3D Free-breathing MRI with High Scan Efficiency for Assessment of Cardiovascular Disease: Combining Acceleration and Motion Correction Techniques

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Coronary artery disease (CAD) is the leading single cause of morbidity and mortality in the Western world, causing over 65.000 deaths every year in England. Improving the treatment and outcome of cardiovascular disease is one of the main priorities of the National Health Service in the UK. CAD reduces the blood supply to the cardiac muscle and can lead to chest pain (angina) or heart attack. Its diagnosis is currently performed by invasive, ionizing, and potentially harmful X-ray cardiac catheterization procedures. Magnetic Resonance Imaging (MRI) is a very promising non-invasive tool for early risk assessment, guidance of therapy and treatment monitoring of CAD. Clinical research studies have shown the potential of perfusion, cardiac function, late gadolinium enhancement (LGE; infarct) and coronary MR angiography (CMRA) for the assessment of CAD. However, technical developments are still needed to allow MRI achieving similar diagnostic accuracy as the current clinical gold standard. Two major challenges in cardiovascular MRI are 1) image quality degradation due to respiratory motion, and 2) long scan times resulting from the required high-spatial resolution. Both can lead to non-diagnostic image quality and a low patient throughput resulting in high operation costs if not adequately addressed. Current developments that facilitate these problems still suffer from either long acquisition times or require acquisitions under multiple breath-holds, which can be difficult in very sick patients and lead to a reduced diagnostic accuracy.LGE and CMRA protocols are particularly affected by these limitations. LGE yields images with a high contrast between viable and fibrotic/infarcted myocardial tissue and requires high resolution to accurately measure infarct size and transmurality as it has important implications on treatment decisions and patient prognosis. Similarly high-resolution 3D acquisitions are required in CMRA to accurately visualize coronary stenosis. The most common approach to detect and correct for respiratory motion in free-breathing LGE and CMRA imaging is the use of diaphragmatic navigator echoes to gate the MR acquisition. With this approach the MR data is acquired only when the respiratory signal coincides with a predefined acceptance window of the breathing cycle (e.g. end-expiration) with all other data being rejected. Small gating windows of 3 to 5mm allow to reduce motion artifacts but lead to prolonged acquisition times, which may even result in scan abortion in patients with highly irregular breathing patterns. Current approaches often lead to long acquisition times and/or suboptimal image quality due to residual respiratory motion.In this proposal we aim to develop, implement and test the clinical feasibility of an efficient (shorter scan time) and robust (neither planning nor patient collaboration required) respiratory motion compensation framework for LGE and CMRA. This framework should allow to reduce the acquisition time of current CMRA and LGE protocols from ~10 min to less than 3 min. We hypothesize that this can be achieved by developing a new 3D approach that combines novel data sampling schemes, novel undersampling reconstruction techniques and a mathematical framework for simultaneous undersampled image reconstruction and motion correction of the free-breathing acquired data. In the near future this project will benefit the scientific research community working in the field of coronary artery imaging and myocardial tissue characterization, as well as those working on MRI reconstruction and motion correction. Moreover, it will strengthen the UK's already strong role in medical imaging research. In the long term, the beneficiaries of this research may be patients with coronary artery disease who will benefit from efficient and accurate non-invasive diagnostic tests. However, a broad clinical application would require subsequent clinical studies in a larger number of patients.