A cardiac MRI scanner could soon diagnose heart disease in real time, rather than just producing images for later analysis. Current cardiovascular MRI is slow and inefficient. Patients must hold their breath during scans because heart and lung motion corrupts the data, and only a small fraction of the acquired information is actually used to reconstruct images. Clinicians then face the difficult task of interpreting multiple separate images alongside other test results, often without clear quantitative benchmarks. This fragmented process wastes time and can miss subtle signs of disease. The SmartHeart programme aims to tightly integrate image acquisition, analysis, and interpretation into a single, continuous feedback loop. Instead of a series of disconnected steps, the scanner would adapt on the fly, extracting multiple tissue parameters simultaneously and eliminating the dead time between specialised scans. The output would be a comprehensive diagnostic assessment, not just a picture. If successful, this could transform cardiovascular care. Patients would spend less time in the scanner, avoid difficult breath-holds, and receive faster, more accurate diagnoses. Clinicians would gain objective, quantitative information that works consistently across different hospitals and patient groups, enabling more confident treatment decisions.
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The vision for our research programme is to pave the way for a fundamentally different approach in which cardiovascular diseases (CVD) are diagnosed, monitored and treated: We propose to develop a diagnosis-driven "smart Magnetic Resonance (MR) scanner" that it is no longer a mere imaging device but instead becomes a highly sophisticated diagnostic tool. The output of a patient scan with the proposed smart MR scanner will not be just an image, but instead a comprehensive diagnostic assessment and interpretation of the patient's cardiovascular health/disease, enabling optimal treatment decisions for best patient outcome. The current approach to cardiovascular MR imaging (cMRI) is essentially serial: image acquisition is followed by image analysis and clinical interpretation. In addition, cardiac/respiratory motion is currently resulting in long scanning times for cMRI, with only a small fraction of the data (10-20%) being used for image reconstruction. This leads to breath-holds that are difficult to tolerate by sick patients. Furthermore, the characterization of clinically relevant tissue parameters requires the acquisition of multiple images which is inefficient. The absolute quantification of tissue parameters also remains a major technical challenge, leading to difficulties in interpreting tissue contrast parameters across scanners, clinical centres and patient populations. Finally, the objective interpretation of comprehensive, multi-parametric cMRI in the context of other complex non-imaging data is highly challenging for clinicians. We propose a transformative approach in which acquisition, analysis and interpretation are tightly coupled, with feedback between the different stages in order to optimize the overall objective: Extracting clinically useful information. Developing such an integrated approach to cardiac imaging will enable rapid, continuous and comprehensive imaging that is both simpler and more efficient than current practice, eliminating "dead time" between separate specialized acquisitions and allowing extraction of multiple dynamic as well as tissue contrast parameters simultaneously.
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