Nuclear Medicine

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 modern clinical practice, particularly in the context of Radiotherapy (RT) treatment planning. The use of sCT enables the calculation of doses, pushing towards Magnetic Resonance Imaging (MRI) guided radiotherapy treatments. Moreover, with the introduction of MRI-Positron Emission Tomography (PET) hybr...

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 of multiple radiotracer signals, thus enhancing diagnostic capabilities, reducing scan times, and improving patient comfort. Traditional methods often require significant delays between tracer injections, leading to physiological changes and noise interference. Recent advancements, including multi-tr...

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 disease (AD) in people with dementia. Although various...

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 as inflammation and microcalcification. Among ava...

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 correction. MRI-guided attenuation correction in PET...

Cascaded 3D Diffusion Models for Whole-body 3D 18-F FDG PET/CT synthesis from Demographics

We propose a cascaded 3D diffusion model framework to synthesize high-fidelity 3D PET/CT volumes directly from demographic variables, addressing the...

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-body bone scintigraphy (WBBS) is frequently used in...

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, hilar, and distal cholangiocarcinoma, as well as gal...

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 inform patient care. Most models, however, lack uncert...

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 tomography (18F-FDG PET-CT) is a multi-modality m...

Machine Learning Derived Blood Input for Dynamic PET Images of Rat Heart

Dynamic FDG PET imaging study of n = 52 rats including 26 control Wistar-Kyoto (WKY) rats and 26 experimental spontaneously hypertensive rats (SHR) ...

A Comprehensive Review of Techniques, Algorithms, Advancements, Challenges, and Clinical Applications of Multi-modal Medical Image Fusion for Improved Diagnosis

Multi-modal medical image fusion (MMIF) is increasingly recognized as an essential technique for enhancing diagnostic precision and facilitating eff...

Exploiting network optimization stability for enhanced PET image denoising using deep image prior.

. Positron emission tomography (PET) is affected by statistical noise due to constraints on tracer dose and scan duration, impacting both diagnostic p...

Deep Learning-Derived Cardiac Chamber Volumes and Mass From PET/CT Attenuation Scans: Associations With Myocardial Flow Reserve and Heart Failure.

BACKGROUND: Computed tomography (CT) attenuation correction scans are an intrinsic part of positron emission tomography (PET) myocardial perfusion ima...

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 productive over the past few years in tackling ...

Baseline [F]FP-CIT PET-based deep learning prediction of levodopa-induced dyskinesia in Parkinson's disease.

We aimed to develop a convolutional neural network (CNN) model with multi-task learning to predict the onset of levodopa-induced dyskinesia (LID) in p...

Identification of HER2-over-expression, HER2-low-expression, and HER2-zero-expression statuses in breast cancer based on F-FDG PET/CT radiomics.

PURPOSE: According to the updated classification system, human epidermal growth factor receptor 2 (HER2) expression statuses are divided into the foll...

Learning-based multi-material CBCT image reconstruction with ultra-slow kV switching.

ObjectiveThe purpose of this study is to perform multiple () material decomposition with deep learning method for spectral cone-beam CT (CBCT) imaging...

Tuning Butterworth filter's parameters in SPECT reconstructions via kernel-based Bayesian optimization with a no-reference image evaluation metric

In Single Photon Emission Computed Tomography (SPECT), the image reconstruction process involves many tunable parameters that have a significant imp...

Deep learning for Parkinson's disease classification using multimodal and multi-sequences PET/MR images.

BACKGROUND: We aimed to use deep learning (DL) techniques to accurately differentiate Parkinson's disease (PD) from multiple system atrophy (MSA), whi...