The broad aim of our research project is to develop new techniques to improve the quality of Magnetic Resonance Imaging (MRI). Our project specifically looks at this in relation to imaging the heart and surrounding area (cardiac imaging). MRI is a safe diagnostic tool providing good images of soft tissue organs and is used routinely for the imaging of static structures such as the brain. However it is not yet widely used for cardiac imaging because MRI is very sensitive to motion. The movement of the beating heart reduces image quality producing blurring and ghosting. A similar effect occurs when someone moves while you are taking their photo, though the mathematics of how the image is affected is different. There are two independent sources of motion: the cardiac contraction (heart beating) and respiratory motion (breathing). It is possible to compensate for the motion associated with cardiac contraction, but respiration is less predictable and consequently harder to compensate. Most cardiac MRI studies are therefore done while the patient holds their breath. Naturally there are limits to how long patients can do this for and therefore the quality of the images can be compromised. A typical cardiac MRI exam requires multiple breath holds and it has to be planned very carefully to ensure that the necessary images are acquired. This planning and the subsequent image analysis require highly trained staff and these are not available at most sites. This is one of the main obstacles for the widespread use of cardiac MRI. In this proposal we aim to develop new, faster and easier ways of acquiring cardiac MR images. We aim to do this by replacing existing 2D methods with 3D techniques. The advantage of this approach is that the whole heart can be imaged during a single acquisition and very little planning is required. To achieve this it is necessary to overcome some of the problems associated with respiratory motion so that the images can be acquired without the patient having to hold their breath. During the acquisition process the motion of the heart due to respiration will be measured and these motion measurements will then be taken into account when forming the images. We will take advantage of the fact that new MRI scanners can now acquire multiple chunks of data (using multiple receive devices) at the same time ( highly parallel imaging ). This enables acceleration of the acquisition and it can provide complementary information about motion. This new technology will complement and facilitate our research in several ways. For example, we will be receiving more information about the effects of the motion on the image thus allowing us to better correct the images. In certain situations, there is a limited amount of overall scan time available for the MR examination. This includes acquisitions where a contrast agent (dye) is injected into the patient. In order to make the best use of the time available for this type of scan we want to avoid acquisition of redundant information. To accomplish this it is necessary to rely on prior information about the movement of the heart and this can potentially degrade the reconstructed images. In this proposal we wish to investigate how we can rely less on prior information, this will involve complicated mathematics to determine the optimal balance between prior information and parallel imaging principles.In summary, the purpose of this proposal is to develop new MR imaging strategies that allow 3D imaging of the heart during free-breathing and under time-constraints. The developed imaging sequences will be tested on both volunteers and patients.

The broad aim of our research project is to develop new techniques to improve the quality of Magnetic Resonance Imaging (MRI). Our project specifically looks at this in relation to imaging the heart and surrounding area (cardiac imaging). MRI is a safe diagnostic tool providing good images of soft tissue organs and is used routinely for the imaging of static structures such as the brain. However it is not yet widely used for cardiac imaging because MRI is very sensitive to motion. The movement of the beating heart reduces image quality producing blurring and ghosting. A similar effect occurs when someone moves while you are taking their photo, though the mathematics of how the image is affected is different. There are two independent sources of motion: the cardiac contraction (heart beating) and respiratory motion (breathing). It is possible to compensate for the motion associated with cardiac contraction, but respiration is less predictable and consequently harder to compensate. Most cardiac MRI studies are therefore done while the patient holds their breath. Naturally there are limits to how long patients can do this for and therefore the quality of the images can be compromised. A typical cardiac MRI exam requires multiple breath holds and it has to be planned very carefully to ensure that the necessary images are acquired. This planning and the subsequent image analysis require highly trained staff and these are not available at most sites. This is one of the main obstacles for the widespread use of cardiac MRI. In this proposal we aim to develop new, faster and easier ways of acquiring cardiac MR images. We aim to do this by replacing existing 2D methods with 3D techniques. The advantage of this approach is that the whole heart can be imaged during a single acquisition and very little planning is required. To achieve this it is necessary to overcome some of the problems associated with respiratory motion so that the images can be acquired without the patient having to hold their breath. During the acquisition process the motion of the heart due to respiration will be measured and these motion measurements will then be taken into account when forming the images. We will take advantage of the fact that new MRI scanners can now acquire multiple chunks of data (using multiple receive devices) at the same time ( highly parallel imaging ). This enables acceleration of the acquisition and it can provide complementary information about motion. This new technology will complement and facilitate our research in several ways. For example, we will be receiving more information about the effects of the motion on the image thus allowing us to better correct the images. In certain situations, there is a limited amount of overall scan time available for the MR examination. This includes acquisitions where a contrast agent (dye) is injected into the patient. In order to make the best use of the time available for this type of scan we want to avoid acquisition of redundant information. To accomplish this it is necessary to rely on prior information about the movement of the heart and this can potentially degrade the reconstructed images. In this proposal we wish to investigate how we can rely less on prior information, this will involve complicated mathematics to determine the optimal balance between prior information and parallel imaging principles.In summary, the purpose of this proposal is to develop new MR imaging strategies that allow 3D imaging of the heart during free-breathing and under time-constraints. The developed imaging sequences will be tested on both volunteers and patients.

The broad aim of our research project is to develop techniques that use medical images and computers to help doctors to improve the treatment of patients with certain types of long-term heart problems. Doctors can not only use medical imaging, such as x-ray and magnetic resonance imaging (MRI), to look inside the body to detect disease but can also use imaging to guide the treatment of disease. Our project specifically concerns the treatment of patients with an irregular heartbeat, called an arrhythmia. This is a common problem affecting 3-5% of people that are over 40 years old and puts these patients at an increased risk of serious problems, such as stroke. Many famous people, such as Tony Blair and Alex Ferguson, have suffered from arrhythmias. Doctors commonly treat these patients with pills that try to prevent the arrhythmia. The patients must take the pills for the rest of their lives since this is not a cure. Moreover, as with any medication, there are unwanted side effects that can be difficult for the patient to tolerate. More recently, doctors are using a new technique to try to cure these patients. The irregular heart rhythm is thought to be caused by abnormal areas within the heart itself. It is possible to destroy these areas, and therefore, to cure the problem by applying a small burn within the heart called an ablation. Doctors apply these burns without actually cutting the patient open by using wires called catheters that are inserted through blood vessels in the legs and threaded up into the heart. These new techniques are very promising, but are less successful than Doctors would like. For some types of disease, between 30% and 40% of patients are no better after the ablation. Doctors use x-ray images to help them see inside the body so that they can place the catheters in the correct position in the heart. X-rays are very penetrating and pass through the body easily. However, the catheters block the x-rays so that they appear different to the patient's body in the x-ray pictures. Therefore, the doctors can see the catheters easily but cannot see the patient's heart on the x-ray pictures. It is thought that one reason that these arrhythmia treatments are unsuccessful is that the doctor cannot see the patient's heart clearly. Another reason, is that the x-rays are only two dimensional shadows of the patient, and don't give any information about depth. MRI and x-ray CT imaging on the other hand has the ability to make three-dimensional pictures of the heart without the need for any harmful x-rays. In our project we aim to allow doctors to use the three-dimensional images of the heart from MR or CT imaging to help to place the catheter inside the heart. Our technique combines MR or CT pictures with x-ray pictures to allow easier treatment of patients with arrhythmias. We will focus on accurate combination of these two different types of images. We will also overlay electrical measurements made inside the heart with this image information. This integrated image and electrical information can be used to build a computerized model that simulates the heart. Just as engineers can create a computer model of a bridge or aeroplane before it is actually built, so we will create a computer model of the human heart. Heart models have been created before, but we will generate models customized to each individual patient, which we will be able to test using measurement made during the treatments. In this project we will use these models to help doctors plan treatments before actually carrying them out on patients.

The broad aim of our research project is to develop techniques that use medical images and computers to help doctors to improve the treatment of patients with certain types of long-term heart problems. Doctors can not only use medical imaging, such as x-ray and magnetic resonance imaging (MRI), to look inside the body to detect disease but can also use imaging to guide the treatment of disease. Our project specifically concerns the treatment of patients with an irregular heartbeat, called an arrhythmia. This is a common problem affecting 3-5% of people that are over 40 years old and puts these patients at an increased risk of serious problems, such as stroke. Many famous people, such as Tony Blair and Alex Ferguson, have suffered from arrhythmias. Doctors commonly treat these patients with pills that try to prevent the arrhythmia. The patients must take the pills for the rest of their lives since this is not a cure. Moreover, as with any medication, there are unwanted side effects that can be difficult for the patient to tolerate. More recently, doctors are using a new technique to try to cure these patients. The irregular heart rhythm is thought to be caused by abnormal areas within the heart itself. It is possible to destroy these areas, and therefore, to cure the problem by applying a small burn within the heart called an ablation. Doctors apply these burns without actually cutting the patient open by using wires called catheters that are inserted through blood vessels in the legs and threaded up into the heart. These new techniques are very promising, but are less successful than Doctors would like. For some types of disease, between 30% and 40% of patients are no better after the ablation. Doctors use x-ray images to help them see inside the body so that they can place the catheters in the correct position in the heart. X-rays are very penetrating and pass through the body easily. However, the catheters block the x-rays so that they appear different to the patient's body in the x-ray pictures. Therefore, the doctors can see the catheters easily but cannot see the patient's heart on the x-ray pictures. It is thought that one reason that these arrhythmia treatments are unsuccessful is that the doctor cannot see the patient's heart clearly. Another reason, is that the x-rays are only two dimensional shadows of the patient, and don't give any information about depth. MRI and x-ray CT imaging on the other hand has the ability to make three-dimensional pictures of the heart without the need for any harmful x-rays. In our project we aim to allow doctors to use the three-dimensional images of the heart from MR or CT imaging to help to place the catheter inside the heart. Our technique combines MR or CT pictures with x-ray pictures to allow easier treatment of patients with arrhythmias. We will focus on accurate combination of these two different types of images. We will also overlay electrical measurements made inside the heart with this image information. This integrated image and electrical information can be used to build a computerized model that simulates the heart. Just as engineers can create a computer model of a bridge or aeroplane before it is actually built, so we will create a computer model of the human heart. Heart models have been created before, but we will generate models customized to each individual patient, which we will be able to test using measurement made during the treatments. In this project we will use these models to help doctors plan treatments before actually carrying them out on patients.

Atrial Fibrillation (AF) is an irregular heart rhythm that affects ~1 million people in the UK. It increases the risk of other cardiovascular diseases including heart failure, stroke and death. Patients who do not respond to drug therapy may be treated using radio frequency catheter ablation treatment, which aims to isolate areas of pathological tissue responsible for AF. In more advanced AF patients, treatment is sub-optimal with 40% of patients suffering from AF recurrence at 18-month follow-up. For these patients, 2-3 repeat procedures may be required. Different ablation approaches are used by different clinical centres and different clinicians, with varying degrees of personalisation to the individual patient anatomy, electrical properties and history. In the current state of the art, there are large clinical population datasets available that inform treatment approaches for the average patient within a population. In parallel to this, there are also increasingly more detailed patient-specific models available. However, these approaches are typically disjoint. My vision is that we link information measured across a population to patient-specific models for predictive treatments. Tailoring ablation therapy to the individual patient, using data collected across a population, will improve long-term outcome and reduce recurrence, so that patients require fewer and shorter procedures. Cardiac electrical signal mapping and imaging systems provide large quantities of spatial and temporal measurements for characterising the atria across populations of patients. These data can be used for constructing computational biophysical models of the atria, which in turn provide a physiological and physics constrained framework for investigating AF properties in personalised patient-specific models. Studying large virtual patient cohorts of these biophysical models can provide important insights into AF treatment approaches. However, these computer models run too slowly to be used during clinical procedures, and currently only capture what happens immediately after the treatment, and not the long-term response (for example, a year after the procedure). Machine learning techniques can capture complex relationships and generate fast predictions. To overcome the challenge of predicting the long-term response within a clinical timeframe, I will train a machine learning network to the large virtual patient cohort and clinical datasets to quickly predict treatment outcome from patient imaging and electrical data. I will also use imaging datasets that show how the structure of the atria changes during the months following the procedure. Updating the model (or digital twin) with these measurements will improve the ability of the model or network to predict the long-term outcome for the patient. This project will move models from the research environment to clinical applications. These methodologies will be combined into a clinical tool that processes imaging and electrical measurements to map population therapy outcomes to patient-specific predictive treatments during the clinical procedure. The tool will take imaging and electrical data for a patient, and output different ablation therapy approaches together with how likely they are to reduce AF recurrence (and improve outcome), to aid patient-specific treatment planning. This project has clinical and industrial project partners to enable clinical translation of the technology. Use of the methodology developed during this fellowship may lead to better treatment selection, and decreased time and cost for AF catheter ablation procedures.