AIMC Topic: Positron-Emission Tomography
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Toward High-Throughput Artificial Intelligence-Based Segmentation in Oncological PET Imaging.
PET clinics
Oct 1, 2021
Artificial intelligence (AI) techniques for image-based segmentation have garnered much attention in recent years. Convolutional neural networks have shown impressive results and potential toward fully automated segmentation in medical imaging, and p...
Artificial Intelligence-Based Image Enhancement in PET Imaging: Noise Reduction and Resolution Enhancement.
PET clinics
Oct 1, 2021
High noise and low spatial resolution are two key confounding factors that limit the qualitative and quantitative accuracy of PET images. Artificial intelligence models for image denoising and deblurring are becoming increasingly popular for the post...
The Evolution of Image Reconstruction in PET: From Filtered Back-Projection to Artificial Intelligence.
PET clinics
Oct 1, 2021
PET can provide functional images revealing physiologic processes in vivo. Although PET has many applications, there are still some limitations that compromise its precision: the absorption of photons in the body causes signal attenuation; the dead-t...
Potential Applications of Artificial Intelligence and Machine Learning in Radiochemistry and Radiochemical Engineering.
PET clinics
Oct 1, 2021
Artificial intelligence and machine learning are poised to disrupt PET imaging from bench to clinic. In this perspective, the authors offer insights into how the technology could be applied to improve the radiosynthesis of new radiopharmaceuticals fo...
Objective Task-Based Evaluation of Artificial Intelligence-Based Medical Imaging Methods:: Framework, Strategies, and Role of the Physician.
PET clinics
Oct 1, 2021
Artificial intelligence-based methods are showing promise in medical imaging applications. There is substantial interest in clinical translation of these methods, requiring that they be evaluated rigorously. We lay out a framework for objective task-...
Feasibility of Deep Learning-Guided Attenuation and Scatter Correction of Whole-Body 68Ga-PSMA PET Studies in the Image Domain.
Clinical nuclear medicine
Aug 1, 2021
OBJECTIVE: This study evaluates the feasibility of direct scatter and attenuation correction of whole-body 68Ga-PSMA PET images in the image domain using deep learning.
Blinded Clinical Evaluation for Dementia of Alzheimer's Type Classification Using FDG-PET: A Comparison Between Feature-Engineered and Non-Feature-Engineered Machine Learning Methods.
Journal of Alzheimer's disease : JAD
Jan 1, 2021
BACKGROUND: Advanced machine learning methods can aid in the identification of dementia risk using neuroimaging-derived features including FDG-PET. However, to enable the translation of these methods and test their usefulness in clinical practice, it...
Machine Learning for the Prediction of Amyloid Positivity in Amnestic Mild Cognitive Impairment.
Journal of Alzheimer's disease : JAD
Jan 1, 2021
BACKGROUND: Amyloid-β (Aβ) evaluation in amnestic mild cognitive impairment (aMCI) patients is important for predicting conversion to Alzheimer's disease. However, Aβ evaluation through Aβ positron emission tomography (PET) is limited due to high cos...
Neuropsychological Tests
Apolipoproteins E
Mental Recall
Hippocampus
Cerebral Cortex
Humans
Machine Learning
Algorithms
Female
Reproducibility of Results
Risk Factors
Cohort Studies
Area Under Curve
Middle Aged
Aged
Magnetic Resonance Imaging
Amyloid beta-Peptides
Disease Progression
Positron-Emission Tomography
Aged, 80 and over
Predictive Value of Tests
Cognitive Dysfunction
Male
Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms.
Molecular imaging and biology
Aug 1, 2020
PURPOSE: Considerable progress has been made in the assessment and management of non-small cell lung cancer (NSCLC) patients based on mutation status in the epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene (KRAS). At the...
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