AIMC Topic: Positron-Emission Tomography
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Prediction Model and Nomogram for Amyloid Positivity Using Clinical and MRI Features in Individuals With Subjective Cognitive Decline.
Human brain mapping
Jun 1, 2025
There is an urgent need for the precise prediction of cerebral amyloidosis using noninvasive and accessible indicators to facilitate the early diagnosis of individuals with the preclinical stage of Alzheimer's disease (AD). Two hundred and four indiv...
Generation of synthetic CT from MRI for MRI-based attenuation correction of brain PET images using radiomics and machine learning.
Medical physics
Jun 1, 2025
BACKGROUND: Accurate quantitative PET imaging in neurological studies requires proper attenuation correction. MRI-guided attenuation correction in PET/MRI remains challenging owing to the lack of direct relationship between MRI intensities and linear...
Deep learning-based triple-tracer brain PET scanning in a single session: A simulation study using clinical data.
NeuroImage
Jun 1, 2025
OBJECTIVES: Multiplexed Positron Emission Tomography (PET) imaging allows simultaneous acquisition of multiple radiotracer signals, thus enhancing diagnostic capabilities, reducing scan times, and improving patient comfort. Traditional methods often ...
A CT-free deep-learning-based attenuation and scatter correction for copper-64 PET in different time-point scans.
Radiological physics and technology
Jun 1, 2025
This study aimed to develop and evaluate a deep-learning model for attenuation and scatter correction in whole-body 64Cu-based PET imaging. A swinUNETR model was implemented using the MONAI framework. Whole-body PET-nonAC and PET-CTAC image pairs wer...
Explainable PET-based intratumoral and peritumoral machine learning model for predicting visceral pleural invasion in clinical-stage IA non-small cell lung cancer: A two-center study.
Clinical radiology
Jun 1, 2025
AIM: The aim of this study was to develop a PET-based machine learning model for predicting visceral pleural invasion (VPI) in patients with clinical stage IA non-small cell lung cancer.
GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models.
Computer methods and programs in biomedicine
Jun 1, 2025
BACKGROUND AND OBJECTIVE: Research in the cross-modal medical image translation domain has been very productive over the past few years in tackling the scarce availability of large curated multi-modality datasets with the promising performance of GAN...
PET imaging of atherosclerosis: artificial intelligence applications and recent advancements.
Nuclear medicine communications
Jun 1, 2025
PET imaging has become a valuable tool for assessing atherosclerosis by targeting key processes such as inflammation and microcalcification. Among available tracers, 18F-sodium fluoride has demonstrated superior performance compared to 18F-fluorodeox...
Machine learning positioning algorithms for long semi-monolithic scintillator PET detectors.
Physics in medicine and biology
May 29, 2025
In this work, machine learning positioning algorithms are developed to improve the spatial resolutions of the semi-monolithic scintillator detectors in both monolithic () and depth of interaction () directions.Two long semi-monolithic scintillator de...
Whole-body CT-to-PET synthesis using a customized transformer-enhanced GAN.
Physics in medicine and biology
May 22, 2025
. Positron emission tomography with 2-deoxy-2-[fluorine-18]fluoro-D-glucose integrated with computed tomography (18F-FDG PET-CT) is a multi-modality medical imaging technique widely used for screening and diagnosis of lesions and tumors, in which, CT...
Exploiting network optimization stability for enhanced PET image denoising using deep image prior.
Physics in medicine and biology
May 16, 2025
. Positron emission tomography (PET) is affected by statistical noise due to constraints on tracer dose and scan duration, impacting both diagnostic performance and quantitative accuracy. While deep learning-based PET denoising methods have been used...
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