Development of a simulation platform for the evaluation of PET neuroimaging protocols in epilepsy

Monte Carlo simulation of PET studies is a reference tool for the evaluation and standardization of PET protocols. However, current Monte Carlo software codes require a high degree of knowledge in physics, mathematics and programming languages, in addition to a high cost of time and computational resources. These drawbacks make their use difficult for a large part of the scientific community. In order to overcome these limitations, a free and an efficient web-based platform was designed, implemented and validated for the simulation of realistic brain PET studies, and specifically employed for the generation of a wellvalidated large database of brain FDG-PET studies of patients with refractory epilepsy

Quantitative analysis of regional distribution of tau pathology with 11C-PBB3-PET in a clinical setting.

PURPOSE The recent developments of tau-positron emission tomography (tau-PET) enable in vivo assessment of neuropathological tau aggregates. Among the tau-specific tracers, the application of 11C-pyridinyl-butadienyl-benzothiazole 3 (11C-PBB3) in PET shows high sensitivity to Alzheimer disease (AD)-related tau deposition. The current study investigates the regional tau load in patients within the AD continuum, biomarker-negative individuals (BN) and patients with suspected non-AD pathophysiology (SNAP) using 11C-PBB3-PET. MATERIALS AND METHODS A total of 23 memory clinic outpatients with recent decline of episodic memory were examined using 11C-PBB3-PET. Pittsburg compound B (11C-PIB) PET was available for 17, 18F-flurodeoxyglucose (18F-FDG) PET for 16, and cerebrospinal fluid (CSF) protein levels for 11 patients. CSF biomarkers were considered abnormal based on Aβ42 ( 450 ng/L). The PET biomarkers were classified as positive or negative using statistical parametric mapping (SPM) analysis and visual assessment. Using the amyloid/tau/neurodegeneration (A/T/N) scheme, patients were grouped as within the AD continuum, SNAP, and BN based on amyloid and neurodegeneration status. The 11C-PBB3 load detected by PET was compared among the groups using both atlas-based and voxel-wise analyses. RESULTS Seven patients were identified as within the AD continuum, 10 SNAP and 6 BN. In voxel-wise analysis, significantly higher 11C-PBB3 binding was observed in the AD continuum group compared to the BN patients in the cingulate gyrus, tempo-parieto-occipital junction and frontal lobe. Compared to the SNAP group, patients within the AD continuum had a considerably increased 11C-PBB3 uptake in the posterior cingulate cortex. There was no significant difference between SNAP and BN groups. The atlas-based analysis supported the outcome of the voxel-wise quantification analysis. CONCLUSION Our results suggest that 11C-PBB3-PET can effectively analyze regional tau load and has the potential to differentiate patients in the AD continuum group from the BN and SNAP group

Simultaneous PET-MRI Studies of the Concordance of Atrophy and Hypometabolism in Syndromic Variants of Alzheimer's Disease and Frontotemporal Dementia: An Extended Case Series

Background: Simultaneous PET-MRI is used to compare patterns of cerebral hypometabolism and atrophy in six different dementia syndromes. Objectives: The primary objective was to conduct an initial exploratory study regarding the concordance of atrophy and hypometabolism in syndromic variants of Alzheimer’s disease (AD) and frontotemporal dementia (FTD). The secondary objective was to determine the effect of image analysis methods on determination of atrophy and hypometabolism. Method: PET and MRI data were acquired simultaneously on 24 subjects with six variants of AD and FTD (n = 4 per group). Atrophy was rated visually and also quantified with measures of cortical thickness. Hypometabolism was rated visually and also quantified using atlas- and SPM-based approaches. Concordance was measured using weighted Cohen’s kappa. Results: Atrophy-hypometabolism concordance differed markedly between patient groups; kappa scores ranged from 0.13 (nonfluent/agrammatic variant of primary progressive aphasia, nfvPPA) to 0.49 (posterior cortical variant of AD, PCA). Heterogeneity was also observed within groups; the confidence intervals of kappa scores ranging from 0–0.25 for PCA to 0.29–0.61 for nfvPPA. More widespread MRI and PET changes were identified using quantitative methods than on visual rating. Conclusion: The marked differences in concordance identified in this initial study may reflect differences in the molecular pathologies underlying AD and FTD syndromic variants but also operational differences in the methods used to diagnose these syndromes. The superior ability of quantitative methodologies to detect changes on PET and MRI, if confirmed on larger cohorts, may favor their usage over qualitative visual inspection in future clinical diagnostic practic

Time-dependent recovery of brain hypometabolism in neuro-COVID-19 patients

Purpose We evaluated brain metabolic dysfunctions and associations with neurological and biological parameters in acute, subacute and chronic COVID-19 phases to provide deeper insights into the pathophysiology of the disease.Methods Twenty-six patients with neurological symptoms (neuro-COVID-19) and [F-18]FDG-PET were included. Seven patients were acute (< 1 month (m) after onset), 12 subacute (4 >= 1-m, 4 >= 2-m and 4 >= 3-m) and 7 with neuro-post-COVID-19 (3 >= 5-m and 4 >= 7-9-m). One patient was evaluated longitudinally (acute and 5-m). Brain hypo- and hypermetabolism were analysed at single-subject and group levels. Correlations between severity/extent of brain hypo- and hypermetabolism and biological (oxygen saturation and C-reactive protein) and clinical variables (global cognition and Body Mass Index) were assessed.Results The "fronto-insular cortex" emerged as the hypometabolic hallmark of neuro-COVID-19. Acute patients showed the most severe hypometabolism affecting several cortical regions. Three-m and 5-m patients showed a progressive reduction of hypometabolism, with limited frontal clusters. After 7-9 months, no brain hypometabolism was detected. The patient evaluated longitudinally showed a diffuse brain hypometabolism in the acute phase, almost recovered after 5 months. Brain hypometabolism correlated with cognitive dysfunction, low blood saturation and high inflammatory status. Hypermetabolism in the brainstem, cerebellum, hippocampus and amygdala persisted over time and correlated with inflammation status.Conclusion Synergistic effects of systemic virus-mediated inflammation and transient hypoxia yield a dysfunction of the fronto-insular cortex, a signature of CNS involvement in neuro-COVID-19. This brain dysfunction is likely to be transient and almost reversible. The long-lasting brain hypermetabolism seems to reflect persistent inflammation processes

Cerebral F18 -FDG PET CT in Children: Patterns during Normal Childhood and Clinical Application of Statistical Parametric Mapping

The first aim was to recruit and analyse a high quality dataset of cerebral FDG PET CT scans in neurologically normal children. Using qualitative, semi-quantitative and statistical parametric mapping (SPM) techniques, the results showed that a pattern of FDG uptake similar to adults does not occur by one year of age as was previously believed, but the regional FDG uptake changes throughout childhood driven by differing age related regional rates of increasing FDG uptake. The second aim was to use this normal dataset in the clinical analysis of cerebral FDG PET CT scans in children with epilepsy and Neurofibromatosis type 1 (NF1). The normal dataset was validated for single-subject-versus-group SPM analysis and was highly specific for identifying the epileptogenic focus likely to result in a good post-operative outcome in children with epilepsy. Qualitative, semi-quantitative and group-versus-group SPM analyses were applied to FDG PET CT scans in children with NF1. The results showed reduced metabolism in the thalami and medial temporal lobes compared to neurologically normal children. This thesis has produced novel findings that advance the understanding of childhood brain development and has developed SPM techniques that can be applied to cerebral FDG PET CT scans in children with neurological disorders

Improvements in the registration of multimodal medical imaging : application to intensity inhomogeneity and partial volume corrections

Alignment or registration of medical images has a relevant role on clinical diagnostic and treatment decisions as well as in research settings. With the advent of new technologies for multimodal imaging, robust registration of functional and anatomical information is still a challenge, particular in small-animal imaging given the lesser structural content of certain anatomical parts, such as the brain, than in humans. Besides, patient-dependent and acquisition artefacts affecting the images information content further complicate registration, as is the case of intensity inhomogeneities (IIH) showing in MRI and the partial volume effect (PVE) attached to PET imaging. Reference methods exist for accurate image registration but their performance is severely deteriorated in situations involving little images Overlap. While several approaches to IIH and PVE correction exist these methods still do not guarantee or rely on robust registration. This Thesis focuses on overcoming current limitations af registration to enable novel IIH and PVE correction methods.El registre d'imatges mèdiques té un paper rellevant en les decisions de diagnòstic i tractament clíniques així com en la recerca. Amb el desenvolupament de noves tecnologies d'imatge multimodal, el registre robust d'informació funcional i anatòmica és encara avui un repte, en particular, en imatge de petit animal amb un menor contingut estructural que en humans de certes parts anatòmiques com el cervell. A més, els artefactes induïts pel propi pacient i per la tècnica d'adquisició que afecten el contingut d'informació de les imatges complica encara més el procés de registre. És el cas de les inhomogeneïtats d'intensitat (IIH) que apareixen a les RM i de l'efecte de volum parcial (PVE) característic en PET. Tot i que existeixen mètodes de referència pel registre acurat d'imatges la seva eficàcia es veu greument minvada en casos de poc solapament entre les imatges. De la mateixa manera, també existeixen mètodes per la correcció d'IIH i de PVE però que no garanteixen o que requereixen un registre robust. Aquesta tesi es centra en superar aquestes limitacions sobre el registre per habilitar nous mètodes per la correcció d'IIH i de PVE

Combined Evaluation of FDG-PET and MRI Improves Detection and Differentiation of Dementia

INTRODUCTION: Various biomarkers have been reported in recent literature regarding imaging abnormalities in different types of dementia. These biomarkers have helped to significantly improve early detection and also differentiation of various dementia syndromes. In this study, we systematically applied whole-brain and region-of-interest (ROI) based support vector machine classification separately and on combined information from different imaging modalities to improve the detection and differentiation of different types of dementia. METHODS: Patients with clinically diagnosed Alzheimer's disease (AD: n = 21), with frontotemporal lobar degeneration (FTLD: n = 14) and control subjects (n = 13) underwent both [F18]fluorodeoxyglucose positron emission tomography (FDG-PET) scanning and magnetic resonance imaging (MRI), together with clinical and behavioral assessment. FDG-PET and MRI data were commonly processed to get a precise overlap of all regions in both modalities. Support vector machine classification was applied with varying parameters separately for both modalities and to combined information obtained from MR and FDG-PET images. ROIs were extracted from comprehensive systematic and quantitative meta-analyses investigating both disorders. RESULTS: Using single-modality whole-brain and ROI information FDG-PET provided highest accuracy rates for both, detection and differentiation of AD and FTLD compared to structural information from MRI. The ROI-based multimodal classification, combining FDG-PET and MRI information, was highly superior to the unimodal approach and to the whole-brain pattern classification. With this method, accuracy rate of up to 92% for the differentiation of the three groups and an accuracy of 94% for the differentiation of AD and FTLD patients was obtained. CONCLUSION: Accuracy rate obtained using combined information from both imaging modalities is the highest reported up to now for differentiation of both types of dementia. Our results indicate a substantial gain in accuracy using combined FDG-PET and MRI information and suggest the incorporation of such approaches to clinical diagnosis and to differential diagnostic procedures of neurodegenerative disorders

Using CT Data to Improve the Quantitative Analysis of 18F-FBB PET Neuroimages

18F-FBB PET is a neuroimaging modality that is been increasingly used to assess brain amyloid deposits in potential patients with Alzheimer’s disease (AD). In this work, we analyze the usefulness of these data to distinguish between AD and non-AD patients. A dataset with 18F-FBB PET brain images from 94 subjects diagnosed with AD and other disorders was evaluated by means of multiple analyses based on t-test, ANOVA, Fisher Discriminant Analysis and Support Vector Machine (SVM) classification. In addition, we propose to calculate amyloid standardized uptake values (SUVs) using only gray-matter voxels, which can be estimated using Computed Tomography (CT) images. This approach allows assessing potential brain amyloid deposits along with the gray matter loss and takes advantage of the structural information provided by most of the scanners used for PET examination, which allow simultaneous PET and CT data acquisition. The results obtained in this work suggest that SUVs calculated according to the proposed method allow AD and non-AD subjects to be more accurately differentiated than using SUVs calculated with standard approaches.This work was supported by the MINECO/FEDER under the TEC2012-34306 and TEC2015-64718-R projects and the Ministry of Economy, Innovation, Science and Employment of the Junta de Andalucía under the Excellence Project P11-TIC- 7103. The work was also supported by the Vicerectorate of Research and Knowledge Transfer of the University of Granada

Aprendizagem profunda para o diagnóstico da doença de Alzheimer com neuroimagem 18F-FDG PET

Neurodegenerative disease is the term used for a range of incurable and debilitating conditions affecting the human's nervous system. Amongst these conditions, Alzheimer's Disease (AD) is responsible for the greatest burden both for the number of people affected and for the high costs in medical care. The challenges of the disease are related to the subtle symptoms, the increasing pace of disability and the long period of time over which patients will require special care. Recent research efforts have been dedicated to the development of computational tools that can be integrated into the workflow of doctors as a complement to support early diagnosis and targeted treatments. This dissertation aims to study the application of Deep Learning (DL) techniques for the automated classification of AD. The study focuses on the role of PET neuroimaging as a biomarker of neurodegenerative diseases, namely in classifying healthy versus AD patients. PET images of the cerebral metabolism of glucose with fluorine 18 (18F) fluorodeoxyglucose (18F FGD) were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The pre-processed dataset is used to train two Convolutional Neural Networks (CNNs). The first CNN architecture aims to explore transfer learning as a promising solution to the data challenge by using a 2D Inception V3 model, from Google, previously trained on a large dataset. This approach requires a preprocessing step in which the PET volumetric data is converted into a two-dimensional input image which is the input to the pre-trained model. The second approach involves a custom 3D-CNN to take advantage of spatial patterns on the full PET volumes by using 3D filters and 3D pooling layers. The comparative study highlights the performance and robustness of these two models in dealing with the limited availability of the labelled data. The performance of the estimators is evaluated through a cross-validation procedure, giving a score of 83.62% for the 2D-CNN and 86.80% for the 3D-CNN. The results achieved contribute to the understanding of the effectiveness of these methods in the diagnosis of AD. Given the expected margin for improvements, they can be considered promising and in line with the current state of the art.Doença neurodegenerativa é um termo utilizado para uma série de condições incuráveis e debilitantes que afetam o sistema nervoso humano. Destas condições, a doença de Alzheimer (DA) é a mais preocupante, tanto pelo número de pessoas afetadas como pelos elevados custos em tratamento medico. Os principais desafios associados a esta doença estão relacionados com os sintomas subtis, o rápido desenvolvimento de incapacidade e ao longo período de tempo durante o qual os pacientes necessitarão de cuidados especiais. Pesquisas recentes têm sido dedicadas ao desenvolvimento de ferramentas computacionais capazes de ser integradas nos procedimentos médicos como complemento para apoiar o diagnóstico precoce e tratamentos adequados. Esta dissertação procura estudar a aplicação de técnicas de aprendizagem profunda (AP) na classificação automatizada da DA. Este estudo tem como foco principal o papel da neuroimagem PET como biomarcador de doenças neurodegenerativas, especialmente na classificação de pacientes saudáveis em comparação com pacientes com DA. Imagens PET do metabolismo cerebral de glucose com flúor-18 (18F) fluorodesoxiglucose (18F FGD) foram obtidas através da base de dados da Alzheimer's Dissesse Neuroimaging Initiative (ADNI). O dataset pré-processado é usado para treinar duas redes neurais convulsionais (RNCs). A arquitetura da primeira RNC procura explorar a transferência de aprendizagem como uma solução promissora para o problema dos dados através da utilização de um modelo Inception V3 2D, da Google, previamente treinado num dataset maior. Esta abordagem requer um passo de pré -processamento onde dados volumétricos PET são convertidos numa imagem bidimensional que por sua vez será os dados de entrada do modelo pré-treinado. A segunda abordagem involve uma RNC 3D personalizada de maneira a utilizar os padrões espaciais presentes nos volumes PET através de filtros 3D e camadas de pooling 3D. O estudo comparativo foca-se no desempenho e robustez dos dois modelos ao lidar com a disponibilidade limitada de dados classificados. O desempenho dos classificadores é avaliado através de um processo de validação cruzada, atribuindo uma pontuação de 83.62% à RNC 2D e de 86.80% à RNC 3D. Os resultados obtidos contribuem para análise da eficácia destes métodos no diagnóstico da DA. Tendo em conta as melhorias expectáveis, estas poderam ser consideradas abordagens promissoras e de acordo com o atual estado da arte.Mestrado em Engenharia Eletrónica e Telecomunicaçõe

Metabolic lesion-deficit mapping of human cognition

In theory the most powerful technique for functional localization in cognitive neuroscience, lesion-deficit mapping is in practice distorted by unmodelled network disconnections and strong ‘parasitic’ dependencies between collaterally damaged ischaemic areas. High-dimensional multivariate modelling can overcome these defects, but only at the cost of commonly impracticable data scales. Here we develop lesion-deficit mapping with metabolic lesions—discrete areas of hypometabolism typically seen on interictal 18F-fluorodeoxyglucose PET imaging in patients with focal epilepsy—that inherently capture disconnection effects, and whose structural dependence patterns are sufficiently benign to allow the derivation of robust functional anatomical maps with modest data. In this cross-sectional study of 159 patients with widely distributed focal cortical impairments, we derive lesion-deficit maps of a broad range of psychological subdomains underlying affect and cognition. We demonstrate the potential clinical utility of the approach in guiding therapeutic resection for focal epilepsy or other neurosurgical indications by applying high-dimensional modelling to predict out-of-sample verbal IQ and depression from cortical metabolism alone