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The ongoing paradigm shift in healthcare towards personalized and precision medicine is posing a critical need for noninvasive imaging technology that can provide quantitative tissue and molecular information. Magnetic resonance signals from biological systems contain information from multiple molecules and multiple physical/biological processes (e.g., T1 relaxation, T2 relation, diffusion, perfusion, etc.). So, magnetic resonance imaging (MRI) is inherently a high-dimensional imaging technology that can acquire structural, functional and molecular information simultaneously. In practice, due to the curse of dimensionality, MRI experiments are often done in a low-dimensional setting to acquire biomarkers one at a time. Such a “divide-and-conquer” approach not only reduces data acquisition efficiency but also makes it difficult to obtain molecular information in high resolution. By synergistically integrating machine learning with sparse sampling, constrained image reconstruction and quantum simulation, we have successfully demonstrated ultrafast high-dimensional imaging of the brain. This talk will give an overview of this unprecedented omni imaging technology and show some exciting experimental results of brain function and diseases.
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