0000000000480030
AUTHOR
M.c Martinez-bisbal
showing 13 related works from this author
Fusingin vivoandex vivoNMR sources of information for brain tumor classification
2011
In this study we classify short echo-time brain magnetic resonance spectroscopic imaging (MRSI) data by applying a model-based canonical correlation analyses algorithm and by using, as prior knowledge, multimodal sources of information coming from high-resolution magic angle spinning (HR-MAS), MRSI and magnetic resonance imaging. The potential and limitations of fusing in vivo and ex vivo nuclear magnetic resonance sources to detect brain tumors is investigated. We present various modalities for multimodal data fusion, study the effect and the impact of using multimodal information for classifying MRSI brain glial tumors data and analyze which parameters influence the classification results…
Deterioro cognitivo: clasificación mediante espectroscopia de resonancia magnética de protón y contribución de la imagen convencional
2006
Objetivo Analizar la eficacia diagnostica de la espectroscopia de resonancia magnetica de proton (1H ERM) en pacientes con deterioro cognitivo y establecer la complementariedad de la informacion de imagen de resonancia magnetica (RM) mediante curvas ROC. Material y metodos Se estudian 64 pacientes con deterioro cognitivo, incluyendo enfermedad de Alzheimer (EA) (N = 31), demencia vascular (N = 6), deterioro cognitivo leve (DCL) (N = 9) y depresion mayor (N = 18). Todos se exploraron con RM cerebral y 1H ERM usando dos tiempos de eco (TE, 31 y 136 ms) con volumen unico en la circonvolucion cingular posterior y lobulo temporal derecho. Los metabolitos analizados fueron N-acetilaspartato (NAA)…
MRS as Endogenous Molecular Imaging for Brain and Prostate Tumors: FP6 Project “eTUMOR“
2006
Molecular imaging has become during the last years in an important tool for supporting cancer diagnosis and prognosis. PET and SPECT are the most common molecular imaging techniques, although very promising and specific biological molecular agent contrast for CT and MRI are being recently developed. However, the above imaging techniques require exogenous contrast agents and usually a sole molecular image can be obtained at once. On the contrary, in vivo magnetic resonance spectroscopy (MRS), in particular 1H MRS can simultaneously provide several molecular images using endogenous metabolites. In addition to biochemical spatial information from molecular imaging spectroscopy, MRS can also pr…
Cognitive impairment: classification by 1H magnetic resonance spectroscopy.
2004
1H magnetic resonance spectroscopy (MRS) allows accurate and non-invasive in vivo metabolic study, and is a useful tool for the diagnosis of different forms of dementias. Cognitive impairment pathologies have been almost exclusively studied with MRS by comparison with healthy without a global comparison amongst Alzheimer disease (AD), vascular dementia, mild cognitive impairment (MCI) and major depression patients with cognitive impairment. Whereas decrease of N-acetylaspartate (NAA) and increase myo-Inositol (mI) at different brain locations by 1H MRS are common features of AD, Choline (Cho) alterations have been inconclusive. In our study, 64 patients with cognitive impairment were evalua…
Quadrature coils for magnetic resonance spectroscopy in the detection of prostate cancer: Single voxel acquisition does not improve the diagnostic ac…
2011
Abstract Objective To determine the viability of quadrature coils for detecting prostate cancer using single voxel and multivoxel spectroscopy images. Material and methods We used a quadrature coil on a 1.5T MR scanner to evaluate 23 patients with suspected prostate cancer and prostate specific antigen levels greater than 4 ng/ml (mean 12 ± 8 ng/ml), independently of findings at digital rectal examination. We acquired T2-weighted images and MR spectroscopy images. We also acquired single voxel studies in areas in which the T2-weighted images or the multivoxel images were altered. We used a citrate solution to verify the spectroscopic calibration. Results Using spectroscopy images and a (Cho…
Incorporating in vivo and ex vivo NMR sources of information for modeling robust brain tumor classifiers
2010
The purpose of this paper is to investigate the potential and limitations of using multimodal sources of information coming from in vivo NMR and ex vivo NMR data for detecting brain tumors. Supervised pattern recognition methods, whose performance directly depends on the prior available observations used in building them, are proposed. We show that high resolution magic angle spinning (HR-MAS) data act as complementary information for classifying magnetic resonance spectroscopic imaging (MRSI) data. In particularly, when considering rare brain tumors, since it is unlikely to acquire sufficient cases to define their metabolite profiles using only in vivo NMR information, HR-MAS can support t…
Spectroscopic metabolomic abnormalities in the thalamus related to auditory hallucinations in patients with schizophrenia
2008
Abstract Objective Previous studies have found neurochemical abnormalities in thalamic nuclei in patients with schizophrenia. These abnormalities have been associated with information processing deficiencies and symptom formation. There are no metabolic spectroscopy studies in patients with schizophrenia attending to auditory hallucinations. The aim of the present study is to explore metabolic Magnetic Resonance Spectroscopy (MRS) ratio differences in the thalamus between schizophrenic patients with and without auditory hallucinations and control subjects. Methods MRS studies (MRI 1.5 T unit) were performed in 49 patients with schizophrenia (30 with auditory hallucinations and 19 without au…
Combining HR-MAS and In Vivo MRI and MRSI Information for Robust Brain Tumor Recognition
2009
In this study we propose to classify short echotime brain MRSI data by using multimodal information coming from magnetic resonance imaging (MRI), magnetic resonance spectroscopic imaging (MRSI) and high resolution magic angle spinning (HR-MAS), and to develop an advanced pattern recognition method that could help clinicians in diagnosing brain tumors. We study the impact of using HR-MAS information in combination with in vivo information for classifying brain tumors and we investigate which parameters influence our classification results.
Ex vivo high resolution magic angle spinning metabolic profiles describe intratumoral histopathological tissue properties in adult human gliomas.
2010
In gliomas one can observe distinct histopathological tissue properties, such as viable tumor cells, necrotic tissue or regions where the tumor infiltrates normal brain. A first screening between the different intratumoral histopathological tissue properties would greatly assist in correctly diagnosing and prognosing gliomas. The potential of ex vivo high resolution magic angle spinning spectroscopy in characterizing these properties is analyzed and the biochemical differences between necrosis, high cellularity and border tumor regions in adult human gliomas are investigated. Statistical studies applied on sets of metabolite concentrations and metabolite ratios extracted from 52 high resolu…
Evidence of Wallerian degeneration in normal appearing white matter in the early stages of relapsing-remitting multiple sclerosis
2003
Objective: Wallerian degeneration in normal appearing white matter in early relapsing-remitting multiple sclerosis (RRMS), and its correlation with the number of relapses and disease duration. Background Recent pathological studies have demonstrated Wallerian degeneration in normal appearing white matter (NAWM) in multiple sclerosis (MS), in established RRMS, and in chronic MS. However, the presence of Wallerian degeneration early in the disease and its correlation with relapse and with disease duration has not been studied. Methods: We performed proton magnetic resonance spectroscopic imaging in 21 MS patients, and 4 healthy controls, age and gender matched, aged under 45 years, with a max…
Non-negative blind source separation techniques for tumor tissue typing using HR-MAS signals.
2010
Given High Resolution Magic Angle Spinning (HR-MAS) signals from several glioblastoma tumor subjects, the goal is to differentiate between tumor tissue types by separating the different sources that contribute to the profile of each spectrum. Blind source separation techniques are applied for obtaining characteristic profiles for necrosis, high cellular tumor and border tumor tissue, and providing the contribution (abundance) of each tumor tissue to the profile of the spectra. The problem is formulated as a non-negative source separation problem. We illustrate the effectiveness of the proposed methods and we analyze to which extent the dimension of the input space could influence the perfor…
Magnetic resonance myelography evaluation of the lumbar spine end plates and intervertebral disks.
2005
Purpose: To evaluate the value of magnetic resonance (MR) myelography in the evaluation of intervertebral disk and end‐plate degenerative changes in the lumbar spine.Material and Methods: Conventional MR and MR myelography examinations were performed in 150 consecutive patients (69 F and 81 M, mean age 45±15 years, range 18–89). Sagittal T1 and T2‐weighted TSE images were compared to MR myelography obtained with a multishot‐TSE‐T2‐weighted sequence (4000/250/fat suppression). Coronal, sagittal, and both oblique MR myelography projections were obtained. Image analysis was carried out independently by two radiologists who categorized lumbar disks into normal, degenerated, or edematous; and ve…
Quantification and classification of high-resolution magic angle spinning data for brain tumor diagnosis.
2007
The goal of this work is to propose a complete protocol (preprocessing, processing and classification) for classifying brain tumors with proton high-resolution magic-angle spinning ((1)H HR-MAS) data. The different steps of the procedure are detailed and discussed. Feature extraction techniques such as peak integration, including also the automated quantitation method AQSES, were combined with linear (LDA) and non-linear (least-squares support vector machine or LS-SVM) classifiers. Classification accuracy was assessed using a stratified random sampling scheme. The results suggest that LS-SVM performs better than LDA while AQSES performs better than the standard peak integration feature extr…