Nuclear Medicine

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 density. Based on our finding that longitudinal relaxation rate, or R , is associated with CT Hounsfield unit in bone and soft tissues in the brain, we propose a deep learning T -enhanced selection of linear attenuation coefficients (DL-TESLA) method ...

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 CT include poor soft tissue contrast and exposure to ionizing radiation. While MRI can overcome these disadvantages, it lacks the photon absorption information needed for PET AC and RTP. Thus, an intelligent transformation from MR to CT, i.e., the M...

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...

Recent advances in medical image processing for the evaluation of chronic kidney disease.

Assessment of renal function and structure accurately remains essential in the diagnosis and prognosis of Chronic Kidney Disease (CKD). Advanced imagi...

True ultra-low-dose amyloid PET/MRI enhanced with deep learning for clinical interpretation.

PURPOSE: While sampled or short-frame realizations have shown the potential power of deep learning to reduce radiation dose for PET images, evidence i...

Improved amyloid burden quantification with nonspecific estimates using deep learning.

PURPOSE: Standardized uptake value ratio (SUVr) used to quantify amyloid-β burden from amyloid-PET scans can be biased by variations in the tracer's n...

Deep learning detection of informative features in tau PET for Alzheimer's disease classification.

BACKGROUND: Alzheimer's disease (AD) is the most common type of dementia, typically characterized by memory loss followed by progressive cognitive dec...

Comparison of deep learning synthesis of synthetic CTs using clinical MRI inputs.

There has been substantial interest in developing techniques for synthesizing CT-like images from MRI inputs, with important applications in simultane...

A physics-guided modular deep-learning based automated framework for tumor segmentation in PET.

An important need exists for reliable positron emission tomography (PET) tumor-segmentation methods for tasks such as PET-based radiation-therapy plan...