Nuclear Medicine

Assignment of the Biological Identity of Breast Masses in Contrast-enhanced Spectral Computed Tomography Examinations of Chest Carried Out in Clinical Routine Diagnostics.

BACKGROUND: Focal breast lesions are observed in up to 5.8% of CT examinations performed in female patients for a wide variety of indications not affecting the breast. To simplify and standardize the further procedure in the case of breast masses visualized by computed tomography (CT), an easy and robust diagnostic approach in assigning surely benign findings, uncertain findings and probably malig...

Image reconstruction and elongation artifact reduction for a dual-panel dedicated prostate PET scanner.

BACKGROUND: The development of PET scanners dedicated to high temporal and spatial resolution organ-specific imaging is an active research area, motivated by the need for cost reduction, improved lesion detectability and quantification in specific clinical scenarios, as well as by ongoing hardware and software innovations. PURPOSE: This study investigates and compares various image reconstruction ...

Scanner-integrated reconstruction versus post-processing deep learning for low-count 18F-FDG PET/CT: a comparative clinical evaluation.

OBJECTIVES: To compare two deep learning (DL) approaches for low-count PET/CT: deep progressive reconstruction (DPR), a scanner-integrated reconstruct...

A comparative review of red, green, and brown seaweed: Bioactive components, health effects, and machine learning approaches.

Edible seaweed subcategorized into Chlorophyta (green algae), Phaeophyceae (brown algae) and Rhodophyta (red algae) are considered sustainable functio...

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) ima...

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 e...

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...