MRI is a key methodology in modern neuroimaging, but conventional MRI relies on visual interpretation of intensity differences in the images, which is heavily dependent on scanner settings. Quantitative MRI (qMRI) is an attractive alternative MRI method that allows quantitative measurement of physical tissue parameters, enabling objective comparison between patients and across time. Moreover, qMRI facilitates early detection of pathological changes in the brain resulting from neurological disorders such as multiple sclerosis. Unfortunately, and despite the demonstrated potential in research settings, the implementation of qMRI in routine clinical practice remains limited due to long scan and post-processing times. While recent developments in artificial intelligence have the potential to accelerate and improve medical imaging pipelines, reduced transparency about the underlying processes, the lack of training data sets and limited information about the accuracy of the results has limited its use for clinical qMRI applications so far. In IQ-BRAIN, we propose a unique research and training programme that tackles this urgent need for improved and accelerated qMRI methodology for neuroimaging applications. By integrating both physics-based models and trustworthy artificial intelligence methods along the qMRI pipeline, our innovative approach combines the best of both worlds. IQ-BRAIN will prepare the next generation of qMRI specialists trained in the different aspects of the qMRI-neuroimaging pipeline, that can bridge the gap between qMRI method development and clinical need. Through a training programme of network-wide events, international secondments, and strong interaction between partners from academia, industry and hospitals, IQ-BRAIN offers early-stage researchers a rich combination of knowledge, expertise and essential transferable skills that prepares them for a thriving career as R&D professionals in the qMRI field.

Cardiovascular Diseases (CVDs) are the number 1 cause of death globally: more people die annually from CVDs than from any other cause. Despite emerging diagnostics tools and therapeutics, several areas of significant unmet need remain unaddressed among CVD patients. The ability to personalize cardiovascular medical care and improve outcomes, will require characterization of disease processes at a molecular level. The current state-of-the-art, e.g., Positron emission tomography (PET), does not provide detailed information about the chemical state of the tissue at a molecular level, therefore it remains difficult to accurately diagnose and confidently select appropriate therapy in many circumstances. The MetaboliQs project brings together two areas of European excellence - diamond-based quantum sensing and medical imaging. We will translate a newly developed hyperpolarization method for magnetic resonance imaging (MRI) based on the quantum dynamics of nitrogen-vacancy (NV) centers. This breakthrough quantum technology will enable previously unachievable, highly sensitive quantification of metabolic activity, paving the way for precision diagnostics and better personalized treatment of cardiovascular and other metabolic diseases. For realizing and eventually commercializing the technology, MetaboliQs brings together a world-class multidisciplinary consortium with end to end expertise - leading diamond quantum technology research institutes (Fraunhofer IAF - quantum-grade diamond growth and fabrication, HUJI - quantum sensing) and innovative companies (Element 6 - worldwide leader in synthetic diamonds, NVision - inventor of diamond-based polarization), as well as two expert users of hyperpolarized and cardiovascular MRI (TUM, ETH Zurich - first in continental Europe to conduct clinical trials of hyperpolarized MRI for cardiovascular disease) and the market leader in electron paramagnetic resonance and preclinical MRI (Bruker).

Our current understanding of the relationship between diet and the development of non-communicable disease (NCD) is limited by a number of factors. These include a lack of understanding of dietary mechanisms that drive NCD, inaccurate tools to collect dietary information, a nascent understanding of the role of personalised nutrition, and the lack of data in vulnerable groups where NCDs are often over-represented. The overarching aim of CoDiet is to develop a series of tools (through eight work packages) which will address the current gaps in our knowledge and lead to the development of a tool that will assess dietary-induced NCD risk. We will achieve this through the six objectives which will answer the challenges of the work programme 1: Development of AI-driven literature searching tools - bring clear understanding of large global literature in the field of physiological and metabolic links between diet and NCD 2: Enhance the understanding of NCD risk factors - we will bring a series of beyond the state of the technics to gain mechanistic insight 3: Understanding of the importance individual variation in response to diet to risk of NCD - this will give insight into the targeting of dietary NCD advice 4: Develop an enhanced method of dietary assessment using machine learning technologies - solving a fundamental problem in nutrition of lack of an accurate dietary tool 5: Develop an enhance diet-NCD monitoring tool - enabling change in NCD in response to diet to be monitored at the population level 6: Develop a dynamic interface between diet and NCD risk factor monitoring and policy - Ensuring CoDiet is applicable at a population level The investigation of these objectives and the answers they provide will open a pathway to enhancing the uptake of NCD protective diet at a population level