Synergistic Inverse Problems Omni-Solver for Expeditious High Quality Multimodal Cardiovascular MRI via Deep Compressive Sensing and Data Coalescing

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Cardiovascular disease is the most common cause of illness and death worldwide. In the UK, it accounts for about 25% of all deaths, and it costs the NHS roughly 9 billion pounds each year. Medical imaging is used to screen and diagnose for disease and to plan and monitor treatment. Cardiovascular magnetic resonance (CMR) is a safe technique that allows detailed non-invasive imaging of the structure and function of the heart without using X-rays, which may increase the risk of cancer slightly. How the images are acquired - using different 'sequences' (or multimodal CMR) - is very flexible and can change the information content of the images to highlight 'biomarkers' of disease. These image biomarkers can allow earlier diagnosis of disease and better treatment planning.A typical CMR study lasts about an hour and can take several more hours to analyse, often requiring the reporting clinician to manually identify and outline structures of interest using a computer mouse. While many types of image can be acquired in a short period of breath-holding, highly detailed images with greater coverage can take 5-10 minutes to complete and image quality can be reduced by poor respiratory motion control which affects the reliability and usefulness of the image biomarkers. Increasing the speed of acquisition while maintaining or improving image quality together with a rapid, reproducible and fully automatic analysis of the resulting images will lead to the development of new biomarkers and improve the reliability of existing ones.In this fellowship proposal, I will work on methods to speed up multimodal CMR imaging by factors up to 12 (depending on the imaging sequence) by using an advanced 'deep learning' based signal processing approach. Deep learning is a new technique that teaches computers to do what comes naturally to humans: to learn by example. This is achieved by using fast computers working in parallel with 'big data' and has produced impressive results in different applications. I will then improve the quality of the CMR images obtained with a new 'transfer learning' technique that teaches a computer to accomplish a given task by using the model it used to do a similar previous one. A 'data coalescing technique' will be devised to take advantage of available 'big data'. In addition, I will develop and implement a fully automatic software pipeline to analyse the resulting images, extracting the cardiac anatomy and image biomarkers in ~2 minutes (speed-up factor of ~100). The methods will be developed using existing CMR images and will be implemented on our commercial CMR scanners for prospective testing in healthy volunteers and patients including those with myocardial infarction (heart attack), atrial fibrillation (fast irregular heart rhythm) and congenital heart disease (birth defect).I. Goals of Researchi. To speed up CMR imaging by acceleration factors up to 12 using new 'deep learning' methods.ii. To develop innovative 'transfer learning' and 'data coalescing' techniques to boost image quality by increasing the image resolution, suppressing the noise and correcting the blurry and other artefacts.iii. To investigate and implement a fully automated detection and analysis software package/pipeline for the assessment of new and reliable image biomarkers.II. Potential Benefit of Researchi. The combination of faster imaging and better image quality will allow automated analysis of image biomarkers extracted from CMR images. This will reduce the processing time by a factor of ~100, reduce clinician variability and allow better guidance and assessment of patients with cardiovascular disease.ii. Faster imaging will reduce study duration and improve the patient experience. It will also increase patient throughput, reduce waiting lists and reduce cost per study.iii. The techniques developed will be widely applicable to all types of CMR image, to other patient groups and also to other types of medical imaging.