AIMC Topic: Positron-Emission Tomography
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Dixon-VIBE Deep Learning (DIVIDE) Pseudo-CT Synthesis for Pelvis PET/MR Attenuation Correction.
Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Aug 30, 2018
Whole-body attenuation correction (AC) is still challenging in combined PET/MR scanners. We describe Dixon-VIBE Deep Learning (DIVIDE), a deep-learning network that allows synthesizing pelvis pseudo-CT maps based only on the standard Dixon volumetric...
Reproducible evaluation of classification methods in Alzheimer's disease: Framework and application to MRI and PET data.
NeuroImage
Aug 18, 2018
A large number of papers have introduced novel machine learning and feature extraction methods for automatic classification of Alzheimer's disease (AD). However, while the vast majority of these works use the public dataset ADNI for evaluation, they ...
Aged, 80 and over
Male
Middle Aged
Image Processing, Computer-Assisted
Humans
Machine Learning
Female
Aged
Magnetic Resonance Imaging
Data Interpretation, Statistical
Radiopharmaceuticals
Alzheimer Disease
Positron-Emission Tomography
Datasets as Topic
Fluorodeoxyglucose F18
Neuroimaging
Atlases as Topic
Convolutional Neural Networks for Neuroimaging in Parkinson's Disease: Is Preprocessing Needed?
International journal of neural systems
Jul 26, 2018
Spatial and intensity normalizations are nowadays a prerequisite for neuroimaging analysis. Influenced by voxel-wise and other univariate comparisons, where these corrections are key, they are commonly applied to any type of analysis and imaging moda...
Multilevel Feature Representation of FDG-PET Brain Images for Diagnosing Alzheimer's Disease.
IEEE journal of biomedical and health informatics
Jul 18, 2018
Using a single imaging modality to diagnose Alzheimer's disease (AD) or mild cognitive impairment (MCI) is a challenging task. FluoroDeoxyGlucose Positron Emission Tomography (FDG-PET) is an important and effective modality used for that purpose. In ...
Global temporal lobe asymmetry as a semi-quantitative imaging biomarker for temporal lobe epilepsy lateralization: A machine learning classification study.
Hellenic journal of nuclear medicine
Jul 12, 2018
OBJECTIVE: The purpose of this study was to evaluate the utility of global semi-quantitative analysis via fluorine-18-flurodeoxyglucose positron emission tomography (F-FDG PET) at lateralizing seizure foci and diagnosing patients with unilateral temp...
Attenuation correction for brain PET imaging using deep neural network based on Dixon and ZTE MR images.
Physics in medicine and biology
Jun 13, 2018
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 sys...
Multicenter validation of [F]-FDG PET and support-vector machine discriminant analysis in automatically classifying patients with amyotrophic lateral sclerosis versus controls.
Amyotrophic lateral sclerosis & frontotemporal degeneration
Jun 4, 2018
OBJECTIVE: F-Fluorodeoxyglucose (F-FDG) positron emission tomography (PET) single-center studies using support vector machine (SVM) approach to differentiate amyotrophic lateral sclerosis (ALS) from controls have shown high overall accuracy on an ind...
Machine learning in the integration of simple variables for identifying patients with myocardial ischemia.
Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
May 22, 2018
BACKGROUND: A significant number of variables are obtained when characterizing patients suspected with myocardial ischemia or at risk of MACE. Guidelines typically use a handful of them to support further workup or therapeutic decisions. However, it ...
Adaptive template generation for amyloid PET using a deep learning approach.
Human brain mapping
May 11, 2018
Accurate spatial normalization (SN) of amyloid positron emission tomography (PET) images for Alzheimer's disease assessment without coregistered anatomical magnetic resonance imaging (MRI) of the same individual is technically challenging. In this st...
Identification of Alzheimer's disease and mild cognitive impairment using multimodal sparse hierarchical extreme learning machine.
Human brain mapping
May 7, 2018
Different modalities such as structural MRI, FDG-PET, and CSF have complementary information, which is likely to be very useful for diagnosis of AD and MCI. Therefore, it is possible to develop a more effective and accurate AD/MCI automatic diagnosis...
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