Nuclear Medicine

Alzheimer's disease diagnosis support for brain perfusion SPECT scans in a real-world clinical cohort.

BackgroundDementia diagnosis is challenging and often delayed. Brain imaging techniques such as single-photon emission computed tomography (SPECT) imaging can help identify subtle changes in brain perfusion. Artificial intelligence methods may support results interpretation for early diagnosis.ObjectiveTo develop and validate multivariate models for the early diagnosis of Alzheimer's disease (AD),...

Group equivariant pyramid network for respiratory motion correction on PET image.

INTRODUCTION: Respiratory-induced artefacts cause blurring in PET images, impairing lesion identification and diagnosis. Existing correction methods exhibit limited feature learning capabilities, making it challenging to effectively eliminate artefacts. METHODS: The authors developed an SE(3)-equivariant convolution for 3D PET medical images. A Group Equivariant Dual-Pyramid Network (GEPN) was con...

Multi-omics deep learning improves FDG PET-CT-based long-term prognostication of breast cancer.

[18F] fluorodeoxyglucose positron emission tomography - computed tomography (FDG PET-CT) is increasingly used for staging of breast cancer in the prim...

Handcrafted vs. Deep Radiomics vs. Fusion vs. Deep Learning: A Comprehensive Review of Machine Learning -Based Cancer Outcome Prediction in PET and SPECT Imaging.

Machine learning (ML), particularly deep learning (DL) and radiomics-based approaches, has emerged as a powerful tool for cancer outcome prediction us...

Development and Validation of a Machine Learning Model for Dementia Staging in a Heterogeneous Cognitive Impairment Cohort Using Multimodal Features.

RATIONALE AND OBJECTIVES: With the emergence of disease-modifying therapies, precise staging of dementia is urgent. This study aimed to develop a mach...

18F-FDG PET radiomics model for predicting TARE response in patients with colorectal cancer liver metastases.

PURPOSE: Predicting treatment response in patients with colorectal cancer liver metastases (CRCLM) who have undergone transarterial radioembolization ...

Generative deep learning synthesizes high signal-to-noise ratio sensitivity maps for PET from low count direct normalization data.

An accurate and precise normalization procedure is essential to correct for variations in detector efficiency in reconstructed positron emission t...

Image-space compensation of inter-crystal scattering in PET using a neural network based filter.

BACKGROUND: In positron emission tomography (PET), gamma photons arriving at the detector ring may undergo one or more Compton scattering events, pote...

Lung scintigraphy for the diagnosis of acute pulmonary embolism.

Pulmonary embolism (PE) remains a major diagnostic challenge due to its potentially life-threatening nature and the clinical burden associated with an...

Optimizing toward Discovery: AI-Driven Exploration of Lewis Acid-Base Catalysts for PET Glycolysis.

The depolymerization of polyethylene terephthalate (PET) through efficient chemical recycling remains a central challenge in plastic waste valorizatio...

Semi-supervised learning for dose prediction in targeted radionuclide therapy: a synthetic data study.

Objective.Accurate and personalized radiation dose estimation is crucial for effective targeted radionuclide therapy (TRT). Deep learning (DL) holds p...

Ultrahigh-Linearity Triboelectric Nanogenerator for Dual Pressure-Frequency Sensing and Multi-Modal Recognition.

Triboelectric nanogenerators (TENGs) hold great promise as self-powered sensors, but their practical applications are limited by charge leakage at hig...

[Diffusion cycle-consistent generative adversarial networks for pelvic active bone marrow segmentation].

OBJECTIVES: To establish a pelvic active bone marrow (ABM) segmentation method based on diffusion cycle-consistent generative adversarial networks for...

Artificial intelligence in lung scintigraphy.

Lung scintigraphy is a common nuclear medicine procedure for evaluating pulmonary perfusion and ventilation, particularly in the diagnosis of pulmonar...

Multimodal Fusion and Transfer Learning for the Detection of Degenerative Parkinsonisms with Dopamine Transporter SPECT Imaging.

Dopamine transporter (DAT) SPECT is a validated biomarker for Parkinson's disease (PD) and related degenerative parkinsonisms. Interpretation relies o...

Clinical validation of a unified data-driven respiratory motion correction technique in 18F-FDG PET/CT imaging of upper abdominal lesions: a real-world study.

PURPOSE: Respiratory motion (RM)-related artifacts significantly impact image quality and diagnostic accuracy in PET/CT imaging. This study aimed to p...

SMGDiff: step mapping generalized diffusion model for efficient noise reduction in cardiac-gated myocardial perfusion SPECT images.

BACKGROUND: Improving the image quality of cardiac gating myocardial perfusion single-photon emission computed tomography (CG MP-SPECT) is crucial for...

Biomarker discovery in Lennox-Gastaut syndrome: Advances and challenges in electrophysiological, genetic, neuroimaging, and neuroinflammatory approaches.

In Lennox-Gastaut Syndrome (LGS), a severe developmental and epileptic encephalopathy, the absence of validated biomarkers limits our ability to detec...

Multisequence MRI Enables High-Fidelity FDG-PET Synthesis for Epilepsy Using GANs.

BACKGROUND: FDG-PET aids presurgical epilepsy evaluation but is limited by access and radiation exposure. PURPOSE: To evaluate synthetic FDG-PET gener...

Sex Differences in Alzheimer's Disease: A Systematic Review of Two Decades of Neuroimaging Research.

OBJECTIVES: Given the heterogeneous nature of Alzheimer's Disease (AD) and its higher prevalence in females, it is crucial to understand sex-related d...