No abstract

No abstract

We present a method for the detection and quantification of white matter infiltration from human brain tumours based on Diffusion Tensor Imaging (DTI). Since white matter destruction alters the local diffusion properties, DTI has the potential to sensitively detect tumour infiltration and to quantify the degree thereof. Here, we consider three tumour patients with gliomas, two with and one without contralateral tumour progress. We use DTI to identify specific fibre systems, where infiltration has to be assessed. On this basis, the problem of arbitrary region of interest definition is solved such that tumour infiltration can be reliably quantified in particular fibre bundles. It is demonstrated at the Corpus Callosum (CC) and the Pyramidal Tract (PT) that fibre bundle infiltration can be well detected by specific visualisation techniques of diffusion tensor data. Infiltration of the CC is quantified by using a reliable method for the determination of diffusion properties inside particular fibre bundles. For an age normalised quantification of white matter infiltration we introduce the Integrity Index, which measures the diffusion anisotropy inside an infiltrated fibre bundle normalised by the diffusion anisotropy in a specific region of healthy fibre tissue. It turns out that the quantification of CC infiltration correlates with contralateral tumour progression and has the potential to serve as a surrogate marker for this process, which is crucial for surgical therapy decisions and intervention planning.

Abstract.A quantitative analysis of brain tumors is an important factor that can have direct impact on a patient's prognosis and treatment. In order to achieve clinical relevance, reproducibility and especially accuracy of a proposed method have to be tested. We propose a framework for the generation of realistic digital phantoms of brain tumors of known volumes and their incorporation into an MR dataset of a healthy volunteer. Deformations that occur due to tumor growth inside the brain are simulated by means of a biomechanical model. Furthermore, a model for the amount of edema at each voxel is included as well as a simulation of contrast enhancement, which provides us with an additional characterization of the tumor. A "ground truth" is generally not available for brain tumors. Our proposed framework provides a flexible tool to generate representative datasets with known ground truth, which is essential for the validation and comparison of current and new quantitative approaches. Experiments are carried out using a semi-automated volumetry approach for a set of generated tumor datasets.

No abstract