Magnetic resonance imaging (MRI) has transformed the way we look through the human body by offering exquisite soft-tissue contrast in high-resolution images, noninvasively. This has made MRI the gold-standard imaging technique for diagnosis and monitoring of many diseases. However, conventional MRI scans do not produce "quantitative" measurements, i.e. standardised measures, and therefore it is difficult to compare MRI images acquired at different hospitals, or at different points in time, limiting the potential of this imaging technology for advanced diagnostic and monitoring precision. Quantitative MRI (qMRI) aims to overcome this problem by yielding reproducible measurements that quantify tissue bio-properties, independent of the scanner and scanning times. This could transform the existing scanners from picture-taking machines to scientific measuring instruments, enabling objective comparisons across clinical sites, individuals and different time-points. But unfortunately qMRIs have excessively long acquisition times which currently create a major obstacle for their wide adoption in clinical routines. Therefore, the main goal of this project is to develop new computational methodologies based on compressed sampling and machine learning that will substantially reduce the scan times of qMRI. Compressed sampling techniques enable efficient acquisition of signals and images from tightly constrained sensor/imaging systems. They have been recently applied to address the issue of scan time in qMRI, but these techniques require much better computational methods for removing image compression artefacts at higher acceleration (compression) rates needed for this application. The project aims to address this gap through advanced machine learning-based models and appropriately chosen datasets to train them. The research has two streams of beneficiaries: (i) A large community of UK and international clinical academics that use qMRI techniques for their research on precision imaging and evaluation of diseases such as cancer, cardiac or neurodegenerative disorders, each with significant socioeconomic impact. The outcomes of this project would allow these studies to become more available and more economically feasible. (ii) A large community of UK and international non-clinical academics/professionals who work on compressed sampling inverse problem techniques, motivated by variety of other sensing/imaging applications that could benefit in their studies from methodologies developed by this project. A number of activities have been carefully designed to effectively engage with beneficiaries of this research. These activities include co-production and validation of knowledge with clinical academics and healthcare industry as our project partners, publishing of the results in leading academic journals/conferences, a project website to publicize up-to-date project advances and share open-source software and demonstrators, and a workshop with field specialists and national academic and non-academic stakeholders in medical technologies.

4D flow Magnetic Resonance Imaging (flow-MRI) is a non-invasive flow imaging technique widely used in medicine and engineering to measure velocity fields in three spatial and one time dimension (4D). For example, it is used to measure the velocity of blood in the heart and surrounding vessels to identify anomalies such as aneurysms and stenoses. The velocity measurements become increasingly noisy, however, as the spatial resolution is increased. To achieve acceptable signal-to-noise ratio (SNR), scans are often repeated and averaged, leading to long acquisition times. This proposal is to extend the capabilities of flow-MRI using Bayesian physics-constrained algorithms that automatically generate the most likely digital twin of a flow from noisy and sparse 4D flow-MRI data. These methods increase the accuracy and the spatiotemporal resolution of flow-MRI by 10 to 100 times, provide quantitative estimates of derived flow quantities that are difficult to measure, and enable the imaging of flows whose short length and/or time scales cannot be captured using state-of-the-art flow-MRI techniques. In porous media flows, for example, these methods will provide velocity fields, stress tensors, and derived quantities far beyond the accuracy of current state-of-the-art flow-MRI, leading to better understanding and new discoveries. In medical imaging, these methods will also enable patient-specific modelling and, if successful, will lead to increased adoption of 4D flow-MRI by clinicians. This would reduce patient scan times, replace invasive techniques such as cardiac catheterization, and permit the imaging of smaller vessels such as those found in neonatal and fetal cardiology. In my PhD I developed these methods and showed that, to obtain a given accuracy in axisymmetric and 2D planar flows, they reduce the required flow-MRI data by 10 to 100 times. The aim of this fellowship is to extend the methods I developed during my PhD from 2D and 3D steady flows in rigid geometries to 4D flows in flexible geometries, to scope out challenges posed by in-vivo cardiovascular haemodynamics, and to disseminate these methods widely. In this proposal I will focus on flow-MRI, but note that these methods could be extended to other velocimetry methods such as PIV.

Context: Testing how well the lung "functions" usually involves the use of breathing tests. However, these tests are extremely difficult to do reliably and accurately in newborn babies and infants. We need new imaging techniques that can help visualise the best and worst functioning areas of the lungs. In adults, x-ray and computed tomography (CT) imaging is often used to study the lungs. However, these methods pose an increased harmful radiation risk to newborns and infants. In addition, the function of the heart is normally measured by invasive methods that are not safe for newborns and infants, or echocardiography, which is technically challenging in these populations. As a result, our knowledge of newborn and infant diseases of the lung and heart is collectively poor compared to that of adolescents and adults. In particular, lung and heart problems in babies born pre-term are the major cause of death, yet remain not well understood. Objectives: The main purpose of this research is to develop safe, robust methods for imaging the lungs and heart in newborns and infants to better understand and manage debilitating diseases, in particular those related to pre-term birth. We will use magnetic resonance imaging (MRI); a safe imaging method that poses no harmful radiation risk to newborns and infants. The main objectives are as follows: - Develop MRI methods to investigate how diseases affect the lungs and heart in newborns and infants: -- Develop software to control the MRI scanner to obtain the best quality images that inform us about the structure and function of the lungs and heart in newborns and infants. -- Develop MRI hardware that is comfortable for newborns and infants and helps to improve image quality. - Test how well our developed methods and technology can detect changes to the structure and function of the lungs and heart in newborns and infant lung diseases, including diseases related to premature birth. -- Measure how well these methods can detect the causes for changes in patient's health over time as disease progresses. My research will be carried out at the University of Sheffield, a world-leading institution in MRI technique development with a unique interdisciplinary balance of scientists and clinicians to ensure that technological developments lead directly to NHS and patient benefit. Potential Applications & Benefits: The long-term benefit of this research is the potential to change the way lung and cardiac disease is managed in newborns and infants and improve patient quality-of-life. In particular, the methods we develop will help identify early signs of disease that cannot easily be identified by other methods. In addition, MRI is safe, and scanning can be repeated often to monitor disease progress or visualise the changes due to treatment. This cannot be done with CT, and will aid our understanding of diseases and help identify new ways they can be treated. We will develop these techniques for whole-body MRI scanners, of the sort available in most hospitals, which will increase accessibility of the technique to NHS clinicians nationally.