Nuclear Medicine

Machine learning for predicting full-count FDG PET brain images from low-count acquisitions in suspected dementia: a clinical and quantitative evaluation.

Objective.Artificial intelligence methods for denoising low-count FDG PET brain images are usually evaluated using image quality metrics alone, with limited direct clinical assessment, particularly in suspected dementia. This study evaluated a machine-learning image quality transfer (IQT) method for predicting full-count FDG PET brain images from low-count acquisitions using both quantitative metr...

Incremental prognostic value of AI-based coronary CT angiography combined with PET/CT dynamic myocardial perfusion imaging in patients with suspected or confirmed coronary artery disease.

BACKGROUND: Coronary computed tomography angiography (CCTA) and positron emission tomography/computed tomography (PET/CT) myocardial perfusion imaging (MPI) provide complementary anatomical and functional information for coronary artery disease (CAD). However, the prognostic value of integrating CCTA-derived coronary imaging characteristics with PET-MPI parameters remains to be established. Theref...

α-Synuclein Seeds Amplification Assays for Parkinson's Disease and Related Synucleinopathies.

Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by the pathological misfolding and aggregation of α-synuclein (α-sy...

Multiparametric PET/CT Tensor Radiomics for Stability-Aware Machine Learning in Lung Cancer Survival Prediction.

To address the challenges of model instability and limited generalizability in radiomics-based lung cancer prognosis, we developed a robust multiparam...

Image optimization for low-dose 18F-FDG breast PET/MRI using deep learning: a pilot study.

BACKGROUND: Positron emission tomography with magnetic resonance imaging (PET/MRI) provides noninvasive molecular characterization of breast cancer an...

MLPET: a localized neural network approach for probabilistic post-reconstruction PET image analysis using informed priors.

PURPOSE: PET images often make small lesions difficult to identify because of noise and system blur. We address this by developing and evaluating MLPE...

Deep progressive learning reconstruction for fast and low-dose whole-body PET scans in an integrated PET/MR system.

BACKGROUND: PET/MR combines molecular and functional imaging but faces challenges such as prolonged scans, noise from reduced tracer activity, and sub...

Maintaining SUV accuracy in low-count PET with PETfectior: a deep learning denoising solution.

The diagnostic image quality of positron emission tomography (PET) acquisitions strongly depends on the administered radiotracer activity and acquisit...

Spectral CT in radiotherapy: physics, principles, clinical applications, and future outlooks.

BACKGROUND: Conventional single-energy CT (SECT) is limited in differentiating materials with similar Hounsfield units. Unlike previous reviews that m...

Examining explainable artificial intelligence in TNM staging with PET-CT: a user-centred observation study.

PURPOSE: Artificial intelligence (AI) is increasingly proposed as a solution to improve efficiency in radiology and nuclear medicine, particularly in ...

Evaluation of deep learning-based reconstruction models on non-TOF BGO PET/CT: impact of acquisition times and BSREM penalization factors on lesion detectability and SNR.

BACKGROUND: New long field-of-view (FOV) PET scanners using bismuth germanate (BGO) detectors without time-of-flight (TOF) capability are now availabl...

FUO-PETMamba: a pet maximum-intensity-projection-based artificial intelligence framework for aetiological classification of fever of unknown origin with multicentre validation.

BACKGROUND: Fever of unknown origin (FUO) remains diagnostically challenging because of heterogeneous causes, non-specific clinical manifestations, an...

Development and multicohort external validation of a preoperative [18F]PSMA-1007 PET-derived deep learning score and multimodal model for predicting biochemical recurrence-free survival after radical prostatectomy.

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...

Deep learning-based malignancy probability estimation of pulmonary nodules in PET/CT imaging.

OBJECTIVE: The current BTS guidelines recommend evaluation of suspicious pulmonary nodules using [18F]FDG-PET/CT imaging, followed by Herder model ris...

Comparison of radiomics handcrafted features and deep features in 18F-FDG PET/CT for differentiating benign and malignant persistent pulmonary ground-glass nodules.

BACKGROUND: The differentiation between benign and malignant persistent pulmonary ground-glass nodules (GGNs) remains challenging, and the relative va...

A Whole-Body PSMA-PET/CT dataset with manually annotated tumor lesions.

We describe a publicly available, large, annotated dataset of 597 whole-body Positron Emission Tomography/Computed Tomography (PET/CT) studies with Pr...

Data-scarce transfer learning fusion for positron emission tomography and computed tomography guided lung biopsy.

OBJECTIVES: We developed a transfer learning-based multimodal fusion deep learning model integrating positron emission tomography/computed tomography ...

Wasserstein Deep Convolutional GAN With Growth Optimizer for Multi-Modal Feature Extraction in Cardiovascular Diagnosis.

BACKGROUND: Cardiovascular disease (CVD) diagnosis using multimodal health care data remains a major challenge due to the heterogeneity of clinical an...

Future Directions for [18F]FDG PET in Central Nervous System Diseases.

[18F]FDG PET is entering a new phase shaped by changes in representation, validation, and clinical integration. Beyond regional interpretation, networ...

Diagnostic performance of machine learning models based on dual-phase 99mTc-MIBI SPECT/CT semiquantitative parameters for differentiating benign and malignant pulmonary nodules.

PURPOSE: To evaluate the diagnostic value of machine learning models based on dual-phase 99mTc-MIBI SPECT/CT semiquantitative parameters for different...