Nuclear Medicine

Comparison of multi-organ CT image segmentation tools for whole-body [18F]FDG-PET/CT clinical imaging.

UNLABELLED: Automated multi-organ segmentation looks to assist clinicians and researchers working with Positron Emission Tomography/Computed Tomography (PET/CT) imaging in streamlining the time-consuming, operator-dependent task of manual delineation. This study aimed to compare two state-of-the-art automated multi-organ CT segmentation tools with typically perceived "gold-standard" manual delinea...

Feasibility of transperineal prostate biopsy guided by fusion of [18F]F-PSMA-1007 PET/MRI with real-time transrectal ultrasound. Prostate cancer classification using imaging biomarkers and radiomics in PIRADS 3 lesions.

OBJECTIVE: To assess the diagnostic feasibility of transperineal biopsy guided by fusion of PET/MRI with [18F]F-PSMA-1007 and real-time transrectal ultrasound (BP PET/MR PSMA + TRUS) in patients with PIRADS 3 lesions. To analyze imaging biomarkers and radiomic features for differentiating between patients with negative biopsy, clinically non-significant prostate cancer (cnsPCa), and clinically sig...

Examination of simple artificial intelligence-based analysis of dopamine transporter scintigraphy for supporting a diagnosis of Parkinson's disease.

INTRODUCTION: In the Movement Disorder Society criteria for the diagnosis of Parkinson's disease (PD), evaluation of the presynaptic dopamine system s...

Early prediction of response to ADT plus ARPIs in mHSPC using interpretable machine learning on PSMA PET/CT: a real-world study.

BACKGROUND: Reliable tools for early prediction of treatment response to androgen deprivation therapy (ADT) plus novel androgen receptor pathway inhib...

Application of Total-Body PET in Pediatric Patients.

This article highlights the advancements in total-body PET (TB PET) imaging, emphasizing its benefits for pediatric patients, including low-dose proto...

Artificial intelligence for TNM staging in NSCLC: a critical appraisal of segmentation utility in [1⁸F]FDG PET/CT.

PURPOSE: This study aims to investigate whether a diagnostic AI model can effectively support lesion detection and staging in non-small cell lung canc...

Prognostic value of a combined model integrating clinical and PET radiomics parameters in metastatic melanoma: A dual-center retrospective study.

OBJECTIVES: To develop and evaluate the predictive efficacy of a combined model incorporating clinical parameters and PET-based radiomics signature (R...

Low-count whole-body PET denoising with deep learning in a multicenter, multi-tracer and externally validated study.

BACKGROUND: Positron Emission Tomography (PET) is a powerful diagnostic tool, but its availability, high cost and radiation burden limit its accessibi...

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

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

An integrated machine learning framework for predicting anthropogenic and natural iodine isotopes in the South China Sea with uncertainty quantification.

Anthropogenic Iodine-129 (129I) is a critical long-lived radionuclide for tracing ocean circulation and environmental contamination. However, its meas...

Accuracy of Photon Dose Calculation on Photon-Counting Computed Tomography-A Comparison Study Based on Virtual Monoenergetic and Electron Density (Rho) Images for Pancreatic Cases.

PURPOSE: Photon-counting computed tomography (PCCT) offers versatile anatomic information because of better energy discrimination and higher spatial r...

Total-body [18F]FDG-PET/CT imaging of healthy volunteers with minimal effective dose.

PURPOSE: High-sensitivity, total-body (TB) positron emission tomography (PET) and computed tomography (CT) imaging systems enable substantial reductio...

Deep learning-based obstructive coronary artery disease prediction from myocardial perfusion SPECT.

PURPOSE: We aim to apply the deep learning (DL) technique to predict the gold-standard invasive coronary angiography (ICA) for coronary artery disease...

Resolution and quality enhancement of SPECT cerebral blood flow images using Pix2pix deep learning.

BACKGROUND: Cerebral blood flow (CBF) imaging can be performed using SPECT with 123I-IMP; however, its spatial resolution and image quality are inferi...

18F-FDG PET-based ensemble deep learning model for the prediction of lymphovascular invasion in colorectal cancer patients.

OBJECTIVE: The aim of this study was to investigate the diagnostic performance of the 2.5-dimensional (2.5D) ensemble deep learning (DL) model based o...

Mapping systemic inter-organ metabolic networks across glycemic continuum using whole-body [18F]FDG PET/CT and machine learning.

PURPOSE: Glucose homeostasis relies on coordinated interactions among multiple organs, and its disruption relates to diabetes development. This study ...

AI-Quantified ¹¹C-MET PET/CT bone marrow metabolic activity for prognostic assessment in newly diagnosed multiple myeloma.

PURPOSE: To develop and validate an AI method for automated quantification of whole-skeleton bone marrow (BM) metabolic activity using Carbon 11 (11C)...

Preventing exotic pet beetle invasion with an improved lightweight and efficient pest detection model deployed on mobile devices.

BACKGROUND: The illegal smuggling of exotic pet beetles presents a growing threat to global ecosystems. Customs authorities play a critical role in pr...

NHOC/NHOP as novel biomarkers for predicting lymph node metastasis in NSCLC using PET/CT radiomics and machine learning: a two-center retrospective study.

PURPOSE: NHOC and NHOP, defined as the normalized distances from peak uptake to tumour centroid and perimeter, are novel PET/CT metrics of tumour aggr...

coPERCIST: AI-assisted PET-CT response assessment.

PURPOSE: The PET Response Criteria in Solid Tumours (PERCIST) 1.0 provides a standardized framework for evaluating treatment response using [18F]fluor...