AIMC Topic: Aniline Compounds
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Modeling autosomal dominant Alzheimer's disease with machine learning.
Alzheimer's & dementia : the journal of the Alzheimer's Association
Jan 21, 2021
INTRODUCTION: Machine learning models were used to discover novel disease trajectories for autosomal dominant Alzheimer's disease.
Improved amyloid burden quantification with nonspecific estimates using deep learning.
European journal of nuclear medicine and molecular imaging
Jan 7, 2021
PURPOSE: Standardized uptake value ratio (SUVr) used to quantify amyloid-β burden from amyloid-PET scans can be biased by variations in the tracer's nonspecific (NS) binding caused by the presence of cerebrovascular disease (CeVD). In this work, we p...
Visual interpretation of [F]Florbetaben PET supported by deep learning-based estimation of amyloid burden.
European journal of nuclear medicine and molecular imaging
Sep 29, 2020
PURPOSE: Amyloid PET which has been widely used for noninvasive assessment of cortical amyloid burden is visually interpreted in the clinical setting. As a fast and easy-to-use visual interpretation support system, we analyze whether the deep learnin...
Application of Deep Learning to Predict Standardized Uptake Value Ratio and Amyloid Status on F-Florbetapir PET Using ADNI Data.
AJNR. American journal of neuroradiology
Jun 4, 2020
BACKGROUND AND PURPOSE: Cortical amyloid quantification on PET by using the standardized uptake value ratio is valuable for research studies and clinical trials in Alzheimer disease. However, it is resource intensive, requiring co-registered MR imagi...
Deep learning-guided joint attenuation and scatter correction in multitracer neuroimaging studies.
Human brain mapping
May 21, 2020
PET attenuation correction (AC) on systems lacking CT/transmission scanning, such as dedicated brain PET scanners and hybrid PET/MRI, is challenging. Direct AC in image-space, wherein PET images corrected for attenuation and scatter are synthesized f...
The clinical feasibility of deep learning-based classification of amyloid PET images in visually equivocal cases.
European journal of nuclear medicine and molecular imaging
Dec 6, 2019
PURPOSE: Although most deep learning (DL) studies have reported excellent classification accuracy, these studies usually target typical Alzheimer's disease (AD) and normal cognition (NC) for which conventional visual assessment performs well. A clini...
Ultra-Low-Dose F-Florbetaben Amyloid PET Imaging Using Deep Learning with Multi-Contrast MRI Inputs.
Radiology
Dec 11, 2018
Purpose To reduce radiotracer requirements for amyloid PET/MRI without sacrificing diagnostic quality by using deep learning methods. Materials and Methods Forty data sets from 39 patients (mean age ± standard deviation [SD], 67 years ± 8), including...
Synthesis of Patient-Specific Transmission Data for PET Attenuation Correction for PET/MRI Neuroimaging Using a Convolutional Neural Network.
Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Aug 30, 2018
Attenuation correction is a notable challenge associated with simultaneous PET/MRI, particularly in neuroimaging, where sharp boundaries between air and bone volumes exist. This challenge leads to concerns about the visual and, more specifically, qua...
Optimization and modeling of methyl orange adsorption onto polyaniline nano-adsorbent through response surface methodology and differential evolution embedded neural network.
Journal of environmental management
Jun 27, 2018
Presence of pigments and dyes in water bodies are growing tremendously and pose as toxic materials and have severe health effects on human and aquatic creatures. Treatments methods for removal of these toxic dyes along with other pollutants are growi...
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