In medical imaging, Magnetic Resonance Fingerprinting (MRF) has emerged as a promising approach to perform fast quantitative Magnetic Resonance Imaging (QMRI). In the case of brain imaging, QMRI provides invaluable insights into brain tissue, aiding in medical diagnosis and personalized treatment. However, existing QMRI methods suffer from slow processing speeds. MRF provides an alternative QMRI framework to derive quantitative values simultaneously but also faces hurdles. “Previous MRF methods weren’t good at generalization,” Juyeon tells us. “Different hospitals use different settings for their medical equipment. It doesn’t work well when you develop a model using one hospital’s dataset and apply it to another. Our goal is to build a really generalizable model.” She introduces a novel concept to address this challenge: the physicsinformed decoder. Her framework, known as BlochNet, has a supervised encoder-decoder architecture, where the encoder predicts essential quantitative values (T1 and T2 values) from the input signal, and the decoder uses MRI physics, specifically Bloch equations, to reconstruct it. 3 DAILY MICCAI Wednesday Physics-based Decoding Improves Magnetic Resonance Fingerprinting Juyeon Heo is a PhD student at the University of Cambridge. In this work, she proposes a solution for the Magnetic Resonance Fingerprinting (MRF) problem. She speaks to us ahead of her poster this morning. Juyeon Heo
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