Nuclear Medicine

The promise of artificial intelligence and deep learning in PET and SPECT imaging.

This review sets out to discuss the foremost applications of artificial intelligence (AI), particularly deep learning (DL) algorithms, in single-photon emission computed tomography (SPECT) and positron emission tomography (PET) imaging. To this end, the underlying limitations/challenges of these imaging modalities are briefly discussed followed by a description of AI-based solutions proposed to ad...

Preoperative prediction of regional lymph node metastasis of colorectal cancer based on F-FDG PET/CT and machine learning.

PURPOSE: To establish and validate a regional lymph node (LN) metastasis prediction model of colorectal cancer (CRC) based on F-FDG PET/CT and radiomic features using machine-learning methods.

Quantitative PET in the 2020s: a roadmap.

Positron emission tomography (PET) plays an increasingly important role in research and clinical applications, catalysed by remarkable technical advan...

Differentiating IDH status in human gliomas using machine learning and multiparametric MR/PET.

BACKGROUND: The purpose of this study was to develop a voxel-wise clustering method of multiparametric magnetic resonance imaging (MRI) and 3,4-dihydr...

Attenuation correction using deep learning for brain perfusion SPECT images.

OBJECTIVE: Non-uniform attenuation correction using computed tomography (CT) improves the image quality and quantification of single-photon emission c...

Improved motor outcome prediction in Parkinson's disease applying deep learning to DaTscan SPECT images.

PURPOSE: Dopamine transporter (DAT) SPECT imaging is routinely used in the diagnosis of Parkinson's disease (PD). Our previous efforts demonstrated th...

Automatic attenuation map estimation from SPECT data only for brain perfusion scans using convolutional neural networks.

In clinical brain SPECT, correction for photon attenuation in the patient is essential to obtain images which provide quantitative information on the ...

Restoration of amyloid PET images obtained with short-time data using a generative adversarial networks framework.

Our purpose in this study is to evaluate the clinical feasibility of deep-learning techniques for F-18 florbetaben (FBB) positron emission tomography ...

Direct Attenuation Correction Using Deep Learning for Cardiac SPECT: A Feasibility Study.

Dedicated cardiac SPECT scanners with cadmium-zinc-telluride cameras have shown capabilities for shortened scan times or reduced radiation doses, as w...

Contrast agent dose reduction in computed tomography with deep learning using a conditional generative adversarial network.

OBJECTIVES: To reduce the dose of intravenous iodine-based contrast media (ICM) in CT through virtual contrast-enhanced images using generative advers...

Low-dose PET image noise reduction using deep learning: application to cardiac viability FDG imaging in patients with ischemic heart disease.

INTRODUCTION: Cardiac [F]FDG-PET is widely used for viability testing in patients with chronic ischemic heart disease. Guidelines recommend injection ...

Weakly supervised deep learning for determining the prognostic value of F-FDG PET/CT in extranodal natural killer/T cell lymphoma, nasal type.

PURPOSE: To develop a weakly supervised deep learning (WSDL) method that could utilize incomplete/missing survival data to predict the prognosis of ex...

Modeling complex particles phase space with GAN for Monte Carlo SPECT simulations: a proof of concept.

A method is proposed to model by a generative adversarial network the distribution of particles exiting a patient during Monte Carlo simulation of emi...

Translating amyloid PET of different radiotracers by a deep generative model for interchangeability.

It is challenging to compare amyloid PET images obtained with different radiotracers. Here, we introduce a new approach to improve the interchangeabil...

Deep learning based automated diagnosis of bone metastases with SPECT thoracic bone images.

SPECT nuclear medicine imaging is widely used for treating, diagnosing, evaluating and preventing various serious diseases. The automated classificati...

Deep-learning-based cardiac amyloidosis classification from early acquired pet images.

The objective of the present work was to evaluate the potential of deep learning tools for characterizing the presence of cardiac amyloidosis from ear...

Deep learning-based metal artefact reduction in PET/CT imaging.

OBJECTIVES: The susceptibility of CT imaging to metallic objects gives rise to strong streak artefacts and skewed information about the attenuation me...

Deep learning-based auto-delineation of gross tumour volumes and involved nodes in PET/CT images of head and neck cancer patients.

PURPOSE: Identification and delineation of the gross tumour and malignant nodal volume (GTV) in medical images are vital in radiotherapy. We assessed ...

Reference evapotranspiration of Brazil modeled with machine learning techniques and remote sensing.

Reference evapotranspiration (ETo) is a fundamental parameter for hydrological studies and irrigation management. The Penman-Monteith method is the st...

Cerebral blood flow measurements with O-water PET using a non-invasive machine-learning-derived arterial input function.

Cerebral blood flow (CBF) can be measured with dynamic positron emission tomography (PET) of O-labeled water by using tracer kinetic modelling. Howeve...