Fully Automatic Quantitative Measurement of Equilibrium Radionuclide Angiocardiography Using a Convolutional Neural Network.

Purpose: The aim of this study was to generate deep learning-based regions of interest (ROIs) from equilibrium radionuclide angiography datasets for left ventricular ejection fraction (LVEF) measurement.

Patients And Methods: Manually drawn ROIs (mROIs) on end-systolic and end-diastolic images were extracted from reports in a Picture Archiving and Communications System. To reduce observer variability, preprocessed ROIs (pROIs) were delineated using a 41% threshold of the maximal pixel counts of the extracted mROIs and were labeled as ground-truth.

One-Year Longitudinal Changes in Tau Accumulation on [F]PI-2620 PET in the Alzheimer Spectrum.

We investigated the longitudinal changes in cortical tau accumulation and their association with cognitive decline in patients in the Alzheimer disease (AD) continuum using 2-(2-([F]fluoro)pyridin-4-yl)-9-pyrrolo[2,3-b:4,5c']dipyridine ([F]PI-2620) PET. We prospectively enrolled 52 participants (age, 69.7 ± 8.

Scaling problem in Parkinson's disease patients with pain.

Introduction: Although pain is common in Parkinson's disease (PD), the underlying mechanism remains unknown. Scaling function and dopaminergic hypofunction may contribute to pain development because increased pain sensitivity is observed in PD and is normalized after levodopa administration. We aimed to determine whether spatial discrimination (SD) and striatal dopaminergic activity (DA) differed between PD patients with and without pain.

Performance of deep learning models for response evaluation on whole-body bone scans in prostate cancer.

Objective: We aimed to develop deep learning classifiers for assessing therapeutic response on bone scans of patients with prostate cancer.

Methods: A set of 3791 consecutive bone scans coupled with their last previous scan (1528 patients) was evaluated. Bone scans were labeled as "progression" or "nonprogression" on the basis of clinical reports and image review.

Surface Functionalization and Reversible Disassembly of DNA-Assembly Nanoparticles for Sensitive and Multiplexed Detection of DNA Targets Without Enzymatic and Catalytic Amplification.

Recently, DNA-assembly nanoparticles based on DNA-metal ion interactions are emerging as new building blocks for drug delivery and metal nanostructure synthesis. However, the surface modification of DNA-assembly nanoparticles using functional biomolecules that can identify specific targets has rarely been explored. In this study, we developed a new immobilization chemical strategy to efficiently functionalize the barcode DNA-assembly nanoparticles (bcDNA NPs) with thiolated probe DNA (pDNA) for synthesizing pDNA-functionalized bcDNA NPs (pDNA-bcDNA NPs).

MR Template-Based Individual Brain PET Volumes-of-Interest Generation Neither Using MR nor Using Spatial Normalization.

Unlabelled: For more anatomically precise quantitation of mouse brain PET, spatial normalization (SN) of PET onto MR template and subsequent template volumes-of-interest (VOIs)-based analysis are commonly used. Although this leads to dependency on the corresponding MR and the process of SN, routine preclinical/clinical PET images cannot always afford corresponding MR and relevant VOIs. To resolve this issue, we propose a deep learning (DL)-based individual-brain-specific VOIs (i.

Retinal Thickness and Its Interocular Asymmetry Between Parkinson's Disease and Drug-Induced Parkinsonism.

Background: Drug-induced parkinsonism (DIP) is common, but diagnosis is challenging. Although dopamine transporter imaging is useful, the cost and inconvenience are problematic, and an easily accessible screening technique is needed. We aimed to determine whether optical coherence tomography (OCT) findings could differentiate DIP from Parkinson's disease (PD).

Fully Automatic Quantitative Measurement of 18F-FDG PET/CT in Thymic Epithelial Tumors Using a Convolutional Neural Network.

Objectives: The aim of this study was to develop a deep learning (DL)-based segmentation algorithm for automatic measurement of metabolic parameters of 18F-FDG PET/CT in thymic epithelial tumors (TETs), comparable performance to manual volumes of interest.

Patients And Methods: A total of 186 consecutive patients with resectable TETs and preoperative 18F-FDG PET/CT were retrospectively enrolled (145 thymomas, 41 thymic carcinomas). A quasi-3D U-net architecture was trained to resemble ground-truth volumes of interest.

Unified Deep Learning-Based Mouse Brain MR Segmentation: Template-Based Individual Brain Positron Emission Tomography Volumes-of-Interest Generation Without Spatial Normalization in Mouse Alzheimer Model.

Although skull-stripping and brain region segmentation are essential for precise quantitative analysis of positron emission tomography (PET) of mouse brains, deep learning (DL)-based unified solutions, particularly for spatial normalization (SN), have posed a challenging problem in DL-based image processing. In this study, we propose an approach based on DL to resolve these issues. We generated both skull-stripping masks and individual brain-specific volumes-of-interest (VOIs-cortex, hippocampus, striatum, thalamus, and cerebellum) based on inverse spatial normalization (iSN) and deep convolutional neural network (deep CNN) models.

Diagnostic and prognostic values of 2-[F]FDG PET/CT in resectable thymic epithelial tumour.

Objectives: We aimed to evaluate the diagnostic ability for the prediction of histologic grades and prognostic values on recurrence and death of pretreatment 2-[F]FDG PET/CT in patients with resectable thymic epithelial tumours (TETs).

Methods: One hundred and fourteen patients with TETs who underwent pretreatment 2-[F]FDG PET/CT between 2012 and 2018 were retrospectively evaluated. TETs were classified into three histologic subtypes: low-risk thymoma (LRT, WHO classification A/AB/B1), high-risk thymoma (HRT, B2/B3), and thymic carcinoma (TC).

Comparison of diagnostic sensitivity of [F]fluoroestradiol and [F]fluorodeoxyglucose positron emission tomography/computed tomography for breast cancer recurrence in patients with a history of estrogen receptor-positive primary breast cancer.

Background: To compare the diagnostic sensitivity of [F]fluoroestradiol ([F]FES) and [F]fluorodeoxyglucose ([F]FDG) positron emission tomography/computed tomography (PET/CT) for breast cancer recurrence in patients with estrogen receptor (ER)-positive primary breast cancer.

Methods: Our database of consecutive patients enrolled in a previous prospective cohort study to assess [F]FES PET/CT was reviewed to identify eligible patients who had ER-positive primary breast cancer with suspected first recurrence at presentation and who underwent [F]FDG PET/CT. The sensitivity of qualitative [F]FES and [F]FDG PET/CT interpretations was assessed, comparing them with histological diagnoses.

Assessment of Electrocardiogram Rhythms by GoogLeNet Deep Neural Network Architecture.

The aim of this study is to design GoogLeNet deep neural network architecture by expanding the kernel size of the inception layer and combining the convolution layers to classify the electrocardiogram (ECG) beats into a normal sinus rhythm, premature ventricular contraction, atrial premature contraction, and right/left bundle branch block arrhythmia. Based on testing MIT-BIH arrhythmia benchmark databases, the scope of training/test ECG data was configured by covering at least three and seven -peak features, and the proposed extended-GoogLeNet architecture can classify five distinct heartbeats; normal sinus rhythm (NSR), premature ventricular contraction (PVC), atrial premature contraction (APC), right bundle branch block (RBBB), and left bundle brunch block(LBBB), with an accuracy of 95.94%, an error rate of 4.

Effect of Nutrition Counseling Program on Weight Control in Obese University Students.

This study was conducted to investigate the effect of nutrition counseling program and related factors on weight control for obese university students. Subjects were 24 students with a body mass index (BMI) of 25 or above. The program was conducted from September 16th to November 18th, 2015.