FOR 5534: Fast Mapping of Quantitative MR-biosignatures at Ultra-high Magnetic Field

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Magnetic resonance imaging (MRI) at ultra-high field (UHF) strengths, such as 7 Tesla, offers unique possibilities for non-invasive tissue characterization. Nevertheless, clinical applications are currently still rare, and present clinical research studies mostly focus on morphological imaging. Advanced tissue contrasts, such as chemical exchange saturation transfer (CEST), X-nuclei MRI and microstructural imaging, have already provided valuable information beyond morphology. The combined application of these MRI contrasts would provide a sound basis for highly insightful multispectral MRI. However, obtaining such an MR-signature scan is currently limited by long acquisition times, poor data quality due to radiofrequency field inhomogeneities, patient motion and the increasing difficulty of interpreting the large amount of complex multispectral data. To fully unleash the potential of 7T MRI, we aim to establish “MR biosignature imaging” augmenting morphological imaging. For this purpose, we will first establish the methodological base by developing complementary fast MR techniques for the unique non-invasive characterization of different tissues, their chemical composition and their microstructure. To turn MR-signatures into pathology-specific MR biosignatures for non-invasive tissue characterization, we will use three clinical research applications. We expect that the MR biosignatures, once established, will reveal early signs of neuro-degeneration, tissue degeneration in chronic diseases and provide insight into cancer risk factors. We are convinced that such an MR biosignature scan would provide a more comprehensive insight into disease processes than the sum of the individual contrasts. To achieve these goals, we will unify the efforts of MRI physicists, engineers, data scientists as well as clinicians. To enable the acquisition of high MR data quality, ‘smart’ hardware will be developed that combines radiofrequency (RF) coil technology with multiple receive and transmit elements as well as integrated multimodal RF coil load- and radar-based motion tracking technology. Data science will be employed to identify the most important data features of the MR-signature scan and to accelerate data acquisition. This research unit (RU) will build on a strong research environment and infrastructure in Erlangen. At the Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), outstanding research groups in the field of data science, machine learning and electrical engineering will contribute by working closely with the researchers of the University Hospital Erlangen (UKER), i.e. with three recently established research groups focusing on novel MR contrasts and UHF MRI, and with collaborating clinical researchers. A dedicated clinical 7T system will be used, offering unique possibilities for combining cutting-edge technological and clinical research.