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BACKGROUND Supervised machine learning has been proposed as a revolutionary approach for identifying sensitive medical image biomarkers (or combination of them) allowing for automatic diagnosis of individual subjects. The aim of this work was to assess the feasibility of a supervised machine learning algorithm for the assisted diagnosis of patients with(More)
[18F]-fluorodeoxyglucose (FDG) Positron Emission Tomography (PET) is a widely used diagnostic tool that can detect and quantify pathophysiology, as assessed through changes in cerebral glucose metabolism. [18F]-FDG PET scans can be analyzed using voxel-based statistical methods such as Statistical Parametric Mapping (SPM) that provide statistical maps of(More)
We have developed, optimized, and validated a method for partial volume effect (PVE) correction of oncological lesions in positron emission tomography (PET) clinical studies, based on recovery coefficients (RC) and on PET measurements of lesion-to-background ratio (L/B m) and of lesion metabolic volume. An operator-independent technique, based on an(More)
Diagnostic accuracy in FDG-PET imaging highly depends on the operating procedures. In this clinical study on dementia, we compared the diagnostic accuracy at a single-subject level of a) Clinical Scenarios, b) Standard FDG Images and c) Statistical Parametrical (SPM) Maps generated via a new optimized SPM procedure. We evaluated the added value of FDG-PET,(More)
Target volume delineation of Positron Emission Tomography (PET) images in radiation treatment planning is challenging because of the low spatial resolution and high noise level in PET data. The aim of this work is the development of an accurate and fast method for semi-automatic segmentation of metabolic regions on PET images. For this purpose, an algorithm(More)
Bioinformatics traditionally deals with computational approaches to the analysis of big data from high-throughput technologies as genomics, proteomics, and sequencing. Bioinformatics analysis allows extraction of new information from big data that might help to better assess the biological details at a molecular and cellular level. The wide-scale and(More)
Recommended guidelines for the diagnosis of dementia due to Alzheimer's Disease (AD) were revised in recent years, including Positron Emission Tomography (PET) as an in-vivo diagnostic imaging technique for the diagnosis of neurodegeneration. In particular PET, using 18Ffluorodeoxiglucouse ([18F]FDG), is able to detect very early changes of glucose(More)
INTRODUCTION Differential diagnosis of parkinsonian disorders can be difficult on clinical grounds, especially in the early stage. Recent advancements in 18-F-fluorodeoxyglucose positron emission tomography (FDG-PET) imaging reveals different patterns of regional glucose metabolism in idiopathic Parkinson's disease (IPD) and atypical parkinsonian syndromes,(More)
OBJECTIVE The aim of this work was to assess robustness and reliability of an adaptive thresholding algorithm for the biological target volume estimation incorporating reconstruction parameters. METHOD In a multicenter study, a phantom with spheres of different diameters (6.5-57.4 mm) was filled with (18)F-FDG at different target-to-background ratios(More)
BACKGROUND Statistical Parametric Mapping (SPM) has been applied for single-subject evaluation of [18F]FDG uptake in Alzheimer Disease (AD). In a single-subject framework, the patient is compared to a dataset of [18F]FDG PET images from healthy subjects (HS) evaluating brain metabolic abnormalities. No studies exist that assess the effects on SPM analysis(More)
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