Nuclear Medicine

Prediction of impulse control disorders in Parkinson’s disease: a longitudinal machine learning study

Impulse control disorders (ICD) in Parkinson’s disease (PD) patients mainly occur as adverse effects of dopamine replacement therapy. Despite several known risk factors associated with ICD development, this cannot yet be accurately predicted at PD diagnosis. We aimed to investigate the predictability of incident ICD by baseline measures of demographic, clinical, dopamine transporter single photon ...

Incremental diagnostic value of AI-derived coronary artery calcium in 18F-flurpiridaz PET Myocardial Perfusion Imaging

Positron Emission Tomography (PET) myocardial perfusion imaging (MPI) is a powerful tool for predicting coronary artery disease (CAD). Coronary artery calcium (CAC) provides incremental risk stratification to PET-MPI and enhances diagnostic accuracy. We assessed additive value of CAC score, derived from PET/CT attenuation maps to stress TPD results using the novel 18F-flurpiridaz tracer in detecti...

Multicenter Evaluation of Interpretable AI for Coronary Artery Disease Diagnosis from PET Biomarkers

Positron emission tomography (PET)/CT for myocardial perfusion imaging (MPI) provides multiple imaging biomarkers, often evaluated separately. We deve...

Dynamic frame-by-frame motion correction for 18F-flurpiridaz PET-MPI using convolution neural network

Precise quantification of myocardial blood flow (MBF) and flow reserve (MFR) in 18F-flurpiridaz PET significantly relies on motion correction (MC). Ho...

Multimodal Integration of Alzheimer’s Plasma Biomarkers, MRI, and Genetic Risk for Individual Prediction of Cerebral Amyloid Burden

Alzheimer’s disease (AD), the most prevalent neurodegenerative disorder, is marked by the accumulation of amyloid-β (Aβ) plaques. Although cerebral Aβ...

AI-based Hepatic Steatosis Detection and Integrated Hepatic Assessment from Cardiac CT Attenuation Scans Enhances All-cause Mortality Risk Stratification: A Multi-center Study

Hepatic steatosis (HS) is a common cardiometabolic risk factor frequently present but under-diagnosed in patients with suspected or known coronary art...

The REgistry of Flow and Perfusion Imaging for Artificial INtelligEnce with PET (REFINE PET): Rationale and Design

The REgistry of Flow and Perfusion Imaging for Artificial INtelligEnce with PET (REFINE PET) was established to aggregate PET and associated computed ...

A multi-modal deep learning framework for predicting PSA progression-free survival in metastatic prostate cancer using PSMA PET/CT imaging

PSMA PET/CT imaging has been increasingly utilized in the management of patients with metastatic prostate cancer (mPCa). Imaging biomarkers derived fr...

Equivariant Spatiotemporal Transformers with MDL-Guided Feature Selection for Malignancy Detection in Dynamic PET

Dynamic Positron Emission Tomography (PET) scans offer rich spatiotemporal data for detecting malignancies, but their high-dimensionality and noise po...

Using deep learning methods to shorten acquisition time in children’s renal cortical imaging

This study evaluates the capability of diffusion-based generative models to reconstruct diagnostic-quality renal cortical images from reduced-acquisit...

Integrating Imaging-Derived Clinical Endotypes with Plasma Proteomics and External Polygenic Risk Scores Enhances Coronary Microvascular Disease Risk Prediction†

Coronary microvascular disease (CMVD) is an underdiagnosed but significant contributor to the burden of ischemic heart disease, characterized by angin...

Artificial Intelligence in Cardiac Amyloidosis: A Systematic Review and Meta-Analysis of Diagnostic Accuracy Across Imaging and Non-Imaging Modalities

Cardiac amyloidosis (CA) is an underdiagnosed infiltrative cardiomyopathy associated with poor outcomes if not detected early. Artificial intelligence...

Combined High-Resolution MRSI and [18F]-FACBC PET to Improve the Presurgical Diagnostic Accuracy in Gliomas

Medical imaging is crucial for glioma management. Combined with MRI, amino acid PET may improve glioma diagnosis, biopsy targeting, and tumor delineat...

Cerebrospinal fluid proteomics for predictive assessment of Alzheimer’s Disease risk

Alzheimer’s disease (AD) involves early molecular changes beyond amyloid-β (Aβ) and tau, that create heterogeneous disease biology, giving rise to var...

Morphological and Functional Alterations in Type 2 Diabetes Pancreata assessed with MRI-based metrics and [18F]FP-(+)-DTBZ PET

To determine if combining PET-derived beta-cell mass (BCM) estimates with MRI- based morphology metrics improves the prediction of beta-cell functiona...

A foundation transformer model with self-supervised learning for ECG-based assessment of cardiac and coronary function

The wide availability of labeled electrocardiogram (ECG) data has driven major advances in artificial intelligence (AI)-based detection of structural ...

The Brain Imaging and Neurophysiology Database: BINDing multimodal neural data into a large-scale repository

The Brain Imaging and Neurophysiology Database (BIND) represents one of the largest multi-institutional, multimodal, clinical neuroimaging repositorie...

A unified deep learning framework for cross-platform harmonization of multi-tracer PET quantification

Quantitative PET underpins diagnosis and treatment monitoring in neurodegenerative disease, yet systematic biases between PET-MRI and PET-CT preclude ...

Discriminating Inflammation from Malignancy with Short-Dynamic Patlak Parametric 18F-FDG PET/CT

Differentiating malignant from inflammatory uptake on 18F-FDG PET/CT remains a major diagnostic challenge, as standardized uptake value (SUV) lacks sp...

CSF Proteomics and Machine Learning Reveal Distinct Stages Across the Alzheimer’s Disease Continuum

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by heterogeneous pathophysiological changes that begin years before symptoms em...