Marina Donativi

Automatic segmentation and therapy follow-up of cerebral glioma in diffusion-tensor images

Gliomas are the most common primary brain tumors, with a typical infiltrative growth pattern along white matter (WM) fibers. Diffusion Tensor Imaging (DTI) is sensitive to the directional diffusion of water along WM tracts, which allows the identification of subtle peritumoral glioma infiltration that are not apparent on conventional Magnetic Resonance imaging. The aim of this study was to characterize pathological and healthy tissue in DTI datasets by statistical texture analysis, developing a Computer Assisted Detection (CAD) technique for cerebral glioma. This system, coupled to voxel-based tumor evolution analysis, could allow objective tumor identification and qualitative and quantitative measurements in the follow-up of patients during chemotherapy. In this paper, preliminary results of tumor segmentation and evolution analysis are shown.

Automatic segmentation of glioblastoma for radiation therapy treatment planning

During the radiation therapy (RT) process, the treatment is planned and simulated with a treatment planning system (TPS): the organs at risk (OAR) and the tumor target are identified and contoured, and the RT dose, delivered by the planned photon beams, is obtained for optimization of the resulting plan. The contouring work-up of tumor target identifies the Planning Treatment Volume (PTV), i.e. the physical RT treatment volume. PTV of glioblastomas (GB) includes, after expansion, Gross Tumor Volume (GTV, the tumor) and Clinical Target Volume (CTV, tumor plus edema). Usually, GTV contouring is performed manually. In this work, we used GlioCAD, a Computer-Assisted Detection software for automatic contouring of gliomas in MRI/DTI, to delineate GTV. The dataset included the images of 21 patients undergoing RT for GB. For each patient, we co-registered CT-planning images and diagnostic MRI (16 T1-gad, 6 T2 Flair, 13 Flair Fat Sat), which were used for GlioCAD training and validation. CAD outlined the tumor with good accuracy, after ruling out some false positives in post-processing. We identified GTVs, suitable for RT requirements. An evolution of GlioCAD will take into account edema for outlining CTV. The method seems promising. A further automatic system for the delineation of sites at risk in the brain is under development, which may be helpful for standardization of RT-treatment planning.

MRDTI: a semi-automated algorithm to identify damaged brain areas from fractional anisotropy maps

Aim of this study was to analyse diffusion tensor imaging (DTI) datasets in order to identify damaged areas or disorders of the brain in a semi-automatic way. For this purpose, a software tool has been developed: it takes in input the fractional anisotropy (FA) map of a (damaged) brain and, after several steps involving the comparison between the two brain hemispheres, it gives back, as output, a binary mask with a ROI (Region of Interest) that shows the probably damaged area. In the same way, starting from the MR image without diffusion weighting (b0), we find another ROI that we compare with the one previously detected from the FA map. Then we overlay these ROIs onto both the FA map and the image without diffusion weighting, trying to quantify how well the ROIs cover the pathological tissue. This procedure was repeated on a few patients (healthy and pathological ones). The algorithm worked well, showing as a preliminary result that FA maps allow a neater detection of the pathological tissue if compared to MR images without diffusion weighting.