Nuclear Medicine

Artificial Intelligence for PET and SPECT Image Enhancement.

Nuclear medicine imaging modalities such as PET and SPECT are confounded by high noise levels and low spatial resolution, necessitating postreconstruction image enhancement to improve their quality and quantitative accuracy. Artificial intelligence (AI) models such as convolutional neural networks, U-Nets, and generative adversarial networks have shown promising outcomes in enhancing PET and SPECT...

Deep Generalized Learning Model for PET Image Reconstruction.

Low-count positron emission tomography (PET) imaging is challenging because of the ill-posedness of this inverse problem. Previous studies have demonstrated that deep learning (DL) holds promise for achieving improved low-count PET image quality. However, almost all data-driven DL methods suffer from fine structure degradation and blurring effects after denoising. Incorporating DL into the traditi...

Deep learning approach using SPECT-to-PET translation for attenuation correction in CT-less myocardial perfusion SPECT imaging.

OBJECTIVE: Deep learning approaches have attracted attention for improving the scoring accuracy in computed tomography-less single photon emission com...

Exploring the infiltrative and degradative ability of Fusarium oxysporum on polyethylene terephthalate (PET) using correlative microscopy and deep learning.

Managing the worldwide steady increase in the production of plastic while mitigating the Earth's global pollution is one of the greatest challenges no...

Automated identification of uncertain cases in deep learning-based classification of dopamine transporter SPECT to improve clinical utility and acceptance.

PURPOSE: Deep convolutional neural networks (CNN) are promising for automatic classification of dopamine transporter (DAT)-SPECT images. Reporting the...

Deep learning for [F]fluorodeoxyglucose-PET-CT classification in patients with lymphoma: a dual-centre retrospective analysis.

BACKGROUND: The rising global cancer burden has led to an increasing demand for imaging tests such as [F]fluorodeoxyglucose ([F]FDG)-PET-CT. To aid im...

Editorial: New trends in single photon emission computed tomography (SPECT).

Antibacterial, Resistance Modulation, Anti-Biofilm Formation, and Efflux Pump Inhibition Properties of (Willd.) N. Halle (Celastraceae) Stem Extract and Its Constituents.

This study investigated the antibacterial, resistance modulation, biofilm inhibition, and efflux pump inhibition potentials of stem extract and its c...

Ultra-fast whole-body bone tomoscintigraphies achieved with a high-sensitivity 360° CZT camera and a dedicated deep-learning noise reduction algorithm.

UNLABELLED: This study aimed to determine whether the whole-body bone Single Photon Emission Computed Tomography (SPECT) recording times of around 10 ...

Deep learning based synthesis of MRI, CT and PET: Review and analysis.

Medical image synthesis represents a critical area of research in clinical decision-making, aiming to overcome the challenges associated with acquirin...

Deep learning-based attenuation correction method in Tc-GSA SPECT/CT hepatic imaging: a phantom study.

This study aimed to evaluate a deep learning-based attenuation correction (AC) method to generate pseudo-computed tomography (CT) images from non-AC s...

Circulating tumor DNA for monitoring classic Hodgkin lymphoma patients: Correlation with FDG-PET/CT.

The value of circulating tumor DNA (ctDNA) as a biomarker of disease activity in classic Hodgkin lymphoma (cHL) patients has not yet been well establi...

Recent Advances and Challenges in Enzymatic Depolymerization and Recycling of PET Wastes.

Poly (ethylene terephthalate) (PET) is one of the most commonly used plastics in daily life and various industries. Enzymatic depolymerization and rec...

Artificial Intelligence and Deep Learning for Advancing PET Image Reconstruction: State-of-the-Art and Future Directions.

Positron emission tomography (PET) is vital for diagnosing diseases and monitoring treatments. Conventional image reconstruction (IR) techniques like ...

Deep learning-based outcome prediction using PET/CT and automatically predicted probability maps of primary tumor in patients with oropharyngeal cancer.

BACKGROUND AND OBJECTIVE: Recently, deep learning (DL) algorithms showed to be promising in predicting outcomes such as distant metastasis-free surviv...

Utilizing deep learning techniques to improve image quality and noise reduction in preclinical low-dose PET images in the sinogram domain.

BACKGROUND: Low-dose positron emission tomography (LD-PET) imaging is commonly employed in preclinical research to minimize radiation exposure to anim...

Feasibility and limitations of deep learning-based coronary calcium scoring in PET-CT: a comparison with coronary calcium score CT.

OBJECTIVE: This study aimed to determine the feasibility and limitations of deep learning-based coronary calcium scoring using positron emission tomog...

The Progressive Veterinary Practice.

Veterinary practices must be forward-thinking to effectively serve today's pet owners and move into the future. The progressive practice considers eve...

Predictive value and potential association of PET/CT radiomics on lymph node metastasis of cervical cancer.

OBJECTIVE: Due to the information-rich nature of positron emission tomography/computed tomography (PET/CT) images, the authors hope to explore radiomi...

Individualized Assessment of Brain Aβ Deposition With fMRI Using Deep Learning.

PET-based Alzheimer's disease (AD) assessment has many limitations in large-scale screening. Non-invasive techniques such as resting-state functional ...