Nuclear Medicine

Feature Integration of FDG PET Brain Imaging Using Deep Learning for Sensitive Cognitive Decline Detection

Background Distinguishing individuals with cognitive decline (CD), including early Alzheimers diseas...

Transparency-First Medical Language Models: Datasheets, Model Cards, and End-to-End Data Provenance for Clinical NLP

We introduce TeMLM, a set of transparency-first release artifacts for clinical language models. TeML...

MlPET: A Localized Neural Network Approach for Probabilistic Post-Reconstruction PET Image Analysis Using Informed Priors

We develop and evaluate MlPET, a fast localized machine learning approach for probabilistic PET imag...

A Generalist Foundation Model for Total-body PET/CT Enables Diagnostic Reporting and System-wide Metabolic Profiling

Total-body PET/CT enables system-wide molecular imaging, but heterogeneous anatomical and metabolic ...

A myocardial reorientation method based on feature point detection for quantitative analysis of PET myocardial perfusion imaging.

OBJECTIVE: Reorienting cardiac positron emission tomography (PET) images to the transaxial plane is ...

Ensemble of Weak Spectral Total Variation Learners: a PET-CT Case Study

Solving computer vision problems through machine learning, one often encounters lack of sufficient...

DpDNet: An Dual-Prompt-Driven Network for Universal PET-CT Segmentation

PET-CT lesion segmentation is challenging due to noise sensitivity, small and variable lesion morp...

Emerging Frameworks for Objective Task-based Evaluation of Quantitative Medical Imaging Methods

Quantitative imaging (QI) is demonstrating strong promise across multiple clinical applications. F...

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...

A robust and versatile deep learning model for prediction of the arterial input function in dynamic small animal $\left[^{18}\text{F}\right]$FDG PET imaging

Dynamic positron emission tomography (PET) and kinetic modeling are pivotal in advancing tracer de...

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

Supervised Diffusion-Model-Based PET Image Reconstruction

Diffusion models (DMs) have recently been introduced as a regularizing prior for PET image reconst...

MS-IQA: A Multi-Scale Feature Fusion Network for PET/CT Image Quality Assessment

Positron Emission Tomography / Computed Tomography (PET/CT) plays a critical role in medical imagi...

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...

Mono-Modalizing Extremely Heterogeneous Multi-Modal Medical Image Registration

In clinical practice, imaging modalities with functional characteristics, such as positron emissio...

Personalized MR-Informed Diffusion Models for 3D PET Image Reconstruction

Recent work has shown improved lesion detectability and flexibility to reconstruction hyperparamet...

petBrain: A New Pipeline for Amyloid, Tau Tangles and Neurodegeneration Quantification Using PET and MRI

INTRODUCTION: Quantification of amyloid plaques (A), neurofibrillary tangles (T2), and neurodegene...

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...

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...

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...