Differentiation between brain metastasis and glioblastoma using MRI and two-dimensional turbo spectroscopic imaging data

Abstract

In this paper we propose a novel technique to differentiate brain metastases from high-grade gliomas, which represent the most aggressive and common brain lesions. In spite of the significant progresses achieved in the field of MRI in the last decades, the differentiation between these two types of tumors is still a challenge as they show a similar appearance on MRI images, but require a completely different therapeutic treatment. Here, we show that such a differentiation is actually possible and can be obtained by making use of MRI as well as of two-dimensional Turbo Spectroscopic Imaging (2D-TSI) information. Specifically, the proposed technique consists of three steps: we first detect the abnormal region on the MRI by applying a new segmentation-classification algorithm able to produce nosologic images of the brain by exploiting both MRI and 2D-TSI information; afterwards, an accurate and efficient tissue typing technique, based on a statistical method known as canonical correlation analysis, is applied to 2D-TSI data in order to detect the lesion and characterize its nature; finally, the lesion contours provided by the aforementioned methods are compared in order to differentiate glioblastoma from metastasis lesions. The latter step exploits the infiltrative nature of glioblastoma, when compared to metastasis, which can be observed on magnetic resonance spectroscopic imaging (MRSI) but not on MRI. Indeed, our studies show that the tumor region detected in the second step for glioblastoma is significantly broader than the lesion obtained in the first step and observable on the MRI image. On the contrary, such a difference does not occur in the metastasis examples. © 2009 Springer Berlin Heidelberg.