Nuclear Medicine

Toward personalized iodine-131 therapy: A review of dosimetric strategies.

BACKGROUND: Iodine-131 (131I) therapy is a cornerstone of nuclear medicine for thyroid diseases and certain cancers. This review evaluates the transition from empirical to personalized dosimetry, focusing on image-based and AI-driven strategies. METHODS: Following PRISMA guidelines, we analyzed studies from 1980 to 2025 across PubMed, Scopus, and other databases. We examine the core dosimetric fra...

Dose reduction for synaptic density PET imaging in Parkinson's disease.

OBJECTIVE: Dynamic PET imaging with 11C-UCB-J enables in vivo quantification of synaptic vesicle glycoprotein 2A (SV2A), with prior reports of lower synaptic density in areas such as the brainstem nuclei and substantia nigra (SN) in Parkinson's disease (PD). Lowering PET dose reduces radiation exposure but increases noise and compromises quantification. This study evaluated a self-supervised two-s...

PETWB-REP: A Multi-Cancer Whole-Body FDG PET/CT Dataset with Corresponding Radiology Reports.

Publicly available, large-scale medical imaging datasets are crucial for developing and validating artificial intelligence (AI) models and conducting ...

Long Axial Field-of-View (LAFOV) PET in the Era of Multi-Parametric Imaging and Theranostics.

This review explores the revolutionary impact of long axial field-of-view (LAFOV) PET/CT imaging in modern nuclear medicine and molecular imaging. LAF...

Photon-counting CT-enabled urolithiasis phenotyping: virtual non-iodine reconstructions, automated measurement, and spectral radiomics.

PURPOSE OF REVIEW: Urolithiasis management increasingly depends on accurate, noninvasive stone phenotyping to guide acute intervention, secondary prev...

SwinPix: A Swin Transformer-based Pix2Pix Framework for Low-Dose PET Denoising Using Multi-level Inputs Toward Standard-Dose Quality.

This study introduces SwinPix, a novel network architecture designed to explore the effectiveness of multi-level low-dose (LD) PET inputs as prior kno...

Accurate CT-free correction of attenuation, scatter, and aperture effects using deep learning in dedicated cardiac pinhole SPECT.

Recent advances and current landscape of software tools for image analysis and dosimetry in nuclear medicine.

Recent advancements in nuclear medicine, particularly in personalised radiopharmaceutical therapy, have emphasised the growing need for precise assess...

Exploring feature importance in machine learning for neuroimaging traits in Alzheimer's disease across a multiethnic cohort.

BackgroundAlzheimer's disease (AD) affects 55 million people worldwide, projected to reach 139 million by 2050; yet, most machine learning (ML)-based ...

Validation of an AI Method for Automated Lymphoma Metabolic Tumor Volume Segmentation Using a Public Benchmark PET/CT Dataset.

The aim of this study was to evaluate the performance of an artificial intelligence (AI)-based method for automated segmentation of total metabolic tu...

A universal machine learning model for the electronic density of states.

In the last few years several "universal" interatomic potentials have appeared, using machine-learning approaches to predict energy and forces of atom...

Impact of Deep Learning-Based Time-of-Flight PET Images of Small Tumors Using a Human Anatomic Phantom.

Time-of-flight (ToF) in PET improves image quality by enhancing the signal-to-noise ratio, and recent deep learning (DL)-based ToF (DL-ToF) methods fu...

MRI In Vivo Detection of Amyloid-β Protein Deposition in Different Brain Regions of Patients with AD and MCI.

To investigate a non-invasive magnetic resonance imaging (MRI)-based method for detecting amyloid-β (Aβ) protein deposition in different brain regions...

Minimum Clinically Achievable Dose for Detecting Liver Lesions Using Deep Learning Image Reconstruction: A Phantom and Patient Study.

RATIONALE AND OBJECTIVES: To investigate the performance of deep learning image reconstruction (DLIR) at an ultra-low dose of approximately 4.5 mGy fo...

18F-FDG PET/CT and receptor-positive circulating tumor cells-based machine learning model for predicting poorly differentiated lung adenocarcinoma.

INTRODUCTION: This study aimed to develop and validate a machine learning model that integrates radiomic features from 2-[18F]fluoro-2-deoxy-D-glucose...

Vessel-specific perivascular fat attenuation index derived from AI-CCTA and its association with impaired coronary flow reserve in patients with INOCA.

OBJECTIVES: To investigate the association between artificial intelligence (AI)-derived coronary computed tomography angiography (CCTA) features and i...

Advances and challenges of PET imaging in bladder cancer-An update and future trends.

Molecular imaging with positron emission tomography (PET) is a powerful tool in the clinical management of bladder cancer, providing functional inform...

AutoPET Challenge on Fully Automated Lesion Segmentation in Oncologic PET/CT Imaging, Part 2: Domain Generalization.

This article reports the results of the second iteration of the autoPET challenge on automated lesion segmentation in whole-body PET/CT, held in conju...

Simultaneous synthesis of perfusion and ventilation images from CT using a dual-decoder residual attention network for lung disease diagnosis.

BACKGROUND: Deep learning algorithms can synthesize pulmonary functional images from CT images. However, previous studies have only been able to predi...

Filling of incomplete sinograms from sparse PET detector configurations using a residual U-Net.

BACKGROUND: Long axial field-of-view PET scanners are becoming increasingly available worldwide for clinical and research nuclear medicine examination...