Nuclear Medicine

Rapid high-quality PET Patlak parametric image generation based on direct reconstruction and temporal nonlocal neural network.

Parametric imaging based on dynamic positron emission tomography (PET) has wide applications in neurology. Compared to indirect methods, direct reconstruction methods, which reconstruct parametric images directly from the raw PET data, have superior image quality due to better noise modeling and richer information extracted from the PET raw data. For low-dose scenarios, the advantages of direct me...

Assessment of deep learning-based PET attenuation correction frameworks in the sinogram domain.

This study set out to investigate various deep learning frameworks for PET attenuation correction in the sinogram domain. Different models for both time-of-flight (TOF) and non-TOF PET emission data were implemented, including direct estimation of the attenuation corrected (AC) emission sinograms from the nonAC sinograms, estimation of the attenuation correction factors (ACFs) from PET emission da...

Evaluating the Efficacy of Povidone-Iodine Solution Infection Prophylaxis in Immediate Tissue Expander-Based Breast Reconstruction: A Controlled Retrospective Analysis.

There is currently no consensus among plastic surgeons regarding the optimal infection prophylaxis for immediate tissue expander placement following ...

Development of attenuation correction methods using deep learning in brain-perfusion single-photon emission computed tomography.

PURPOSE: Computed tomography (CT)-based attenuation correction (CTAC) in single-photon emission computed tomography (SPECT) is highly accurate, but it...

Automated Data Quality Control in FDOPA brain PET Imaging using Deep Learning.

INTRODUCTION: With biomedical imaging research increasingly using large datasets, it becomes critical to find operator-free methods to quality control...

Deep learning image reconstruction algorithm for pancreatic protocol dual-energy computed tomography: image quality and quantification of iodine concentration.

OBJECTIVES: To evaluate the image quality and iodine concentration (IC) measurements in pancreatic protocol dual-energy computed tomography (DECT) rec...

A different overview of staging PET/CT images in patients with esophageal cancer: the role of textural analysis with machine learning methods.

OBJECTIVE: This study evaluates the ability of several machine learning (ML) algorithms, developed using volumetric and texture data extracted from ba...

Ultrasensitive biosensing platform based on luminescence quenching ability of fullerenol quantum dots.

An ultrasensitive biosensing platform for DNA and ochratoxin A (OTA) detection is constructed based on the luminescence quenching ability of fullereno...

Artificial intelligence could alert for focal skeleton/bone marrow uptake in Hodgkin's lymphoma patients staged with FDG-PET/CT.

To develop an artificial intelligence (AI)-based method for the detection of focal skeleton/bone marrow uptake (BMU) in patients with Hodgkin's lympho...

A Multiprocessing Scheme for PET Image Pre-Screening, Noise Reduction, Segmentation and Lesion Partitioning.

Accurate segmentation and partitioning of lesions in PET images provide computer-aided procedures and doctors with parameters for tumour diagnosis, st...

Deep Learning for Fully Automated Prediction of Overall Survival in Patients with Oropharyngeal Cancer Using FDG-PET Imaging.

PURPOSE: Accurate prognostic stratification of patients with oropharyngeal squamous cell carcinoma (OPSCC) is crucial. We developed an objective and r...

Prediction of the treatment outcome using machine learning with FDG-PET image-based multiparametric approach in patients with oral cavity squamous cell carcinoma.

AIM: To investigate the value of machine learning-based multiparametric analysis using 2-[F]-fluoro-2-deoxy-d-glucose positron-emission tomography (FD...

Machine Learning with F-Sodium Fluoride PET and Quantitative Plaque Analysis on CT Angiography for the Future Risk of Myocardial Infarction.

Coronary F-sodium fluoride (F-NaF) PET and CT angiography-based quantitative plaque analysis have shown promise in refining risk stratification in pat...

Quantitative salivary gland SPECT/CT using deep convolutional neural networks.

Quantitative single-photon emission computed tomography/computed tomography (SPECT/CT) using Tc-99m pertechnetate aids in evaluating salivary gland fu...

Deep learning-based attenuation correction for brain PET with various radiotracers.

OBJECTIVES: Attenuation correction (AC) is crucial for ensuring the quantitative accuracy of positron emission tomography (PET) imaging. However, obta...

A novel deep-learning-based approach for automatic reorientation of 3D cardiac SPECT images.

PURPOSE: Reconstructed transaxial cardiac SPECT images need to be reoriented into standard short-axis slices for subsequent accurate processing and an...

Quantitative Molecular Positron Emission Tomography Imaging Using Advanced Deep Learning Techniques.

The widespread availability of high-performance computing and the popularity of artificial intelligence (AI) with machine learning and deep learning (...

Convolutional neural networks for PET functional volume fully automatic segmentation: development and validation in a multi-center setting.

PURPOSE: In this work, we addressed fully automatic determination of tumor functional uptake from positron emission tomography (PET) images without re...

Multi-task weak supervision enables anatomically-resolved abnormality detection in whole-body FDG-PET/CT.

Computational decision support systems could provide clinical value in whole-body FDG-PET/CT workflows. However, limited availability of labeled data ...

Application of a Machine Learning Approach for the Analysis of Clinical and Radiomic Features of Pretreatment [F]-FDG PET/CT to Predict Prognosis of Patients with Endometrial Cancer.

PURPOSE: To examine the prognostic significance of pretreatment 2-deoxy-2-[F]fluoro-D-glucose ([F]-FDG) positron emission tomography (PET)-based radio...