Notes in Computer Scienc. 〈10.1007/978-3-319-02126-3_7〉
Liste complète des métadonnées

Littérature citée [11 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-00912667
Contributeur : Bjoern Menze <>
Soumis le : lundi 2 décembre 2013 - 14:49:04
Dernière modification le : jeudi 11 janvier 2018 - 16:20:54
Document(s) archivé(s) le : lundi 3 mars 2014 - 09:45:25

Fichier

MBIA-Florian-13.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

Citation

Florian Dittmann, Bjoern Menze, Ender Konukoglu, Jan Unkelbach. Use of Diffusion Tensor Images in Glioma Growth Modeling for Radiotherapy Target Delineation. Multimodal Brain Image Analysis, Sep 2013, Nagoya, Japan. 8159, pp.63-73, 2013, Lecture Notes in Computer Scienc. 〈10.1007/978-3-319-02126-3_7〉. 〈hal-00912667〉

Partager

Métriques

Consultations de la notice

340

Téléchargements de fichiers

279