Latest AI and machine learning research in nuclear medicine for healthcare professionals.
BACKGROUND: To develop and validate deep learning (DL) and traditional clinical-metabolic (CM) models based on 18Â F-FDG PET/CT images for noninvasively predicting high-grade patterns (HGPs) of invasive lung adenocarcinoma (LUAD).
PURPOSE: The aim of this study was to generate and validate artificial delayed-phase technetium-99m methoxyisobutylisonitrile scintigraphy (aMIBI) images from early-phase technetium-99m methoxyisobutylisonitrile scintigraphy (eMIBI) images.
Alzheimer's Disease (AD) as one of the most prevalent neurodegenerative disorders worldwide, characterized by significant memory and cognitive decline...
In symptomatic patients undergoing coronary CTA for suspected coronary artery disease (CAD), we assessed if quantification of plaque burden, in additi...
This study presents a comprehensive investigation into the interplay between machine learning (ML) models, morphological features, and outdoor thermal...
BACKGROUND: The presence of a gap between adjacent detector blocks in Positron Emission Tomography (PET) scanners introduces a partial loss of project...
Alzheimer's disease (AD) represents a significant challenge due to its progressive neurodegenerative impact, particularly within an aging global demog...
Lung adenocarcinoma (LUAD) constitutes a major cause of cancer-related fatalities worldwide. Early identification of malignant pulmonary nodules const...
Radiomics allows extraction from medical images of quantitative features that are able to reveal tissue patterns that are generally invisible to human...
SPECT is a widely used imaging modality in nuclear medicine which provides essential functional insights into cardiovascular, neurological, and oncolo...
Low-dose PET offers a valuable means of minimizing radiation exposure in PET imaging. However, the prevalent practice of employing additional CT scans...
AIM: To develop a positron emission tomography/computed tomography (PET/CT)-based radiomics model for predicting programmed cell death ligand 1 (PD-L1...
BACKGROUND: Lung adenocarcinoma (LAC) comprises a substantial subset of non-small cell lung cancer (NSCLC) diagnoses, where epidermal growth factor re...
We previously demonstrated that a deep learning (DL) model of myocardial perfusion SPECT imaging improved accuracy for detection of obstructive corona...
OBJECTIVE: This study aimed to develop and validate a nomogram combining F-FDG PET radiomics and clinical factors to non-invasively predict bone marro...
The advent of sophisticated image analysis techniques has facilitated the extraction of increasingly complex data, such as radiomic features, from var...
PURPOSE: To explore the development and validation of automated machine learning (AutoML) models for F-FDG PET imaging-based radiomics signatures to p...
The choice of imaging modalities is essential in sarcoma management, as different techniques provide complementary information depending on tumor subt...
BACKGROUND: This study develops a deep learning-based automated lesion segmentation model for whole-body 3DF-fluorodeoxyglucose (FDG)-Position emissio...
BACKGROUND: In the HECKTOR 2022 challenge set [1], several state-of-the-art (SOTA, achieving best performance) deep learning models were introduced fo...