AIMC Topic: Positron-Emission Tomography
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A machine learning approach to screen for preclinical Alzheimer's disease.
Neurobiology of aging
May 4, 2021
Combining multimodal biomarkers could help in the early diagnosis of Alzheimer's disease (AD). We included 304 cognitively normal individuals from the INSIGHT-preAD cohort. Amyloid and neurodegeneration were assessed on F-florbetapir and F-fluorodeox...
Deep Learning for Fully Automated Prediction of Overall Survival in Patients with Oropharyngeal Cancer Using FDG-PET Imaging.
Clinical cancer research : an official journal of the American Association for Cancer Research
May 4, 2021
PURPOSE: Accurate prognostic stratification of patients with oropharyngeal squamous cell carcinoma (OPSCC) is crucial. We developed an objective and robust deep learning-based fully-automated tool called the DeepPET-OPSCC biomarker for predicting ove...
Prediction of the treatment outcome using machine learning with FDG-PET image-based multiparametric approach in patients with oral cavity squamous cell carcinoma.
Clinical radiology
Apr 30, 2021
AIM: To investigate the value of machine learning-based multiparametric analysis using 2-[F]-fluoro-2-deoxy-d-glucose positron-emission tomography (FDG-PET) images to predict treatment outcome in patients with oral cavity squamous cell carcinoma (OCS...
Increasing the confidence of F-Florbetaben PET interpretations: Machine learning quantitative approximation.
Revista espanola de medicina nuclear e imagen molecular
Apr 21, 2021
AIM: To assess the added value of semiquantitative parameters on the visual assessment and to study the patterns of F-Florbetaben brain deposition.
Deep learning-based attenuation correction for brain PET with various radiotracers.
Annals of nuclear medicine
Apr 3, 2021
OBJECTIVES: Attenuation correction (AC) is crucial for ensuring the quantitative accuracy of positron emission tomography (PET) imaging. However, obtaining accurate μ-maps from brain-dedicated PET scanners without AC acquisition mechanism is challeng...
Quantitative Molecular Positron Emission Tomography Imaging Using Advanced Deep Learning Techniques.
Annual review of biomedical engineering
Apr 2, 2021
The widespread availability of high-performance computing and the popularity of artificial intelligence (AI) with machine learning and deep learning (ML/DL) algorithms at the helm have stimulated the development of many applications involving the use...
Recent Advances in Imaging of Preclinical, Sporadic, and Autosomal Dominant Alzheimer's Disease.
Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics
Mar 29, 2021
Observing Alzheimer's disease (AD) pathological changes in vivo with neuroimaging provides invaluable opportunities to understand and predict the course of disease. Neuroimaging AD biomarkers also allow for real-time tracking of disease-modifying tre...
Convolutional neural networks for PET functional volume fully automatic segmentation: development and validation in a multi-center setting.
European journal of nuclear medicine and molecular imaging
Mar 27, 2021
PURPOSE: In this work, we addressed fully automatic determination of tumor functional uptake from positron emission tomography (PET) images without relying on other image modalities or additional prior constraints, in the context of multicenter image...
Emerging methods for the characterization of ischemic heart disease: ultrafast Doppler angiography, micro-CT, photon-counting CT, novel MRI and PET techniques, and artificial intelligence.
European radiology experimental
Mar 25, 2021
After an ischemic event, disruptive changes in the healthy myocardium may gradually develop and may ultimately turn into fibrotic scar. While these structural changes have been described by conventional imaging modalities mostly on a macroscopic scal...
Artificial neural networks for positioning of gamma interactions in monolithic PET detectors.
Physics in medicine and biology
Mar 23, 2021
To detect gamma rays with good spatial, timing and energy resolution while maintaining high sensitivity we need accurate and efficient algorithms to estimate the first gamma interaction position from the measured light distribution. Furthermore, mono...
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