AIMC Topic: Positron-Emission Tomography
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Predicting changes in brain metabolism and progression from mild cognitive impairment to dementia using multitask Deep Learning models and explainable AI.
NeuroImage
Jun 26, 2024
BACKGROUND: The prediction of Alzheimer's disease (AD) progression from its early stages is a research priority. In this context, the use of Artificial Intelligence (AI) in AD has experienced a notable surge in recent years. However, existing investi...
Machine Learning for Automated Interpretation of Fluorodeoxyglucose-Positron Emission Tomography Scans in Lymphoma.
Journal of clinical oncology : official journal of the American Society of Clinical Oncology
Jun 21, 2024
A deep learning model for generating [F]FDG PET Images from early-phase [F]Florbetapir and [F]Flutemetamol PET images.
European journal of nuclear medicine and molecular imaging
Jun 11, 2024
INTRODUCTION: Amyloid-β (Aβ) plaques is a significant hallmark of Alzheimer's disease (AD), detectable via amyloid-PET imaging. The Fluorine-18-Fluorodeoxyglucose ([F]FDG) PET scan tracks cerebral glucose metabolism, correlated with synaptic dysfunct...
Automated Lugano Metabolic Response Assessment in F-Fluorodeoxyglucose-Avid Non-Hodgkin Lymphoma With Deep Learning on F-Fluorodeoxyglucose-Positron Emission Tomography.
Journal of clinical oncology : official journal of the American Society of Clinical Oncology
Jun 6, 2024
PURPOSE: Artificial intelligence can reduce the time used by physicians on radiological assessments. For F-fluorodeoxyglucose-avid lymphomas, obtaining complete metabolic response (CMR) by end of treatment is prognostic.
Precise positioning of gamma ray interactions in multiplexed pixelated scintillators using artificial neural networks.
Biomedical physics & engineering express
Jun 5, 2024
. The positioning ofray interactions in positron emission tomography (PET) detectors is commonly made through the evaluation of the Anger logic flood histograms. machine learning techniques, leveraging features extracted from signal waveform, have de...
Anatomically Guided PET Image Reconstruction Using Conditional Weakly-Supervised Multi-Task Learning Integrating Self-Attention.
IEEE transactions on medical imaging
Jun 3, 2024
To address the lack of high-quality training labels in positron emission tomography (PET) imaging, weakly-supervised reconstruction methods that generate network-based mappings between prior images and noisy targets have been developed. However, the ...
Transformer-CNN hybrid network for improving PET time of flight prediction.
Physics in medicine and biology
May 30, 2024
In positron emission tomography (PET) reconstruction, the integration of time-of-flight (TOF) information, known as TOF-PET, has been a major research focus. Compared to traditional reconstruction methods, the introduction of TOF enhances the signal-...
A multimodal machine learning model for predicting dementia conversion in Alzheimer's disease.
Scientific reports
May 29, 2024
Alzheimer's disease (AD) accounts for 60-70% of the population with dementia. Mild cognitive impairment (MCI) is a diagnostic entity defined as an intermediate stage between subjective cognitive decline and dementia, and about 10-15% of people annual...
Automated glioblastoma patient classification using hypoxia levels measured through magnetic resonance images.
BMC neuroscience
May 25, 2024
INTRODUCTION: The challenge of treating Glioblastoma (GBM) tumors is due to various mechanisms that make the tumor resistant to radiation therapy. One of these mechanisms is hypoxia, and therefore, determining the level of hypoxia can improve treatme...
Deep learning generation of preclinical positron emission tomography (PET) images from low-count PET with task-based performance assessment.
Medical physics
May 6, 2024
BACKGROUND: Preclinical low-count positron emission tomography (LC-PET) imaging offers numerous advantages such as facilitating imaging logistics, enabling longitudinal studies of long- and short-lived isotopes as well as increasing scanner throughpu...
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