Cost benefit analysis of portable chest radiography through glass: Initial experience at a tertiary care centre during COVID-19 pandemic.

Introduction: Portable chest radiography through glass (TG-CXR) is a novel technique, particularly useful during the COVID-19 (Coronavirus disease 2019) pandemic. The purpose of this study was to understand the cost and benefit of adopting TG-CXR in quantifiable terms.

Methods: Portable or bedside radiographs are typically performed by a team of two technologists.

MR imaging in the presence of ballistic debris of unknown composition: a review of the literature and practical approach.

Due to a combination of increasing indications for MR imaging, increased MRI accessibility, and extensive global armed conflict over the last few decades, an increasing number of patients now and in the future will present with retained metallic ballistic debris of unknown composition. To date, there are no guidelines on how to safely image these patients which may result in patients who would benefit from MRI not receiving it. In this article, we review the current literature pertaining to the MRI safety of retained ballistic materials and present the process we use to safely image these patients.

Establishing Diagnostic Reference Levels for CT Through a Provincial Medical Informatics Metadata Repository in Ontario.

Purpose: To determine whether computed tomography radiation dose data could be captured electronically across hospitals to derive regional diagnostic reference levels for quality improvement.

Methods: Data on consecutive computed tomography examinations from 8 hospitals were collected automatically in a central database (Repository) from April 2017 to September 2017. The most frequently performed examinations were used to determine the standard protocols for each hospital.

Synthesizing Chest X-Ray Pathology for Training Deep Convolutional Neural Networks.

Medical datasets are often highly imbalanced with over-representation of prevalent conditions and poor representation of rare medical conditions. Due to privacy concerns, it is challenging to aggregate large datasets between health care institutions. We propose synthesizing pathology in medical images as a means to overcome these challenges.

Training and Validating a Deep Convolutional Neural Network for Computer-Aided Detection and Classification of Abnormalities on Frontal Chest Radiographs.

Objectives: Convolutional neural networks (CNNs) are a subtype of artificial neural network that have shown strong performance in computer vision tasks including image classification. To date, there has been limited application of CNNs to chest radiographs, the most frequently performed medical imaging study. We hypothesize CNNs can learn to classify frontal chest radiographs according to common findings from a sufficiently large data set.