Advanced computed tomography (CT) allows for amazing visualization of the human body including the beating heart. However, the complex interactions of blood flow, which is crucial in the diagnosis and treatment planning of many diseases, are not fully reflected by these images. Magnetic resonance imaging (MRI) and ultrasound are able to measure functional data like blood flow, but at a low resolution. Furthermore, these techniques are not able to predict the changes in blood flow after surgical treatment.
Putting Together the Team
Professor Tino Ebbers has long experience in imaging of the cardiovascular system, mainly using MRI. For the 4D Flow CT project he changed imaging technique to computed tomography (CT). He brought on board Professor Anders Persson who is world leading in CT imaging of the heart, for flow mechanics and simulation, Professor Matts Karlsson who is an expert in this field, and Professor Carl-johan Carlhäll, for physiological knowledge.
-This project is really a collaboration. When we combined our three specialties we had all the knowledge we needed on site. Adding the computer power at National Supercomputer Centre we fast became world leading in the field, Tino says.
The research group aims to extract blood flow data from CT images of the heart using image-based simulations. The goal is earlier and more accurate detection as well as improved management of cardiac diseases.
From Simple Models to In Vivo Measurements
Simulation of blood flow in the whole heart has so far mainly focused on exploration in simplified 3D models and validation with in vivo measurements are few. Even though many forms of functional imaging data and modelling approaches are currently available, a gap persists between modelling and experimental research.
-You need to have all the knowledge from simulation to clinical experience to succeed in creating something clinically useful. And you need to be able to communicate to combine technology with medicine, Tino explains.
They have bridged the gap by developing and evaluating an approach in which intracardiac flow fields are computed based on patient-specific high-resolution cardiac CT data. The heart is segmented at one cardiac phase, and advanced registration techniques are used to track the heart wall. Using computational techniques usually employed by the automotive or aerospace industry, detailed intracardiac and vascular blood fields are obtained.
-Recently we have showed that our 4D Flow CT method can produce blood-flow patterns that are qualitatively and quantitatively similar to the current reference standard 4D Flow MRI.
While 4D Flow MRI can obtain accurate intracardiac flow fields, the use of computer tomography data allows for studying patient groups that cannot be studied using magnetic resonance imaging. The high resolution also allows the simulated data to reveal processes that couldn’t be studied before, like the coagulation of blood or the occurrence of turbulence in the blood flow.
-One clinical application that we are exploring is in atrial fibrillation. The patients have an increased risk of blood cloths forming in the atrium and by migrating to the brain or coronary arteries they may induce a stroke or heart attack. We are building a model that can identify where the blood cloths are forming. The goal is that the information from our model may be used to identify patients at risk, Tino continues.
The simulation-based approach potentially allows for studies of what-if scenarios where different treatment options can be explored.
-With the CT data we can simulate what will happen if we change something. For example, if we simulate the exchange of a defect heart valve we can predict how it will affect the flow. This could be used to plan heart surgery in the future.
However, the heart is complex and adapts to changes. If the flow is inefficient due to a stenosis or a defect valve the heart will be enlarged to compensate. After a surgical intervention the heart will adjust to normal size. The model has to take this into account.
-The model is a simplified version of reality and we have to find a balance in how much information to include to come close enough to make it clinically useful, Tino concludes.