Nuclear Medicine

ACTIVITY CONCENTRATION ESTIMATION IN AUTOMATED KIDNEY SEGMENTATION BASED ON CONVOLUTION NEURAL NETWORK METHOD FOR 177LU-SPECT/CT KIDNEY DOSIMETRY.

For 177Lu-DOTATATE treatments, dosimetry based on manual kidney segmentation from computed tomograph...

IMPROVEMENTS OF 111IN SPECT IMAGES RECONSTRUCTED WITH SPARSELY ACQUIRED PROJECTIONS BY DEEP LEARNING GENERATED SYNTHETIC PROJECTIONS.

The aim was to improve single-photon emission computed tomography (SPECT) quality for sparsely acqui...

PET and AI Trajectories Finally Coming into Alignment.

Toward High-Throughput Artificial Intelligence-Based Segmentation in Oncological PET Imaging.

Artificial intelligence (AI) techniques for image-based segmentation have garnered much attention in...

Artificial Intelligence-Based Image Enhancement in PET Imaging: Noise Reduction and Resolution Enhancement.

High noise and low spatial resolution are two key confounding factors that limit the qualitative and...

The Evolution of Image Reconstruction in PET: From Filtered Back-Projection to Artificial Intelligence.

PET can provide functional images revealing physiologic processes in vivo. Although PET has many app...

Potential Applications of Artificial Intelligence and Machine Learning in Radiochemistry and Radiochemical Engineering.

Artificial intelligence and machine learning are poised to disrupt PET imaging from bench to clinic....

Computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles.

The exploration of three-dimensional chromatin interaction and organization provides insight into me...

Performance Evaluation of a Deep Learning System for Differential Diagnosis of Lung Cancer With Conventional CT and FDG PET/CT Using Transfer Learning and Metadata.

PURPOSE: We aimed to evaluate the performance of a deep learning system for differential diagnosis o...

Automated classification of increased uptake regions in bone single-photon emission computed tomography/computed tomography images using three-dimensional deep convolutional neural network.

OBJECTIVE: This study proposes an automated classification of benign and malignant in highly integra...

Feasibility of Deep Learning-Guided Attenuation and Scatter Correction of Whole-Body 68Ga-PSMA PET Studies in the Image Domain.

OBJECTIVE: This study evaluates the feasibility of direct scatter and attenuation correction of whol...

Non-invasive measurement of PD-L1 status and prediction of immunotherapy response using deep learning of PET/CT images.

BACKGROUND: Currently, only a fraction of patients with non-small cell lung cancer (NSCLC) treated w...

Swarming behavior and in vivo monitoring of enzymatic nanomotors within the bladder.

Enzyme-powered nanomotors are an exciting technology for biomedical applications due to their abilit...

PET/CT for Brain Amyloid: A Feasibility Study for Scan Time Reduction by Deep Learning.

PURPOSE: This study was to develop a convolutional neural network (CNN) model with a residual learni...

[Accuracy of Classification of Cerebral Blood Flow Reduction Patterns Using Statistical Analysis Images Generated with Simulated SPECT Datasets via Deep Learning].

PURPOSE: The aim of this study was to evaluate the classification accuracy of specific blood flow re...

Blinded Clinical Evaluation for Dementia of Alzheimer's Type Classification Using FDG-PET: A Comparison Between Feature-Engineered and Non-Feature-Engineered Machine Learning Methods.

BACKGROUND: Advanced machine learning methods can aid in the identification of dementia risk using n...

Deep learning for intelligent diagnosis in thyroid scintigraphy.

OBJECTIVE: To construct deep learning (DL) models to improve the accuracy and efficiency of thyroid ...

Study of the Usefulness of Bone Scan Index Calculated From 99m-Technetium- Hydroxymethylene Diphosphonate (Tc-HMDP) Bone Scintigraphy for Bone Metastases from Prostate Cancer Using Deep Learning Algorithms.

BACKGROUND: BSI calculated from bone scintigraphy using technetium-methylene diphosphonate (Tc-MDP) ...

Robotic Pet Use Among Community-Dwelling Older Adults.

OBJECTIVE: The primary purpose of this study was to explore the efficacy of robotic pets in alleviat...

Combining Superpixels and Deep Learning Approaches to Segment Active Organs in Metastatic Breast Cancer PET Images.

Semi-automatic measurements are performed on FDG PET-CT images to monitor the evolution of metastati...

Deep learning approaches for bone and bone lesion segmentation on 18FDG PET/CT imaging in the context of metastatic breast cancer.

FDG PET/CT imaging is commonly used in diagnosis and follow-up of metastatic breast cancer, but its ...