Nuclear Medicine

Applications of machine learning and deep learning in SPECT and PET imaging: General overview, challenges and future prospects.

The integration of positron emission tomography (PET) and single-photon emission computed tomography (SPECT) imaging techniques with machine learning (ML) algorithms, including deep learning (DL) models, is a promising approach. This integration enhances the precision and efficiency of current diagnostic and treatment strategies while offering invaluable insights into disease mechanisms. In this c...

Low PSA radiographic disease progression on C11-choline PET.

BACKGROUND: For men with prostate cancer, radiographic progression may occur without a concordant rise in prostate-specific antigen (PSA). Our study aimed to assess the prevalence of radiographic progression using C-11 choline positron emission tomography (PET) imaging in patients achieving ultra-low PSA values and to evaluate clinical outcomes in this patient population.

Image Denoising of Low-Dose PET Mouse Scans with Deep Learning: Validation Study for Preclinical Imaging Applicability.

PURPOSE: Positron emission tomography (PET) image quality can be improved by higher injected activity and/or longer acquisition time, but both may oft...

Deep learning techniques in PET/CT imaging: A comprehensive review from sinogram to image space.

Positron emission tomography/computed tomography (PET/CT) is increasingly used in oncology, neurology, cardiology, and emerging medical fields. The su...

Deep learning based diagnosis of Alzheimer's disease using FDG-PET images.

PURPOSE: The aim of this study is to develop a deep neural network to diagnosis Alzheimer's disease and categorize the stages of the disease using FDG...

Machine learning methods for tracer kinetic modelling.

Tracer kinetic modelling based on dynamic PET is an important field of Nuclear Medicine for quantitative functional imaging. Yet, its implementation i...

Advancements in Positron Emission Tomography Detectors: From Silicon Photomultiplier Technology to Artificial Intelligence Applications.

This review article focuses on PET detector technology, which is the most crucial factor in determining PET image quality. The article highlights the ...

Bayesian Convolutional Neural Networks in Medical Imaging Classification: A Promising Solution for Deep Learning Limits in Data Scarcity Scenarios.

Deep neural networks (DNNs) have already impacted the field of medicine in data analysis, classification, and image processing. Unfortunately, their p...

A Shortened Model for Logan Reference Plot Implemented via the Self-Supervised Neural Network for Parametric PET Imaging.

Dynamic PET imaging provides superior physiological information than conventional static PET imaging. However, the dynamic information is gained at th...

Physics-Guided Deep Scatter Estimation by Weak Supervision for Quantitative SPECT.

Accurate scatter estimation is important in quantitative SPECT for improving image contrast and accuracy. With a large number of photon histories, Mon...

A look at radiation detectors and their applications in medical imaging.

The effectiveness and precision of disease diagnosis and treatment have increased, thanks to developments in clinical imaging over the past few decade...

A comparison of 18 F-FDG PET-based radiomics and deep learning in predicting regional lymph node metastasis in patients with resectable lung adenocarcinoma: a cross-scanner and temporal validation study.

OBJECTIVE: The performance of 18 F-FDG PET-based radiomics and deep learning in detecting pathological regional nodal metastasis (pN+) in resectable l...

PET/CT-based deep learning grading signature to optimize surgical decisions for clinical stage I invasive lung adenocarcinoma and biologic basis under its prediction: a multicenter study.

PURPOSE: No consensus on a grading system for invasive lung adenocarcinoma had been built over a long period of time. Until October 2020, a novel grad...

Deep learning-based iodine contrast-augmenting algorithm for low-contrast-dose liver CT to assess hypovascular hepatic metastasis.

PURPOSE: To investigate the image quality and diagnostic performance of low-contrast-dose liver CT using a deep learning-based iodine contrast-augment...

Prediction of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer by using a deep learning model with 18F-FDG PET/CT.

OBJECTIVES: The aim of the study is 18F-FDG PET/CT imaging by using deep learning method are predictive for pathological complete response pCR after N...

Multicentric Ga-PSMA PET radiomics for treatment response assessment of Lu-PSMA-617 radioligand therapy in patients with metastatic castration-resistant prostate cancer.

OBJECTIVE: The treatment with Lutetium PSMA (Lu-PSMA) in patients with metastatic castration-resistant prostate cancer (mCRPC) has recently been appro...

Comparison of post reconstruction- and reconstruction-based deep learning denoising methods in cardiac SPECT.

. The quality of myocardial perfusion SPECT (MPS) images is often hampered by low count statistics. Poor image quality might hinder reporting the stud...

Short-axis PET image quality improvement based on a uEXPLORER total-body PET system through deep learning.

PURPOSE: The axial field of view (AFOV) of a positron emission tomography (PET) scanner greatly affects the quality of PET images. Although a total-bo...

AI for PET image reconstruction.

Image reconstruction for positron emission tomography (PET) has been developed over many decades, with advances coming from improved modelling of the ...

Robot-assisted PSMA-radioguided Surgery to Assess Surgical Margins and Nodal Metastases in Prostate Cancer Patients: Report on Three Cases Using an Intraoperative PET-CT Specimen Imager.

INTRODUCTION: The aim of this feasibility study was to test the intraoperative use of this brand-new specimen PET/CT to guide robot-assisted radical p...