Towards an identification of tumor growth parameters from time series of images.
In cancer treatment, understanding the aggressiveness of the tumor is essential in therapy planning and patient follow-up. In this article, we present a novel method for quantifying the speed of invasion of gliomas in white and grey matter from time series of magnetic resonance (MR) images. The proposed approach is based on mathematical tumor growth models using the reaction-diffusion formalism. The quantification process is formulated by an inverse problem and solved using anisotropic fast marching method yielding an efficient algorithm. It is tested on a few images to get a first proof of concept with promising new results.
Asclepios Research Project, INRIA Sophia Antipolis, France. ender.konukoglu@sophia.inria.fr
Ender
E
Konukoglu
Olivier
O
Clatz
Pierre-Yves
PY
Bondiau
Maxime
M
Sermesant
Hervé
H
Delingette
Nicholas
N
Ayache
D000465:Algorithms; D001932:Brain Neoplasms / Q000473:pathology*; D005909:Glioblastoma / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009361:Neoplasm Invasiveness; D009367:Neoplasm Staging / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique