Adaptive CaRdiac cOne BEAm computed Tomography (ACROBEAT): Developing the next generation of cardiac cone beam CT imaging.

Purpose: An important factor when considering the use of interventional cone beam computed tomography (CBCT) imaging during cardiac procedures is the trade-off between imaging dose and image quality. Accordingly, Adaptive CaRdiac cOne BEAm computed Tomography (ACROBEAT) presents an alternative acquisition method, adapting the gantry velocity and projection rate of CBCT imaging systems in accordance with a patient's electrocardiogram (ECG) signal in real-time. The aim of this study was to experimentally investigate that ACROBEAT acquisitions deliver improved image quality compared to conventional cardiac CBCT imaging protocols with fewer projections acquired.

Derivation of Respiratory Metrics in Health and Asthma.

Unlabelled: The ability to continuously monitor breathing metrics may have indications for general health as well as respiratory conditions such as asthma. However, few studies have focused on breathing due to a lack of available wearable technologies. To examine the performance of two machine learning algorithms in extracting breathing metrics from a finger-based pulse oximeter, which is amenable to long-term monitoring.

Toward improved 3D carotid artery imaging with Adaptive CaRdiac cOne BEAm computed Tomography (ACROBEAT).

Purpose: Interventional treatments of aneurysms in the carotid artery are increasingly being supplemented with three-dimensional (3D) x-ray imaging. The 3D imaging provides additional information on device sizing and stent malapposition during the procedure. Standard 3D x-ray image acquisition is a one-size fits all model, exposing patients to additional radiation and results in images that may have cardiac-induced motion blur around the artery.

Derivation of Breathing Metrics From a Photoplethysmogram at Rest: Machine Learning Methodology.

Background: There has been a recent increased interest in monitoring health using wearable sensor technologies; however, few have focused on breathing. The ability to monitor breathing metrics may have indications both for general health as well as respiratory conditions such as asthma, where long-term monitoring of lung function has shown promising utility.

Objective: In this paper, we explore a long short-term memory (LSTM) architecture and predict measures of interbreath intervals, respiratory rate, and the inspiration-expiration ratio from a photoplethysmogram signal.

Experimental Validation of the Multiphysics Modelling of Radiofrequency Ablation using Physical Phantom.

Spastic Cerebral Palsy is the most common type of Cerebral Palsy (CP) that affects muscle coordination and control. Using Radiofrequency Ablation (RFA) to target the motor branches of the median and ulnar nerve may provide an additional treatment to Botulinum Toxin for spasticity in the hand. However, there are a limited number of studies that report the use of RFA in treating upper-limb spasticity.

A novel hardware implementation for detecting respiration rate using photoplethysmography.

Asthma is a serious public health problem. Continuous monitoring of breathing may offer an alternative way to assess disease status. In this paper we present a novel hardware implementation for the capture and storage of a photoplethysmography (PPG) signal.