Voxel-based statistical analysis of thalamic glucose metabolism in traumatic brain injury: relationship with consciousness and cognition.

OBJECTIVE: To study the relationship between thalamic glucose metabolism and neurological outcome after severe traumatic brain injury (TBI). METHODS: Forty-nine patients with severe and closed TBI and 10 healthy control subjects with (18)F-FDG PET were studied. Patients were divided into three groups: MCS&VS group (n = 17), patients in a vegetative or a minimally conscious state; In-PTA group (n = 12), patients in a state of post-traumatic amnesia (PTA); and Out-PTA group (n = 20), patients who had emerged from PTA. SPM5 software implemented in MATLAB 7 was used to determine the quantitative differences between patients and controls. FDG-PET images were spatially normalized and an automated thalamic ROI mask was generated. Group differences were analysed with two sample voxel-wise t-tests. RESULTS: Thalamic hypometabolism was the most prominent in patients with low consciousness (MCS&VS group) and the thalamic hypometabolism in the In-PTA group was more prominent than that in the Out-PTA group. Healthy control subjects showed the greatest thalamic metabolism. These differences in metabolism were more pronounced in the internal regions of the thalamus. CONCLUSIONS: The results confirm the vulnerability of the thalamus to suffer the effect of the dynamic forces generated during a TBI. Patients with thalamic hypometabolism could represent a sub-set of subjects that are highly vulnerable to neurological disability after TBI.

Using the grid to analyze the pharmacokinetic modelling after contrast administration in dynamic MRI.

The analysis of the angiogenesis in hepatic lesions is an important marker of tumour aggressiveness and response to therapy. However, the quantitative analysis of this fact requires a deep knowledge of the hepatic perfusion. The development of pharmacokinetic models constitutes a very valuable tool, but it is computationally intensive. Moreover, abdominal imaging processing increases the computational requirements since the movement of the patient makes images in a time series incomparable, requiring a previous pre-processing. This work presents a Grid environment developed to deal with the computational demand of pharmacokinetic modelling. This article proposes and implements a four-level software architecture that provides a simple interface to the user and deals transparently with the complexity of Grid environment. The four layers implemented are: Grid Layer (the closest to the Grid infrastructure), the Gate-to- Grid (which transforms the user requests to Grid operations), the Web Services layer (which provides a simple, standard and ubiquitous interface to the user) and the Application Layer. An application has been developed on top of this architecture to manage the execution of multi-parametric groups of co-registration actions on a large set of medical images. The execution has been performed on the EGEE Grid infrastructure. The application is platform-independent and can be used from any computer without special requirements.