Latest AI and machine learning research in nuclear medicine for healthcare professionals.
BACKGROUND: This study aimed to evaluate the predictive model based on multi-dual-energy computed tomography (DECT) imaging parameters and model integrating DECT parameters with intra- and peritumoral- radiomics and deep learning model using a 2.5D Vision Transformer (ViT) for the preoperative prediction of pathological grading in non-muscle-invasive bladder cancer (NMIBC). MATERIALS AND METHODS: ...
Alzheimer's disease (AD) is a progressive neurodegenerative disorder, with mild cognitive impairment (MCI) as its prodromal stage. Accurate MCI conversion prediction is critical for early intervention and resource allocation. Recently, deep learning-based multimodal neuroimaging fusion has become a hot research topic in AI-assisted AD diagnosis. Existing multimodal fusion approaches are limited by...
To develop a deep learning-based automated trunk muscle volumetry method using whole-body CT from PET/CT and evaluate its performance against bioelect...
Objective.Artificial intelligence methods for denoising low-count FDG PET brain images are usually evaluated using image quality metrics alone, with l...
BACKGROUND: Coronary computed tomography angiography (CCTA) and positron emission tomography/computed tomography (PET/CT) myocardial perfusion imaging...
Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by the pathological misfolding and aggregation of α-synuclein (α-sy...
To address the challenges of model instability and limited generalizability in radiomics-based lung cancer prognosis, we developed a robust multiparam...
BACKGROUND: Positron emission tomography with magnetic resonance imaging (PET/MRI) provides noninvasive molecular characterization of breast cancer an...
PURPOSE: PET images often make small lesions difficult to identify because of noise and system blur. We address this by developing and evaluating MLPE...
BACKGROUND: PET/MR combines molecular and functional imaging but faces challenges such as prolonged scans, noise from reduced tracer activity, and sub...
PURPOSE: Deep learning (DL) has shown promise in enabling attenuation correction (AC) for SPECT myocardial perfusion imaging (MPI) without relying on ...
The diagnostic image quality of positron emission tomography (PET) acquisitions strongly depends on the administered radiotracer activity and acquisit...
BACKGROUND: Conventional single-energy CT (SECT) is limited in differentiating materials with similar Hounsfield units. Unlike previous reviews that m...
PURPOSE: Artificial intelligence (AI) is increasingly proposed as a solution to improve efficiency in radiology and nuclear medicine, particularly in ...
BACKGROUND: New long field-of-view (FOV) PET scanners using bismuth germanate (BGO) detectors without time-of-flight (TOF) capability are now availabl...
BACKGROUND: Fever of unknown origin (FUO) remains diagnostically challenging because of heterogeneous causes, non-specific clinical manifestations, an...
PURPOSE: To develop and validate a preoperative [18F]PSMA-1007 PET-derived deep learning score (DLS) and an integrated model combining DLS, D'Amico ri...
OBJECTIVE: The current BTS guidelines recommend evaluation of suspicious pulmonary nodules using [18F]FDG-PET/CT imaging, followed by Herder model ris...
BACKGROUND: The differentiation between benign and malignant persistent pulmonary ground-glass nodules (GGNs) remains challenging, and the relative va...
We describe a publicly available, large, annotated dataset of 597 whole-body Positron Emission Tomography/Computed Tomography (PET/CT) studies with Pr...