Nuclear Medicine

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 classification of medical images is becoming increasingly important in developing computer-aided diagnosis systems. Deep learning, particularly for the convolutional neural networks, has been widely applied to the classification of medical images. In order to re...

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 early acquired PET images, i.e. 15 min after [18F]-Florbetaben tracer injection. 47 subjects were included in the study: 13 patients with transthyretin-related amyloidosis cardiac amyloidosis (ATTR-CA), 15 patients with immunoglobulin light-chain amyloi...

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...

Deep learning-based T1-enhanced selection of linear attenuation coefficients (DL-TESLA) for PET/MR attenuation correction in dementia neuroimaging.

PURPOSE: The accuracy of existing PET/MR attenuation correction (AC) has been limited by a lack of correlation between MR signal and tissue electron d...

Transforming UTE-mDixon MR Abdomen-Pelvis Images Into CT by Jointly Leveraging Prior Knowledge and Partial Supervision.

Computed tomography (CT) provides information for diagnosis, PET attenuation correction (AC), and radiation treatment planning (RTP). Disadvantages of...

Sub-2 mm depth of interaction localization in PET detectors with prismatoid light guide arrays and single-ended readout using convolutional neural networks.

PURPOSE: Depth of interaction (DOI) readout in PET imaging has been researched in efforts to mitigate parallax error, which would enable the developme...

Automatic rat brain image segmentation using triple cascaded convolutional neural networks in a clinical PET/MR.

The purpose of this work was to develop and evaluate a deep learning approach for automatic rat brain image segmentation of magnetic resonance imaging...

Artificial intelligence enables whole-body positron emission tomography scans with minimal radiation exposure.

PURPOSE: To generate diagnostic F-FDG PET images of pediatric cancer patients from ultra-low-dose F-FDG PET input images, using a novel artificial int...

Diagnostic accuracy of stress-only myocardial perfusion SPECT improved by deep learning.

PURPOSE: Deep convolutional neural networks (CNN) for single photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) has been u...

Denoising non-steady state dynamic PET data using a feed-forward neural network.

The quality of reconstructed dynamic PET images, as well as the statistical reliability of the estimated pharmacokinetic parameters is often compromis...

Deep learning-assisted ultra-fast/low-dose whole-body PET/CT imaging.

PURPOSE: Tendency is to moderate the injected activity and/or reduce acquisition time in PET examinations to minimize potential radiation hazards and ...

A novel fast kilovoltage switching dual-energy CT with deep learning: Accuracy of CT number on virtual monochromatic imaging and iodine quantification.

PURPOSE: A novel fast kilovoltage switching dual-energy CT with deep learning [Deep learning based-spectral CT (DL-Spectral CT)], which generates a co...

A semantic database for integrated management of image and dosimetric data in low radiation dose research in medical imaging.

Medical ionizing radiation procedures and especially medical imaging are a non negligible source of exposure to patients. Whereas the biological effec...

Clinical value of machine learning-based interpretation of I-123 FP-CIT scans to detect Parkinson's disease: a two-center study.

PURPOSE: Our aim was to develop and validate a machine learning (ML)-based approach for interpretation of I-123 FP-CIT SPECT scans to discriminate Par...

A machine learning-based radiomics model for the prediction of axillary lymph-node metastasis in breast cancer.

OBJECTIVE: The aim of this study was to develop and validate machine learning-based radiomics model for predicting axillary lymph-node (ALN) metastasi...

4D deep image prior: dynamic PET image denoising using an unsupervised four-dimensional branch convolutional neural network.

Although convolutional neural networks (CNNs) demonstrate the superior performance in denoising positron emission tomography (PET) images, a supervise...

A deep learning framework for F-FDG PET imaging diagnosis in pediatric patients with temporal lobe epilepsy.

PURPOSE: Epilepsy is one of the most disabling neurological disorders, which affects all age groups and often results in severe consequences. Since mi...