In this work, we design a deep learning approach to MRF using a fully connected network (FCN). Neural networks (NNs) have been proposed as a feasible alternative, but this approach is still in its infancy. A typical drawback of dictionary-based MRF is an explosion of the dictionary size as a function of the number of reconstructed parameters, according to the "curse of dimensionality", which determines an explosion of resource requirements. ![]() Magnetic resonance fingerprinting (MRF) is a rapidly developing approach for fast quantitative MRI. Vice Provost for Undergraduate Education.Office of Vice President for Business Affairs and Chief Financial Officer.Office of VP for University Human Resources.Stanford Woods Institute for the Environment.Stanford Institute for Economic Policy Research (SIEPR). ![]() Institute for Stem Cell Biology and Regenerative Medicine.Institute for Human-Centered Artificial Intelligence (HAI).Institute for Computational and Mathematical Engineering (ICME).Freeman Spogli Institute for International Studies.Stanford Doerr School of Sustainability.
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