Computed Tomography (CT) is widely used for the diagnosis of variety of medical and surgical conditions and acquiring high quality CT diagnostic images with lower radiation would be ideal to reduce the risks of radiation. One method of solving this problem is to focus on optimizing the reconstruction methods in CT. The first version of AREMI uses multi-core GPU for the reconstruction from raw attenuation data measured by a Siemens Definition Flash CT scanner.

Cardiac magnetic resonance imaging is used to evaluate the right ventricle in a number of congenital heart conditions, such as post-op Tetralogy of Fallot. Currently, the clinical standard is to assess right ventricle systolic function, size, mass, stroke volume and myocardial scarring with MRI. Other parameters such as strain and diastolic function require intense post-processing and are therefore used only in the research setting. We focus on the development of an automated motion tracking program to assess the RV motion.

The advent of real-time 3D echocardiography allows better quantification of size, shape and function of the heart. Although the 3D echocardiography has potential benefits in patient care in a number of ways, including pre- and post-surgical planning, its application is restricted by the limited field-of-view.  This limitation can be overcome by fusing multiple images taken from different probe positions. We estimate the probe location using a multi-camera motion tracking system and develop of an image fusion approach for better contrast and field-of-view.

The advent of real-time 3D echocardiography allows better quantification of size, shape and function of the heart. Although the 3D echocardiography has potential benefits in patient care in a number of ways, including pre- and post-surgical planning, its application is restricted by the limited field-of-view.  This limitation can be overcome by fusing multiple images taken from different probe positions. We estimate the probe location using a multi-camera motion tracking system and develop of an image fusion approach for better contrast and field-of-view.

DENSE is a non-invasive MR protocol that provides myocardial displacement mapping with excellent spatial resolution. A cine DENSE sequence provides a series of images over segments of the cardiac cycle, which facilitates analyzing the kinematics over the entire cardiac cycle. This project will assess the potential benefits of using the DENSE sequence in cardiac regional abnormality detection.

The interaction between fluids and structures in the body is vital to the function of many physiological processes, including blood flow in the cardiovascular system. Methods that allow computer simulation of fluid-structure interaction are useful in understanding these interactions. We build fluid-structure interaction simulation models for real patients using 3-D anatomical information from MRI, ultrasound, and CT. These patient-specific simulation models will be used for optimization of surgical and non-invasive interventional procedures.

The assessment of LV is often limited to systolic function and it mainly focuses on the analysis of regional wall motion or ejection fraction. However, recent clinical studies suggest that assessment of diastolic function is as important as systolic function. The diastolic function plays an important role in assessing cardiovascular abnormalities, particularly in the case of heart failure with preserved ejection fraction. We are developing automated methods to assess the LV impaired relaxation using short-axis cine MR images.

With the advent of 3D echocardiography, the functional analysis and visualization of three dimensional data has become increasingly important. The actual heart motion is a complicated combination of motions and it can only be observed accurately in 3D. This project focuses on the development of automatic algorithms for tracking myocardial boundaries, motion estimation and 3D visualization of the 3D echocardiographic data.

Heart Failure (HF) is one of the leading causes of death in the US and Canada and around the world

• 25% of patients treated for HF are re-hospitalized within 30 days
• 50% readmitted within 6 months

The main goal of this project is develop a low cost, robust, and scalable heart tele-monitoring system based on ASTUS technology capable of automatically analyzing the real-time data  using advanced machine learning algorithms:

• Automate the monitoring process from measurement, transmission, privacy, and notification for remote regions
• Support multiple sensors and gathering of a wide range of bodily measurements
• Provide  cloud-based analytics engine using advanced prediction algorithms to detect major cardiac events and to assist the physician to diagnose the patient condition remotely

This project is based on ASTUS sensor technology, a simple and effective wireless Tele-Medicine solution to remotely monitor patient’s health. The solution provides all vital physiological measurements performed by the patient himself, a relative or a nurse. Medical data are sent from a Set-Top box, a Smartphone or a Tablet to a Secure File Server via a wireless transmission (Wifi, Cellular networks, Satellites). Doctor accesses to the Secure File Server via a computer, a Tablet or a Smartphone. The doctor is instantly alerted if a value exceeds a threshold and is able to take immediate actions and send prescriptions.

This e-Health equipment is perfectly adapted to a various population such as travelers, isolated old people, residential care, chronically ill patients, and hospitalized patients at their home needing regular monitoring.

This project is in collaboration with Dr. Becher from the ABACUS Lab at the University of Alberta, Dr. Greiner from the Alberta Innovate Center for Machine Learning (AICML),  AMMI Lab, Alberta Health Services, Dr. Papadas from ASTUS Inc, CISCO Systems, Telus