Automated detection and classification of brain injury lesions on structural magnetic resonance imaging

El daño cerebral adquirido (DCA) es un problema social y sanitario grave, de magnitud creciente y de una gran complejidad diagnóstica y terapéutica. Su elevada incidencia, junto con el aumento de la supervivencia de los pacientes, una vez superada la fase aguda, lo convierten también en un problema de alta prevalencia. En concreto, según la Organización Mundial de la Salud (OMS) el DCA estará entre las 10 causas más comunes de discapacidad en el año 2020. La neurorrehabilitación permite mejorar el déficit tanto cognitivo como funcional y aumentar la autonomía de las personas con DCA. Con la incorporación de nuevas soluciones tecnológicas al proceso de neurorrehabilitación se pretende alcanzar un nuevo paradigma donde se puedan diseñar tratamientos que sean intensivos, personalizados, monitorizados y basados en la evidencia. Ya que son estas cuatro características las que aseguran que los tratamientos son eficaces. A diferencia de la mayor parte de las disciplinas médicas, no existen asociaciones de síntomas y signos de la alteración cognitiva que faciliten la orientación terapéutica. Actualmente, los tratamientos de neurorrehabilitación se diseñan en base a los resultados obtenidos en una batería de evaluación neuropsicológica que evalúa el nivel de afectación de cada una de las funciones cognitivas (memoria, atención, funciones ejecutivas, etc.). La línea de investigación en la que se enmarca este trabajo de investigación pretende diseñar y desarrollar un perfil cognitivo basado no sólo en el resultado obtenido en esa batería de test, sino también en información teórica que engloba tanto estructuras anatómicas como relaciones funcionales e información anatómica obtenida de los estudios de imagen. De esta forma, el perfil cognitivo utilizado para diseñar los tratamientos integra información personalizada y basada en la evidencia. Las técnicas de neuroimagen representan una herramienta fundamental en la identificación de lesiones para la generación de estos perfiles cognitivos. La aproximación clásica utilizada en la identificación de lesiones consiste en delinear manualmente regiones anatómicas cerebrales. Esta aproximación presenta diversos problemas relacionados con inconsistencias de criterio entre distintos clínicos, reproducibilidad y tiempo. Por tanto, la automatización de este procedimiento es fundamental para asegurar una extracción objetiva de información. La delineación automática de regiones anatómicas se realiza mediante el registro tanto contra atlas como contra otros estudios de imagen de distintos sujetos. Sin embargo, los cambios patológicos asociados al DCA están siempre asociados a anormalidades de intensidad y/o cambios en la localización de las estructuras. Este hecho provoca que los algoritmos de registro tradicionales basados en intensidad no funcionen correctamente y requieran la intervención del clínico para seleccionar ciertos puntos (que en esta tesis hemos denominado puntos singulares). Además estos algoritmos tampoco permiten que se produzcan deformaciones grandes deslocalizadas. Hecho que también puede ocurrir ante la presencia de lesiones provocadas por un accidente cerebrovascular (ACV) o un traumatismo craneoencefálico (TCE). Esta tesis se centra en el diseño, desarrollo e implementación de una metodología para la detección automática de estructuras lesionadas que integra algoritmos cuyo objetivo principal es generar resultados que puedan ser reproducibles y objetivos. Esta metodología se divide en cuatro etapas: pre-procesado, identificación de puntos singulares, registro y detección de lesiones. Los trabajos y resultados alcanzados en esta tesis son los siguientes: Pre-procesado. En esta primera etapa el objetivo es homogeneizar todos los datos de entrada con el objetivo de poder extraer conclusiones válidas de los resultados obtenidos. Esta etapa, por tanto, tiene un gran impacto en los resultados finales. Se compone de tres operaciones: eliminación del cráneo, normalización en intensidad y normalización espacial. Identificación de puntos singulares. El objetivo de esta etapa es automatizar la identificación de puntos anatómicos (puntos singulares). Esta etapa equivale a la identificación manual de puntos anatómicos por parte del clínico, permitiendo: identificar un mayor número de puntos lo que se traduce en mayor información; eliminar el factor asociado a la variabilidad inter-sujeto, por tanto, los resultados son reproducibles y objetivos; y elimina el tiempo invertido en el marcado manual de puntos. Este trabajo de investigación propone un algoritmo de identificación de puntos singulares (descriptor) basado en una solución multi-detector y que contiene información multi-paramétrica: espacial y asociada a la intensidad. Este algoritmo ha sido contrastado con otros algoritmos similares encontrados en el estado del arte. Registro. En esta etapa se pretenden poner en concordancia espacial dos estudios de imagen de sujetos/pacientes distintos. El algoritmo propuesto en este trabajo de investigación está basado en descriptores y su principal objetivo es el cálculo de un campo vectorial que permita introducir deformaciones deslocalizadas en la imagen (en distintas regiones de la imagen) y tan grandes como indique el vector de deformación asociado. El algoritmo propuesto ha sido comparado con otros algoritmos de registro utilizados en aplicaciones de neuroimagen que se utilizan con estudios de sujetos control. Los resultados obtenidos son prometedores y representan un nuevo contexto para la identificación automática de estructuras. Identificación de lesiones. En esta última etapa se identifican aquellas estructuras cuyas características asociadas a la localización espacial y al área o volumen han sido modificadas con respecto a una situación de normalidad. Para ello se realiza un estudio estadístico del atlas que se vaya a utilizar y se establecen los parámetros estadísticos de normalidad asociados a la localización y al área. En función de las estructuras delineadas en el atlas, se podrán identificar más o menos estructuras anatómicas, siendo nuestra metodología independiente del atlas seleccionado. En general, esta tesis doctoral corrobora las hipótesis de investigación postuladas relativas a la identificación automática de lesiones utilizando estudios de imagen médica estructural, concretamente estudios de resonancia magnética. Basándose en estos cimientos, se han abrir nuevos campos de investigación que contribuyan a la mejora en la detección de lesiones. ABSTRACT Brain injury constitutes a serious social and health problem of increasing magnitude and of great diagnostic and therapeutic complexity. Its high incidence and survival rate, after the initial critical phases, makes it a prevalent problem that needs to be addressed. In particular, according to the World Health Organization (WHO), brain injury will be among the 10 most common causes of disability by 2020. Neurorehabilitation improves both cognitive and functional deficits and increases the autonomy of brain injury patients. The incorporation of new technologies to the neurorehabilitation tries to reach a new paradigm focused on designing intensive, personalized, monitored and evidence-based treatments. Since these four characteristics ensure the effectivity of treatments. Contrary to most medical disciplines, it is not possible to link symptoms and cognitive disorder syndromes, to assist the therapist. Currently, neurorehabilitation treatments are planned considering the results obtained from a neuropsychological assessment battery, which evaluates the functional impairment of each cognitive function (memory, attention, executive functions, etc.). The research line, on which this PhD falls under, aims to design and develop a cognitive profile based not only on the results obtained in the assessment battery, but also on theoretical information that includes both anatomical structures and functional relationships and anatomical information obtained from medical imaging studies, such as magnetic resonance. Therefore, the cognitive profile used to design these treatments integrates information personalized and evidence-based. Neuroimaging techniques represent an essential tool to identify lesions and generate this type of cognitive dysfunctional profiles. Manual delineation of brain anatomical regions is the classical approach to identify brain anatomical regions. Manual approaches present several problems related to inconsistencies across different clinicians, time and repeatability. Automated delineation is done by registering brains to one another or to a template. However, when imaging studies contain lesions, there are several intensity abnormalities and location alterations that reduce the performance of most of the registration algorithms based on intensity parameters. Thus, specialists may have to manually interact with imaging studies to select landmarks (called singular points in this PhD) or identify regions of interest. These two solutions have the same inconvenient than manual approaches, mentioned before. Moreover, these registration algorithms do not allow large and distributed deformations. This type of deformations may also appear when a stroke or a traumatic brain injury (TBI) occur. This PhD is focused on the design, development and implementation of a new methodology to automatically identify lesions in anatomical structures. This methodology integrates algorithms whose main objective is to generate objective and reproducible results. It is divided into four stages: pre-processing, singular points identification, registration and lesion detection. Pre-processing stage. In this first stage, the aim is to standardize all input data in order to be able to draw valid conclusions from the results. Therefore, this stage has a direct impact on the final results. It consists of three steps: skull-stripping, spatial and intensity normalization. Singular points identification. This stage aims to automatize the identification of anatomical points (singular points). It involves the manual identification of anatomical points by the clinician. This automatic identification allows to identify a greater number of points which results in more information; to remove the factor associated to inter-subject variability and thus, the results are reproducible and objective; and to eliminate the time spent on manual marking. This PhD proposed an algorithm to automatically identify singular points (descriptor) based on a multi-detector approach. This algorithm contains multi-parametric (spatial and intensity) information. This algorithm has been compared with other similar algorithms found on the state of the art. Registration. The goal of this stage is to put in spatial correspondence two imaging studies of different subjects/patients. The algorithm proposed in this PhD is based on descriptors. Its main objective is to compute a vector field to introduce distributed deformations (changes in different imaging regions), as large as the deformation vector indicates. The proposed algorithm has been compared with other registration algorithms used on different neuroimaging applications which are used with control subjects. The obtained results are promising and they represent a new context for the automatic identification of anatomical structures. Lesion identification. This final stage aims to identify those anatomical structures whose characteristics associated to spatial location and area or volume has been modified with respect to a normal state. A statistical study of the atlas to be used is performed to establish which are the statistical parameters associated to the normal state. The anatomical structures that may be identified depend on the selected anatomical structures identified on the atlas. The proposed methodology is independent from the selected atlas. Overall, this PhD corroborates the investigated research hypotheses regarding the automatic identification of lesions based on structural medical imaging studies (resonance magnetic studies). Based on these foundations, new research fields to improve the automatic identification of lesions in brain injury can be proposed.

Examination of the predictive value of structural magnetic resonance scans in bipolar disorder:a pattern classification approach

Background - Bipolar disorder (BD) is one of the leading causes of disability worldwide. Patients are further disadvantaged by delays in accurate diagnosis ranging between 5 and 10 years. We applied Gaussian process classifiers (GPCs) to structural magnetic resonance imaging (sMRI) data to evaluate the feasibility of using pattern recognition techniques for the diagnostic classification of patients with BD. Method - GPCs were applied to gray (GM) and white matter (WM) sMRI data derived from two independent samples of patients with BD (cohort 1: n = 26; cohort 2: n = 14). Within each cohort patients were matched on age, sex and IQ to an equal number of healthy controls. Results - The diagnostic accuracy of the GPC for GM was 73% in cohort 1 and 72% in cohort 2; the sensitivity and specificity of the GM classification were respectively 69% and 77% in cohort 1 and 64% and 99% in cohort 2. The diagnostic accuracy of the GPC for WM was 69% in cohort 1 and 78% in cohort 2; the sensitivity and specificity of the WM classification were both 69% in cohort 1 and 71% and 86% respectively in cohort 2. In both samples, GM and WM clusters discriminating between patients and controls were localized within cortical and subcortical structures implicated in BD. Conclusions - Our results demonstrate the predictive value of neuroanatomical data in discriminating patients with BD from healthy individuals. The overlap between discriminative networks and regions implicated in the pathophysiology of BD supports the biological plausibility of the classifiers.

Association between biomechanical structural stresses of atherosclerotic carotid plaques and subsequent ischaemic cerebrovascular events - A longitudinal in vivo magnetic resonance imaging-based finite element study

Background: High-resolution magnetic resonance (MR) imaging has been used for MR imaging-based structural stress analysis of atherosclerotic plaques. The biomechanical stress profile of stable plaques has been observed to differ from that of unstable plaques; however, the role that structural stresses play in determining plaque vulnerability remains speculative. Methods: A total of 61 patients with previous history of symptomatic carotid artery disease underwent carotid plaque MR imaging. Plaque components of the index artery such as fibrous tissue, lipid content and plaque haemorrhage (PH) were delineated and used for finite element analysis-based maximum structural stress (M-C Stress) quantification. These patients were followed up for 2 years. The clinical end point was occurrence of an ischaemic cerebrovascular event. The association of the time to the clinical end point with plaque morphology and M-C Stress was analysed. Results: During a median follow-up duration of 514 days, 20% of patients (n=12) experienced an ischaemic event in the territory of the index carotid artery. Cox regression analysis indicated that M-C Stress (hazard ratio (HR): 12.98 (95% confidence interval (CI): 1.32-26.67, pZ0.02), fibrous cap (FC) disruption (HR: 7.39 (95% CI: 1.61e33.82), p Z 0.009) and PH (HR: 5.85 (95% CI: 1.27e26.77), p Z 0.02) are associated with the development of subsequent cerebrovascular events. Plaques associated with future events had higher M-C Stress than those which had remained asymptomatic (median (interquartile range, IQR): 330 kPa (229e494) vs. 254 kPa (166-290), p Z0.04). Conclusions: High biomechanical structural stresses, in addition to FC rupture and PH, are associated with subsequent cerebrovascular events.

Structural analysis and magnetic resonance imaging predict plaque vulnerability: A study comparing symptomatic and asymptomatic individuals

Background: More than half of all cerebral ischemic events are the result of rupture of extracranial plaques. The clinical determination of carotid plaque vulnerability is currently based solely on luminal stenosis; however, it has been increasingly suggested that plaque morphology and biomechanical stress should also be considered. We used finite element analysis based on in vivo magnetic resonance imaging (MRI) to simulate the stress distributions within plaques of asymptomatic and symptomatic individuals. Methods: Thirty nonconsecutive subjects (15 symptomatic and 15 asymptomatic) underwent high-resolution multisequence in vivo MRI of the carotid bifurcation. Stress analysis was performed based on the geometry derived from in vivo MRI of the carotid artery at the point of maximal stenosis. The finite element analysis model considered plaque components to be hyperelastic. The peak stresses within the plaques of symptomatic and asymptomatic individuals were compared. Results: High stress concentrations were found at the shoulder regions of symptomatic plaques, and the maximal stresses predicted in this group were significantly higher than those in the asymptomatic group (508.2 ± 193.1 vs 269.6 ± 107.9 kPa; P = .004). Conclusions: Maximal predicted plaque stresses in symptomatic patients were higher than those predicted in asymptomatic patients by finite element analysis, suggesting the possibility that plaques with higher stresses may be more prone to be symptomatic and rupture. If further validated by large-scale longitudinal studies, biomechanical stress analysis based on high resolution in vivo MRI could potentially act as a useful tool for risk assessment of carotid atheroma. It may help in the identification of patients with asymptomatic carotid atheroma at greatest risk of developing symptoms or mild-to-moderate symptomatic stenoses, which currently fall outside current clinical guidelines for intervention.

Association between cam-type deformities and magnetic resonance imaging-detected structural hip damage: a cross-sectional study in young men

Objective Femoroacetabular impingement may be a risk factor for hip osteoarthritis in men. An underlying hip deformity of the cam type is common in asymptomatic men with nondysplastic hips. This study was undertaken to examine whether hip deformities of the cam type are associated with signs of hip abnormality, including labral lesions and articular cartilage damage, detectable on magnetic resonance imaging (MRI). Methods In this cross-sectional, population-based study in asymptomatic young men, 1,080 subjects underwent clinical examination and completed a self-report questionnaire. Of these subjects, 244 asymptomatic men with a mean age of 19.9 years underwent MRI. All MRIs were read for cam-type deformities, labral lesions, cartilage thickness, and impingement pits. The relationship between cam-type deformities and signs of joint damage were examined using logistic regression models adjusted for age and body mass index. Odds ratios (ORs) and 95% confidence intervals (95% CIs) were determined. Results Sixty-seven definite cam-type deformities were detected. These deformities were associated with labral lesions (adjusted OR 2.77 [95% CI 1.31, 5.87]), impingement pits (adjusted OR 2.9 [95% CI 1.43, 5.93]), and labral deformities (adjusted OR 2.45 [95% CI 1.06, 5.66]). The adjusted mean difference in combined anterosuperior femoral and acetabular cartilage thickness was −0.19 mm (95% CI −0.41, 0.02) lower in those with cam-type deformities compared to those without. Conclusion Our findings indicate that the presence of a cam-type deformity is associated with MRI-detected hip damage in asymptomatic young men.

Quantifying the volume fraction and texture of cancellous bone using 3.0 Tesla magnetic resonance imaging

Introduction: 3.0 Tesla MRI offers the potential to quantify the volume fraction and structural texture of cancellous bone, along with quantification of marrow composition, in a single non-invasive examination. This study describes our preliminary investigations to identify parameters which describe cancellous bone structure including the relationships between texture and volume fraction.

Children and adolescents in full-time special education : an epidemiological and magnetic resonance imaging study

The need for special education (SE) is increasing. The majority of those whose problems are due to neurodevelopmental disorders have no specific aetiology. The aim of this study was to evaluate the contribution of prenatal and perinatal factors and factors associated with growth and development to later need for full-time SE and to assess joint structural and volumetric brain alterations among subjects with unexplained, familial need for SE. A random sample of 900 subjects in full-time SE allocated into three levels of neurodevelopmental problems and 301 controls in mainstream education (ME) provided data on socioeconomic factors, pregnancy, delivery, growth, and development. Of those, 119 subjects belonging to a sibling-pair in full-time SE with unexplained aetiology and 43 controls in ME underwent brain magnetic resonance imaging (MRI). Analyses of structural brain alterations and midsagittal area and diameter measurements were made. Voxel-based morphometry (VBM) analysis provided detailed information on regional grey matter, white matter, and cerebrospinal fluid (CSF) volume differences. Father’s age ≥ 40 years, low birth weight, male sex, and lower socio-economic status all increased the probability of SE placement. At age 1 year, one standard deviation score decrease in height raised the probability of SE placement by 40% and in head circumference by 28%. At infancy, the gross motor milestones differentiated the children. From age 18 months, the fine motor milestones and those related to speech and social skills became more important. Brain MRI revealed no specific aetiology for subjects in SE. However, they had more often ≥ 3 abnormal findings in MRIs (thin corpus callosum and enlarged cerebral and cerebellar CSF spaces). In VBM, subjects in full-time SE had smaller global white matter, CSF, and total brain volumes than controls. Compared with controls, subjects with intellectual disabilities had regional volume alterations (greater grey matter volumes in the anterior cingulate cortex bilaterally, smaller grey matter volume in left thalamus and left cerebellar hemisphere, greater white matter volume in the left fronto-parietal region, and smaller white matter volumes bilaterally in the posterior limbs of the internal capsules). In conclusion, the epidemiological studies emphasized several factors that increased the probability of SE placement, useful as a framework for interventional studies. The global and regional brain MRI findings provide an interesting basis for future investigations of learning-related brain structures in young subjects with cognitive impairments or intellectual disabilities of unexplained, familial aetiology.

Opioid dependence: Brain structure and function : a magnetic resonance imaging, neuropsychological, and electromagnetic study

Background: Opiod dependence is a chronic severe brain disorder associated with enormous health and social problems. The relapse back to opioid abuse is very high especially in early abstinence, but neuropsychological and neurophysiological deficits during opioid abuse or soon after cessation of opioids are scarcely investigated. Also the structural brain changes and their correlations with the length of opioid abuse or abuse onset age are not known. In this study the cognitive functions, neural basis of cognitive dysfunction, and brain structural changes was studied in opioid-dependent patients and in age and sex matched healthy controls. Materials and methods: All subjects participating in the study, 23 opioid dependents of whom, 15 were also benzodiazepine and five cannabis co-dependent and 18 healthy age and sex matched controls went through Structured Clinical Interviews (SCID) to obtain DSM-IV axis I and II diagnosis and to exclude psychiatric illness not related to opioid dependence or personality disorders. Simultaneous magnetoencephalography (MEG) and electroencephalography (EEG) measurements were done on 21 opioid-dependent individuals on the day of hospitalization for withdrawal therapy. The neural basis of auditory processing was studied and pre-attentive attention and sensory memory were investigated. During the withdrawal 15 opioid-dependent patients participated in neuropsychological tests, measuring fluid intelligence, attention and working memory, verbal and visual memory, and executive functions. Fifteen healthy subjects served as controls for the MEG-EEG measurements and neuropsychological assessment. The brain magnetic resonance imaging (MRI) was obtained from 17 patients after approximately two weeks abstinence, and from 17 controls. The areas of different brain structures and the absolute and relative volumes of cerebrum, cerebral white and gray matter, and cerebrospinal fluid (CSF) spaces were measured and the Sylvian fissure ratio (SFR) and bifrontal ratio were calculated. Also correlation between the cerebral measures and neuropsychological performance was done. Results: MEG-EEG measurements showed that compared to controls the opioid-dependent patients had delayed mismatch negativity (MMN) response to novel sounds in the EEG and P3am on the contralateral hemisphere to the stimulated ear in MEG. The equivalent current dipole (ECD) of N1m response was stronger in patients with benzodiazepine co-dependence than those without benzodiazepine co-dependence or controls. In early abstinence the opioid dependents performed poorer than the controls in tests measuring attention and working memory, executive function and fluid intelligence. Test results of the Culture Fair Intelligence Test (CFIT), testing fluid intelligence, and Paced Auditory Serial Addition Test (PASAT), measuring attention and working memory correlated positively with the days of abstinence. MRI measurements showed that the relative volume of CSF was significantly larger in opioid dependents, which could also be seen in visual analysis. Also Sylvian fissures, expressed by SFR were wider in patients, which correlated negatively with the age of opioid abuse onset. In controls the relative gray matter volume had a positive correlation with composite cognitive performance, but this correlation was not found in opioid dependents in early abstinence. Conclusions: Opioid dependents had wide Sylvian fissures and CSF spaces indicating frontotemporal atrophy. Dilatation of Sylvian fissures correlated with the abuse onset age. During early withdrawal cognitive performance of opioid dependents was impaired. While intoxicated the pre-attentive attention to novel stimulus was delayed and benzodiazepine co-dependence impaired sound detection. All these changes point to disturbances on frontotemporal areas.

Nanomaterial based Magnetic Resonance Imaging of Cancer

Magnetic Resonance Imaging (MRI) is a widely used non-invasive medical tool for detection and diagnosis of cancer. In recent years, MRI has witnessed significant contributions from nanotechnology to incorporate advanced features such as multimodality of nanoparticles, therapeutic delivery, specific targeting and the optical detectability for molecular imaging. Accurate composition, right scheme of surface chemistry and properly designed structure is essential for achieving desired properties of nanomaterials such as non-fouling surface, high imaging contrast, chemical stability, target specificity and/or multimodality. This review provides an overview of the recent progress in theranostic nanomaterials in imaging and the development of nanomaterial based magnetic resonance imaging of cancer. In particular, targeted theranostics is a promising approach along with its targeting strategy in cancer treatment using MRI and multimodal imaging. We also discuss recent advances in integrin mediated targeted MRI of cancer.

Graphene oxide-Fe3O4 nanoparticle composite with high transverse proton relaxivity value for magnetic resonance imaging

The potential of graphene oxide-Fe3O4 nanoparticle (GO-Fe3O4) composite as an image contrast enhancing material in magnetic resonance imaging has been investigated. Proton relaxivity values were obtained in three different homogeneous dispersions of GO-Fe3O4 composites synthesized by precipitating Fe3O4 nanoparticles in three different reaction mixtures containing 0.01 g, 0.1 g, and 0.2 g of graphene oxide. A noticeable difference in proton relaxivity values was observed between the three cases. A comprehensive structural and magnetic characterization revealed discrete differences in the extent of reduction of the graphene oxide and spacing between the graphene oxide sheets in the three composites. The GO-Fe3O4 composite framework that contained graphene oxide with least extent of reduction of the carboxyl groups and largest spacing between the graphene oxide sheets provided the optimum structure for yielding a very high transverse proton relaxivity value. It was found that the GO-Fe3O4 composites possessed good biocompatibility with normal cell lines, whereas they exhibited considerable toxicity towards breast cancer cells. (C) 2015 AIP Publishing LLC.

MnFe2O4-Fe3O4 core-shell nanoparticles as a potential contrast agent for magnetic resonance imaging

In recent years, magnetic core-shell nanoparticles have received widespread attention due to their unique properties that can be used for various applications. We introduce here a magnetic core-shell nanoparticle system for potential application as a contrast agent in magnetic resonance imaging (MRI). MnFe2O4-Fe3O4 core-shell nanoparticles were synthesized by the wet-chemical synthesis method. Detailed structural and compositional charaterization confirmed the formation of a core-shell microstructure for the nanoparticles. Magnetic charaterization revealed the superparamagnetic nature of the as-synthesized core-shell nanoparticles. Average size and saturation magnetization values obtained for the as-synthesized core-shell nanoparticle were 12.5 nm and 69.34 emu g(-1) respectively. The transverse relaxivity value of the water protons obtained in the presence of the core-shell nanoparticles was 184.1 mM(-1) s(-1). To investigate the effect of the core-shell geometry towards enhancing the relaxivity value, transverse relaxivity values were also obtained in the presence of separately synthesized single phase Fe3O4 and MnFe2O4 nanoparticles. Average size and saturation magnetization values for the as-synthesized Fe3O4 nanoparticles were 12 nm and 65.8 emu g(-1) respectively. Average size and saturation magnetization values for the MnFe2O4 nanoparticles were 9 nm and 61.5 emu g(-1) respectively. The transverse relaxivity value obtained in the presence of single phase Fe3O4 and MnFe2O4 nanoparticles was 96.6 and 83.2 mM(-1) s(-1) respectively. All the nanoparticles (core-shell and single phase) were coated with chitosan by a surfactant exchange reaction before determining the relaxivity values. For similar nanoparticle sizes and saturation magnetization values, the highest value of the transverse relaxivity in the case of core-shell nanoparticles clearly illustrated that the difference in the magnetic nature of the core and shell phases in the core-shell nanoparticles creates greater magnetic inhomogeneity in the surrounding medium yielding a high value for proton relaxivity. The MnFe2O4-Fe3O4 core-shell nanoparticles exhibited extremely low toxicity towards the MCF-7 cell line. Taken together, this opens up new avenues for the use of core-shell nanoparticles in MRI.

Usefulness of computed tomography and magnetic resonance imaging in radiotherapy planning for the treatment of brain tumors

Advanced diagnostic techniques such as magnetic resonance imaging and computed tomography have become useful tools for confirmation of presumptive diagnosis of structural lesions in the brain such as encephalic neoplasms in small animal veterinary practice in Colombia, allowing an effective treatment planning that is more specific and less invasive for this type of pathology.

Cardiac Magnetic Resonance Imaging: What Can It Add to Our Knowledge of the Right Ventricle in Pulmonary Arterial Hypertension?

Pulmonary arterial hypertension (PAH) is a disease of the pulmonary vasculature characterized by vasoconstriction and vascular remodeling leading to a progressive increase in pulmonary vascular resistance (PVR). It is becoming increasingly recognized that it is the response of the right ventricle (RV) to the increased afterload resulting from this increase in PVR that is the most important determinant of patient outcome. A range of hemodynamic, structural, and functional measures associated with the RV have been found to have prognostic importance in PAH and, therefore, have potential value as parameters for the evaluation and follow-up of patients. If such measures are to be used clinically, there is a need for simple, reproducible, accurate, easy-to-use, and noninvasive methods to assess them. Cardiac magnetic resonance imaging (CMRI) is regarded as the "gold standard" method for assessment of the RV, the complex structure of which makes accurate assessment by 2-dimensional methods, such as echocardiography, challenging. However, the majority of data concerning the use of CMRI in PAH have come from studies evaluating a variety of different measures and using different techniques and protocols, and there is a clear need for the development of standardized methodology if CMRI is to be established in the routine assessment of patients with PAH. Should such standards be developed, it seems likely that CMRI will become an important method for the noninvasive assessment and monitoring of patients with PAH. (C) 2012 Elsevier Inc. All rights reserved. (Am J Cardiol 2012;110[suppl]:25S-31S)

Distensibility and diameter of ascending aorta assessed by cardiac magnetic resonance imaging in adults with tetralogy of fallot or complete transposition

Structural abnormalities of the medial aorta have been described for conotruncal defects (e.g., tetralogy of Fallot [TOF] and complete transposition of the great arteries (dextrotransposition [d]-TGA). In TOF, progressive aortic dilation is a frequent finding. In patients with d-TGA with an atrial switch, this problem is less often described. The aim of the present study was to compare the extent of dilative aortopathy and aortic distensibility in adults with an atrial switch procedure (n = 39) to that in adults with repaired TOF (n = 39) and controls (n = 39), using cardiac magnetic resonance imaging. The groups were matched for age and gender. Diameters of the aorta indexed to the body surface area were significantly increased in the patients with d-TGA and TOF compared to that of the controls at the aortic sinus up to the level of the right pulmonary artery. On multivariate testing, the diagnosis of a conotruncal defect (β = 0.260; p = 0.003) and aortic regurgitant fraction (β = 0.405; p <0.001) were independent predictors of an increased aortic sinus diameter. Ascending aorta distensibility was significantly reduced in those with d-TGA and TOF compared to controls: 3.6 (interquartile range 1.5 to 4.4) versus 2.8 (interquartile range 2.0 to 3.7) versus 5.5 (interquartile range 4.8 to 6.9) ×10(-3) mm Hg(-1) (p <0.001). The independent predictors of ascending aorta distensibility were the diagnosis of a conotruncal defect (p <0.001) and age (p = 0.028). In conclusion, intrinsic aortopathy, manifested as increased ascending aortic diameters and reduced ascending aortic distensibility, is not only evident in adults with TOF, but also in adults with d-TGA and an atrial switch procedure. Long-term follow-up is needed to monitor the aortic size in both patient groups.