Nuclear Medicine

Position estimation using neural networks in semi-monolithic PET detectors.

. The goal of this work is to experimentally compare the 3D spatial and energy resolution of a semi-monolithic detector suitable for total-body positron emission tomography (TB-PET) scanners using different surface crystal treatments and silicon photomultiplier (SiPM) models.. An array of 1 × 8 lutetium yttrium oxyorthosilicate (LYSO) slabs of 25.8 × 3.1 × 20 mmseparated with Enhanced Specular Ref...

A multidomain fusion model of radiomics and deep learning to discriminate between PDAC and AIP based on F-FDG PET/CT images.

PURPOSE: To explore a multidomain fusion model of radiomics and deep learning features based on F-fluorodeoxyglucose positron emission tomography/computed tomography (F-FDG PET/CT) images to distinguish pancreatic ductal adenocarcinoma (PDAC) and autoimmune pancreatitis (AIP), which could effectively improve the accuracy of diseases diagnosis.

Multi-center, multi-vendor validation of deep learning-based attenuation correction in SPECT MPI: data from the international flurpiridaz-301 trial.

PURPOSE: Although SPECT myocardial perfusion imaging (MPI) is susceptible to artifacts from soft tissue attenuation, most scans are performed without ...

Deep learning-based dynamic PET parametric K image generation from lung static PET.

OBJECTIVES: PET/CT is a first-line tool for the diagnosis of lung cancer. The accuracy of quantification may suffer from various factors throughout th...

AI-based classification algorithms in SPECT myocardial perfusion imaging for cardiovascular diagnosis: a review.

In the last few years, deep learning has made a breakthrough and established its position in machine learning classification problems in medical image...

Multi-stage classification of Alzheimer's disease from F-FDG-PET images using deep learning techniques.

The study aims to implement a convolutional neural network framework that uses the 18F-FDG PET modality of brain imaging to detect multiple stages of ...

Opportunistic deep learning powered calcium scoring in oncologic patients with very high coronary artery calcium (≥ 1000) undergoing 18F-FDG PET/CT.

Our aim was to identify and quantify high coronary artery calcium (CAC) with deep learning (DL)-powered CAC scoring (CACS) in oncological patients wit...

Deep learning signature of brain [F]FDG PET associated with cognitive outcome of rapid eye movement sleep behavior disorder.

An objective biomarker to predict the outcome of isolated rapid eye movement sleep behavior disorder (iRBD) is crucial for the management. This study ...

Fluorescence-guided extended pelvic lymphadenectomy during robotic radical prostatectomy.

We evaluated and described the impact of prostatic indocyanine green (ICG) injection on extended pelvic lymph node (LN) dissection (ePLND) in robotic-...

Strategies to Implement Pet Robots in Long-Term Care Facilities for Dementia Care: A Modified Delphi Study.

OBJECTIVES: Pet robots are technology-based substitutes for live animals that have demonstrated psychosocial benefits for people living with dementia ...

Deep learning for myocardial ischemia auxiliary diagnosis using CZT SPECT myocardial perfusion imaging.

BACKGROUND: The World Health Organization reported that cardiovascular disease is the most common cause of death worldwide. On average, one person die...

Prediction Model of Residual Neural Network for Pathological Confirmed Lymph Node Metastasis of Ovarian Cancer.

PURPOSE: We want to develop a model for predicting lymph node status based on positron emission computed tomography (PET) images of untreated ovarian ...

Deep Learning Coronary Artery Calcium Scores from SPECT/CT Attenuation Maps Improve Prediction of Major Adverse Cardiac Events.

Low-dose ungated CT attenuation correction (CTAC) scans are commonly obtained with SPECT/CT myocardial perfusion imaging. Despite the characteristical...

Using domain knowledge for robust and generalizable deep learning-based CT-free PET attenuation and scatter correction.

Despite the potential of deep learning (DL)-based methods in substituting CT-based PET attenuation and scatter correction for CT-free PET imaging, a c...

Mitigating bias in deep learning for diagnosis of coronary artery disease from myocardial perfusion SPECT images.

PURPOSE: Artificial intelligence (AI) has high diagnostic accuracy for coronary artery disease (CAD) from myocardial perfusion imaging (MPI). However,...

Development of a deep learning network for Alzheimer's disease classification with evaluation of imaging modality and longitudinal data.

. Neuroimaging uncovers important information about disease in the brain. Yet in Alzheimer's disease (AD), there remains a clear clinical need for rel...

DuDoSS: Deep-learning-based dual-domain sinogram synthesis from sparsely sampled projections of cardiac SPECT.

PURPOSE: Myocardial perfusion imaging (MPI) using single-photon emission-computed tomography (SPECT) is widely applied for the diagnosis of cardiovasc...

Exploring behaviours perceived as important for human-Dog bonding and their translation to a robotic platform.

To facilitate long-term engagement with social robots, emerging evidence suggests that modelling robots on social animals with whom many people form e...

Breast PET/MRI Hybrid Imaging and Targeted Tracers.

The recent introduction of hybrid positron emission tomography/magnetic resonance imaging (PET/MRI) as a promising imaging modality for breast cancer ...

Deep Learning-Based Attenuation Correction Improves Diagnostic Accuracy of Cardiac SPECT.

To improve diagnostic accuracy, myocardial perfusion imaging (MPI) SPECT studies can use CT-based attenuation correction (AC). However, CT-based AC is...