Nuclear Medicine

An FDG-PET-Based Machine Learning Framework to Support Neurologic Decision-Making in Alzheimer Disease and Related Disorders.

BACKGROUND AND OBJECTIVES: Distinguishing neurodegenerative diseases is a challenging task requiring...

Photon-counting micro-CT scanner for deep learning-enabled small animal perfusion imaging.

In this work, we introduce a benchtop, turn-table photon-counting (PC) micro-computed tomography (CT...

A hybrid predictor-corrector network and spatiotemporal classifier method for noisy plant PET image classification.

. Plant Positron Emission Tomography (PET) is a new and efficient imaging technique which aims at pr...

Correction to "Machine learning prediction of tau-PET in Alzheimer's disease using plasma, MRI, and clinical data".

Correction to: Artificial intelligence-based, volumetric assessment of the bone marrow metabolic activity in [F]FDG PET/CT predicts survival in multiple myeloma.

Deep learning-quantified body composition from positron emission tomography/computed tomography and cardiovascular outcomes: a multicentre study.

BACKGROUND AND AIMS: Positron emission tomography (PET)/computed tomography (CT) myocardial perfusio...

Development of a Diagnostic Prediction Model for Post-Stroke Cognitive Impairment in Acute Large Vessel Occlusion Stroke Using Multimodal MRI and PET/CT: A Study Protocol.

OBJECTIVE: Stroke is a leading cause of morbidity and disability worldwide. Post-stroke cognitive im...

Radiomics of PET Using Neural Networks for Prediction of Alzheimer's Disease Diagnosis.

Positron emission tomography (PET) imaging technology is widely used for diagnosing Alzheimer's dise...

Differentiating Treatment-Resistant Depression With and Without Parkinsonism in the Elderly From a Psychiatric Perspective by Tc-TRODAT-1 SPECT Imaging.

OBJECTIVES: Late-life depression often overlaps with neurodegenerative diseases leading to diagnosti...

Generation of synthetic CT from MRI for MRI-based attenuation correction of brain PET images using radiomics and machine learning.

BACKGROUND: Accurate quantitative PET imaging in neurological studies requires proper attenuation co...

Deep learning-based triple-tracer brain PET scanning in a single session: A simulation study using clinical data.

OBJECTIVES: Multiplexed Positron Emission Tomography (PET) imaging allows simultaneous acquisition o...

FedSynthCT-Brain: A federated learning framework for multi-institutional brain MRI-to-CT synthesis.

The generation of Synthetic Computed Tomography (sCT) images has become a pivotal methodology in mod...

A CT-free deep-learning-based attenuation and scatter correction for copper-64 PET in different time-point scans.

This study aimed to develop and evaluate a deep-learning model for attenuation and scatter correctio...

Explainable PET-based intratumoral and peritumoral machine learning model for predicting visceral pleural invasion in clinical-stage IA non-small cell lung cancer: A two-center study.

AIM: The aim of this study was to develop a PET-based machine learning model for predicting visceral...

GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models.

BACKGROUND AND OBJECTIVE: Research in the cross-modal medical image translation domain has been very...

PET imaging of atherosclerosis: artificial intelligence applications and recent advancements.

PET imaging has become a valuable tool for assessing atherosclerosis by targeting key processes such...

Machine learning positioning algorithms for long semi-monolithic scintillator PET detectors.

In this work, machine learning positioning algorithms are developed to improve the spatial resolutio...

Machine learning based differential diagnosis of SAPHO syndrome and secondary bone tumors using whole body bone scintigraphy.

SAPHO syndrome is an inflammatory disorder with bone and cutaneous manifestations, for which whole-b...

Progress in the application of fludeoxyglucose positron emission tomography computed tomography in biliary tract cancer.

Biliary tract cancer (BTC) is a group of heterogeneous sporadic diseases, including intrahepatic, hi...

Uncertainty quantification for deep learning-based metastatic lesion segmentation on whole body PET/CT.

Deep learning models are increasingly being implemented for automated medical image analysis to info...

Whole-body CT-to-PET synthesis using a customized transformer-enhanced GAN.

. Positron emission tomography with 2-deoxy-2-[fluorine-18]fluoro-D-glucose integrated with computed...