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Magnetic resonance (MR) imaging technologies provide unique capabilities to probe the mysteries of biological systems, and have enabled novel insights into anatomy, metabolism, and physiology in both health and disease. However, while MRI is decades old, is associated with multiple Nobel prizes (in physics, chemistry, and medicine), and has already revolutionized fields like medicine and neuroscience, current MRI methods are still very far from achieving the full potential of the MRI signal. In particular, traditional methods are based on classical sampling theory, and suffer from fundamental trade-offs between signal-to-noise ratio, spatial resolution, and data acquisition speed. These issues are exacerbated in high-dimensional applications, due to the curse of dimensionality. Our work addresses the limitations of traditional MR imaging using signal processing approaches that are enabled by modern computational capabilities. These approaches are possible because of certain "blessings of dimensionality," e.g., that high-dimensional data often possess unexpectedly simple structure that can be exploited to alleviate classical barriers to fast high-resolution imaging. This seminar will describe approaches we have developed that use novel constrained imaging models to guide the design of new MR data acquisition and image reconstruction methods, and enable substantial acceleration of both low-dimensional and high-dimensional MR imaging experiments. [more]

Please join this link: https://zoom.us/j/8868284415?pwd=NmpZRjZsRnRvU0NJMDBTUERzc2x0Zz09 [more]

Organized by the Open Science Initiative at the Max Planck Institute for Human Cognitive and Brain Sciences, this is a space to discuss various aspects related to open and reproducible research practices, from general issues to specific applications, presenting both enthusiastic and critical perspectives. [more]

Join online: https://zoom.us/j/95065830000 [more]

Join online: https://zoom.us/j/95065830000 [more]

MRI signal generation is substantially influenced by factors such as water content, iron, myelin, and several other contributors. Iron levels can be directly assessed using mass spectrometry, while the quantitative impacts of myelin's structure and composition remain unknown to a certain extent and are often inferred from theoretical simulations. Additionally, MRI relaxation rates and susceptibility are sensitive to these tissue constituents, but their specificity is limited. In this context, post-mortem investigations utilizing complementary methods such as TEM, LA-ICP-MS, MALDI-MSI, CARS, and SAXS-TT provide unique insights for the validation and understanding of quantitative MRI parameters. However, in-situ post-mortem MRI has to accommodate for factors like variable temperature, deoxygenated blood, and perfusion. Furthermore, the process of formalin fixation introduces a significant confounder, often obstructing direct conclusions. In this presentation, I aim to summarize our work on translating post-mortem MRI findings to in-vivo conditions, outline the analytical methods used to assess brain tissue structure and composition, and discuss potential collaborations with the MPI CBS. [more]