Nuclear Medicine

Measurement of Glomerular Filtration Rate using Quantitative SPECT/CT and Deep-learning-based Kidney Segmentation.

Quantitative SPECT/CT is potentially useful for more accurate and reliable measurement of glomerular filtration rate (GFR) than conventional planar scintigraphy. However, manual drawing of a volume of interest (VOI) on renal parenchyma in CT images is a labor-intensive and time-consuming task. The aim of this study is to develop a fully automated GFR quantification method based on a deep learning ...

Machine learning polymer models of three-dimensional chromatin organization in human lymphoblastoid cells.

We present machine learning models of human genome three-dimensional structure that combine one dimensional (linear) sequence specificity, epigenomic information, and transcription factor binding profiles, with the polymer-based biophysical simulations in order to explain the extensive long-range chromatin looping observed in ChIA-PET experiments for lymphoblastoid cells. Random Forest, Gradient B...

Scaled Subprofile Modeling and Convolutional Neural Networks for the Identification of Parkinson's Disease in 3D Nuclear Imaging Data.

Over the last years convolutional neural networks (CNNs) have shown remarkable results in different image classification tasks, including medical imag...

Thyroid Diagnosis from SPECT Images Using Convolutional Neural Network with Optimization.

Thyroid disease has now become the second largest disease in the endocrine field; SPECT imaging is particularly important for the clinical diagnosis o...

MRI-based attenuation correction for brain PET/MRI based on anatomic signature and machine learning.

Deriving accurate attenuation maps for PET/MRI remains a challenging problem because MRI voxel intensities are not related to properties of photon att...

Deep Learning Based Attenuation Correction of PET/MRI in Pediatric Brain Tumor Patients: Evaluation in a Clinical Setting.

Positron emission tomography (PET) imaging is a useful tool for assisting in correct differentiation of tumor progression from reactive changes. O-(2...

Simultaneous cosegmentation of tumors in PET-CT images using deep fully convolutional networks.

PURPOSE: To investigate the use and efficiency of 3-D deep learning, fully convolutional networks (DFCN) for simultaneous tumor cosegmentation on dual...

Widespread occurrence of glyphosate in urine from pet dogs and cats in New York State, USA.

Glyphosate is one of the most widely used herbicides in the United States, which has led to its ubiquitous occurrence in food and water and regular de...

Tumor co-segmentation in PET/CT using multi-modality fully convolutional neural network.

Automatic tumor segmentation from medical images is an important step for computer-aided cancer diagnosis and treatment. Recently, deep learning has b...

Prediction of Lymph Node Maximum Standardized Uptake Value in Patients With Cancer Using a 3D Convolutional Neural Network: A Proof-of-Concept Study.

OBJECTIVE: The purpose of this study is to determine whether a convolutional neural network (CNN) can predict the maximum standardized uptake value (S...

Ultra-Low-Dose F-Florbetaben Amyloid PET Imaging Using Deep Learning with Multi-Contrast MRI Inputs.

Purpose To reduce radiotracer requirements for amyloid PET/MRI without sacrificing diagnostic quality by using deep learning methods. Materials and Me...

Personalized Models for Injected Activity Levels in SPECT Myocardial Perfusion Imaging.

We propose a patient-specific ("personalized") approach for tailoring the injected activities to individual patients in order to achieve dose reductio...

Pet robot intervention for people with dementia: A systematic review and meta-analysis of randomized controlled trials.

This study aims to systematically evaluate the efficacy of Pet robot intervention (PRI) for people with dementia. Two waves of electronic searches of ...

Effect of PET-MR Inconsistency in the Kernel Image Reconstruction Method.

Anatomically-driven image reconstruction algorithms have become very popular in positron emission tomography (PET) where they have demonstrated improv...

3D Auto-Context-Based Locality Adaptive Multi-Modality GANs for PET Synthesis.

Positron emission tomography (PET) has been substantially used recently. To minimize the potential health risk caused by the tracer radiation inherent...

Automated classification of benign and malignant lesions in F-NaF PET/CT images using machine learning.

PURPOSE: F-NaF PET/CT imaging of bone metastases is confounded by tracer uptake in benign diseases, such as osteoarthritis. The goal of this work was ...

Resource-Efficient Pet Dog Sound Events Classification Using LSTM-FCN Based on Time-Series Data.

The use of IoT (Internet of Things) technology for the management of pet dogs left alone at home is increasing. This includes tasks such as automatic ...

Repeat Ultrasonography in the First Years after Therapy with Radioiodine Is Not Necessary in Most Patients with Papillary Thyroid Carcinoma when Postoperative Ultrasonography Is Negative: A Reduction of Costs and False-Positives.

BACKGROUND: Periodic ultrasonography (US) examination is recommended in many patients with papillary thyroid carcinoma (PTC) after treatment with radi...

Prediction of early metastatic disease in experimental breast cancer bone metastasis by combining PET/CT and MRI parameters to a Model-Averaged Neural Network.

Macrometastases in bone are preceded by bone marrow invasion of disseminated tumor cells. This study combined functional imaging parameters from FDG-P...

A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using F-FDG PET of the Brain.

Purpose To develop and validate a deep learning algorithm that predicts the final diagnosis of Alzheimer disease (AD), mild cognitive impairment, or n...