Showing papers by "Montserrat Robles published in 2011"

A Voxel-Based Analysis of FDG-PET in Traumatic Brain Injury: Regional Metabolism and Relationship between the Thalamus and Cortical Areas

Abstract: The objective was to study the correlations and the differences in glucose metabolism between the thalamus and cortical structures in a sample of severe traumatic brain injury (TBI) patients with different neurological outcomes. We studied 49 patients who had suffered a severe TBI and 10 healthy control subjects using 18F-fluorodeoxyglucose positron emission tomography (18F-FDG-PET). The patients were divided into three groups: a vegetative or minimally-conscious state (MCSV an In-post-traumatic amnesia (In-PTA) group (n=12), which included patients in PTA; and an Out-PTA group (n=20), which included patients who had recovered from PTA. SPM5 software was used to determine the metabolic differences between the groups. FDG-PET images were normalized and four regions of interest were generated around the thalamus, precuneus, and the frontal and temporal lobes. The groups were parameterized using Student...

Adaptive Multiresolution Non-Local Means Filter for 3D MR Image Denoising

Abstract: In this paper, an adaptive multiresolution version of the Blockwise Non-Local (NL-) means filter is presented for 3D Magnetic Resonance (MR) images. Based on an adaptive soft wavelet coefficient mixing, the proposed filter implicitly adapts the amount of denoising according to the spatial and frequency information contained in the image. Two versions of the filter are described for Gaussian and Rician noise. Quantitative validation was carried out on Brainweb datasets by using several quality metrics. The results show that the proposed multiresolution filter obtained competitive performance compared to recently proposed Rician NL-means filters. Finally, qualitative experiments on anatomical and Diffusion-Weighted MR images show that the proposed filter efficiently removes noise while preserving fine structures in classical and very noisy cases. The impact of the proposed denoising method on fiber tracking is also presented on a HARDI dataset.

Compatibility between 3T 1H SV-MRS data and automatic brain tumour diagnosis support systems based on databases of 1.5T 1H SV-MRS spectra.

Abstract: This study demonstrates that 3T SV-MRS data can be used with the currently available automatic brain tumour diagnostic classifiers which were trained on databases of 1.5T spectra. This will allow the existing large databases of 1.5T MRS data to be used for diagnostic classification of 3T spectra, and perhaps also the combination of 1.5T and 3T databases. Brain tumour classifiers trained with 154 1.5T spectra to discriminate among high grade malignant tumours and common grade II glial tumours were evaluated with a subsequently-acquired set of 155 1.5T and 37 3T spectra. A similarity study between spectra and main brain tumour metabolite ratios for both field strengths (1.5T and 3T) was also performed. Our results showed that classifiers trained with 1.5T samples had similar accuracy for both test datasets (0.87 ± 0.03 for 1.5T and 0.88 ± 0.03 for 3.0T). Moreover, non-significant differences were observed with most metabolite ratios and spectral patterns. These results encourage the use of existing classifiers based on 1.5T datasets for diagnosis with 3T 1H SV-MRS. The large 1.5T databases compiled throughout many years and the prediction models based on 1.5T acquisitions can therefore continue to be used with data from the new 3T instruments.

An archetype-based solution for the interoperability of computerised guidelines and electronic health records

Abstract: Clinical guidelines contain recommendations based on the best empirical evidence available at the moment. There is a wide consensus about the benefits of guidelines and about the fact that they should be deployed through clinical information systems, making them available during consultation time. However, one of the main obstacles to this integration is still the interaction with the electronic health record. In this paper we present an archetype-based approach to solve the interoperability problems of guideline systems, as well as to enable guideline sharing. We also describe the knowledge requirements for the development of archetype-enabled guideline systems, and then focus on the development of appropriate guideline archetypes and on the connection of these archetypes to the target electronic health record.

A generic and extensible automatic classification framework applied to brain tumour diagnosis in HealthAgents

Abstract: New biomedical technologies enable the diagnosis of brain tumours by using non-invasive methods. HealthAgents is a European Union-funded research project that aims to build an agent-based distributed decision support system (dDSS) for the diagnosis of brain tumours. This is achieved using the latest biomedical knowledge, information and communication technologies and pattern recognition (PR) techniques. As part of the PR development of HealthAgents, an independent and automatic classification framework (CF) has been developed. This framework has been integrated with the HealthAgents dDSS using the HealthAgents agent platform. The system offers (1) the functionality to search for distributed classifiers to solve specific questions; (2) automatic classification of new cases; (3) instant deployment of new validated classifiers; and (4) the ability to rank a set of classifiers according to their performance and suitability for the case in hand. The CF enables both the deployment of new classifiers using the provided Extensible Markup Language1 classifier specification, and the inclusion of new PR techniques that make the system extensible. These features may enable the rapid integration of PR laboratory results into industrial or research applications, such as the HealthAgents dDSS. Two classification nodes have been deployed and they currently offer classification services by means of dedicated servers connected to the HealthAgents agent platform: one node being located at the Katholieke Universiteit Leuven, Belgium and the other at the Universidad Politecnica de Valencia, Spain. These classification nodes share the current set of brain tumour classifiers that have been trained from in vivo magnetic resonance spectroscopy data. The combination of the CF with a distributed agent system constitutes the basis of the brain tumour dDSS developed in HealthAgents.

MRI denoising based on Sparseness and Self-Similarity

Abstract: In this paper, we present two new approaches for MRI denoising. The first is an extension of the original method proposed by Guleryuz (2007). Based on local 3D DCT hard thresholding, our proposed method has been adapted to deal with Rician noise (typical of magnitude MR images) using a pseudo-oracle principle. The second proposed method is a new rotationally invariant 3D version of the Rician-adapted Non Local Means filter (Buades, 2005, Coupe 2008a, Wiest-Daessle 2008) that uses a prefiltered image.