AIMC Topic: Positron-Emission Tomography
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Automated Neural Architecture Search for Cardiac Amyloidosis Classification from [18F]-Florbetaben PET Images.
Journal of imaging informatics in medicine
Oct 2, 2024
Medical image classification using convolutional neural networks (CNNs) is promising but often requires extensive manual tuning for optimal model definition. Neural architecture search (NAS) automates this process, reducing human intervention signifi...
Deep learning-based binary classification of beta-amyloid plaques using 18 F florapronol PET.
Nuclear medicine communications
Sep 27, 2024
PURPOSE: This study aimed to investigate a deep learning model to classify amyloid plaque deposition in the brain PET images of patients suspected of Alzheimer's disease.
Development and validation of a machine learning model to predict myocardial blood flow and clinical outcomes from patients' electrocardiograms.
Cell reports. Medicine
Sep 25, 2024
We develop a machine learning (ML) model using electrocardiography (ECG) to predict myocardial blood flow reserve (MFR) and assess its prognostic value for major adverse cardiovascular events (MACEs). Using 3,639 ECG-positron emission tomography (PET...
A Machine Learning Approach for Predicting Biochemical Outcome After PSMA-PET-Guided Salvage Radiotherapy in Recurrent Prostate Cancer After Radical Prostatectomy: Retrospective Study.
JMIR cancer
Sep 20, 2024
BACKGROUND: Salvage radiation therapy (sRT) is often the sole curative option in patients with biochemical recurrence after radical prostatectomy. After sRT, we developed and validated a nomogram to predict freedom from biochemical failure.
Artificial intelligence-based analysis of behavior and brain images in cocaine-self-administered marmosets.
Journal of neuroscience methods
Sep 19, 2024
BACKGROUND: The sophisticated behavioral and cognitive repertoires of non-human primates (NHPs) make them suitable subjects for studies involving cocaine self-administration (SA) schedules. However, ethical considerations, adherence to the 3Rs princi...
Deep learning-based techniques for estimating high-quality full-dose positron emission tomography images from low-dose scans: a systematic review.
BMC medical imaging
Sep 11, 2024
This systematic review aimed to evaluate the potential of deep learning algorithms for converting low-dose Positron Emission Tomography (PET) images to full-dose PET images in different body regions. A total of 55 articles published between 2017 and ...
PET radiomics-based lymphovascular invasion prediction in lung cancer using multiple segmentation and multi-machine learning algorithms.
Physical and engineering sciences in medicine
Sep 3, 2024
The current study aimed to predict lymphovascular invasion (LVI) using multiple machine learning algorithms and multi-segmentation positron emission tomography (PET) radiomics in non-small cell lung cancer (NSCLC) patients, offering new avenues for p...
A Scoping Review of Machine-Learning Derived Radiomic Analysis of CT and PET Imaging to Investigate Atherosclerotic Cardiovascular Disease.
Tomography (Ann Arbor, Mich.)
Sep 3, 2024
BACKGROUND: Cardiovascular disease affects the carotid arteries, coronary arteries, aorta and the peripheral arteries. Radiomics involves the extraction of quantitative data from imaging features that are imperceptible to the eye. Radiomics analysis ...
Advancing Tau PET Quantification in Alzheimer Disease with Machine Learning: Introducing THETA, a Novel Tau Summary Measure.
Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Sep 3, 2024
Alzheimer disease (AD) exhibits spatially heterogeneous 3- or 4-repeat tau deposition across participants. Our overall goal was to develop an automated method to quantify the heterogeneous burden of tau deposition into a single number that would be c...
Deep learning applications for quantitative and qualitative PET in PET/MR: technical and clinical unmet needs.
Magma (New York, N.Y.)
Aug 21, 2024
We aim to provide an overview of technical and clinical unmet needs in deep learning (DL) applications for quantitative and qualitative PET in PET/MR, with a focus on attenuation correction, image enhancement, motion correction, kinetic modeling, and...
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