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

Attenuation correction for brain PET imaging using deep neural network based on dixon and ZTE MR images

Positron Emission Tomography (PET) is a functional imaging modality widely used in neuroscience studies. To obtain meaningful quantitative results from PET images, attenuation correction is necessary during image reconstruction. For PET/MR hybrid systems, PET attenuation is challenging as Magnetic Resonance (MR) images do not reflect attenuation coefficients directly. To address this issue, we present deep neural network methods to derive the continuous attenuation coefficients for brain PET imaging from MR images. With only Dixon MR images as the network input, the existing U-net structure was adopted and analysis using forty patient data sets shows it is superior than other Dixon based methods. When both Dixon and zero echo time (ZTE) images are available, we have proposed a modified U-net structure, named GroupU-net, to efficiently make use of both Dixon and ZTE information through group convolution modules when the network goes deeper. Quantitative analysis based on fourteen real patient data sets demonstrates that both network approaches can perform better than the standard methods, and the proposed network structure can further reduce the PET quantification error compared to the U-net structure.Comment: 15 pages, 12 figure

Neuroconductor: an R platform for medical imaging analysis

Neuroconductor (https://neuroconductor.org) is an open-source platform for rapid testing and dissemination of reproducible computational imaging software. The goals of the project are to: (i) provide a centralized repository of R software dedicated to image analysis, (ii) disseminate software updates quickly, (iii) train a large, diverse community of scientists using detailed tutorials and short courses, (iv) increase software quality via automatic and manual quality controls, and (v) promote reproducibility of image data analysis. Based on the programming language R (https://www.r-project.org/), Neuroconductor starts with 51 inter-operable packages that cover multiple areas of imaging including visualization, data processing and storage, and statistical inference. Neuroconductor accepts new R package submissions, which are subject to a formal review and continuous automated testing. We provide a description of the purpose of Neuroconductor and the user and developer experience

Age-Specific 18F-FDG Image Processing Pipelines and Analysis Are Essential for Individual Mapping of Seizure Foci in Paediatric Patients with Intractable Epilepsy

Fluoro-18-deoxyglucose positron emission tomography (FDG-PET) is an important tool for the pre-surgical assessment of children with drug-resistant epilepsy. Standard assessment is carried out visually and this is often subjective and highly user-dependent. Voxel-wise statistics can be used to remove user-dependent biases by automatically identifying areas of significant hypo/hyper-metabolism, associated to the epileptogenic area. In the clinical settings, this analysis is carried out using commercially available software. These software packages suffer from two main limitations when applied to paediatric PET data: 1) paediatric scans are spatially normalised to an adult standard template and 2) statistical comparisons use an adult control dataset. The aim of this work is to provide a reliable observer-independent pipeline for the analysis of paediatric FDG-PET scans, as part of pre-surgical planning in epilepsy. METHODS: A pseudo-control dataset (n = 19 for 6-9y, n = 93 for 10-20y) was used to create two age-specific FDG-PET paediatric templates in standard paediatric space. The FDG-PET scans of 46 epilepsy patients (n = 16 for 6-9y, n = 30 for 10-17y) were retrospectively collated and analysed using voxel-wise statistics. This was implemented with the standard pipeline available in the commercial software Scenium and an in-house Statistical Parametric Mapping v.8 (SPM8) pipeline (including age-specific paediatric templates and normal database). A kappa test was used to assess the level of agreement between findings of voxel-wise analyses and the clinical diagnosis of each patient. The SPM8 pipeline was further validated using post-surgical seizure-free patients. RESULTS: Improved agreement with the clinical diagnosis was reported using SPM8, in terms of focus localisation, especially for the younger patient group: kScenium=0.489 versus kSPM=0.805. The proposed pipeline also showed a sensitivity of ~70% in both age ranges, for the localisation of hypo-metabolic areas on paediatric FDG-PET scans in post-surgical seizure-free patients. CONCLUSION: We show that by creating age-specific templates and using paediatric control databases, our pipeline provides an accurate and sensitive semi-quantitative method for assessing FDG-PET scans of patients under 18y

Non-standard templates for non-standard populations: optimizing template selection for voxel-based morphometry pre-processing

The human brain is a complex and powerful organ, directing every aspect of life from somatosensory and motor function to visceral responses to higher order cognition. Neurological and psychiatric disorders often disrupt normal functioning. While the clinical symptoms of such disorders are known, their biological underpinnings are not as clearly characterized. Structural neuroimaging is a powerful, non-invasive tool that can play a critical role in finding biomarkers of these illnesses. Currently, variations in pre-processing techniques yield inconsistent and conflicting results. As neuroimaging is a nascent branch of medical research, gold standards in imaging methodologies have not yet been established. Quantitatively validating and optimizing the way these images are preprocessed is the first step towards standardization. Voxel-based morphometry (VBM) is one technique that is commonly used to compare whole-brain structural differences between groups. Statistical tests are used to compare intensities of voxels throughout all brain scans in each group. In order to ensure that comparable voxels are being tested, the images must be fitted into a common space, which is done through image preprocessing. Spatial normalization to templates is an early pre-processing step that is executed unreliably as many options for both templates and normalization algorithms exist. To determine the effect variations in template usage may cause, we utilized a VBM approach to detect simulated lesions. Template performance was analyzed by comparing the accuracy with which the lesion was detected

A Standardized [18F]-FDG-PET Template for Spatial Normalization in Statistical Parametric Mapping of Dementia

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Normative Analysis of Individual Brain Differences Based on a Population MRI-Based Atlas of Cynomolgus Macaques

The developmental trajectory of the primate brain varies substantially with aging across subjects. However, this ubiquitous variability between individuals in brain structure is difficult to quantify and has thus essentially been ignored. Based on a large-scale structural magnetic resonance imaging dataset acquired from 162 cynomolgus macaques, we create a species-specific 3D template atlas of the macaque brain, and deploy normative modeling to characterize individual variations of cortical thickness (CT) and regional gray matter volume (GMV). We observed an overall decrease in total GMV and mean CT, and an increase in white matter volume from juvenile to early adult. Specifically, CT and regional GMV were greater in prefrontal and temporal cortices relative to early unimodal areas. Age-dependent trajectories of thickness and volume for each cortical region revealed an increase in the medial temporal lobe, and decreases in all other regions. A low percentage of highly individualized deviations of CT and GMV were identified (0.0021%, 0.0043%, respectively, P \u3c 0.05, false discovery rate [FDR]-corrected). Our approach provides a natural framework to parse individual neuroanatomical differences for use as a reference standard in macaque brain research, potentially enabling inferences regarding the degree to which behavioral or symptomatic variables map onto brain structure in future disease studies