Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 7 1 2020 03 30 3 13 EN Elmira Yazdani Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran Sajjad Aghabozorgi Sahaf Department of Energy Engineering, Sharif University of Technology, Tehran, Iran Hamidreza Saligheh Rad Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran 2019 11 29 2020 01 16 Purpose: Magnetic Resonance Fingerprinting (MRF) is a novel framework that uses a random acquisition to acquire a unique tissue response, or fingerprint. Through a pattern-matching algorithm, every voxel-vise fingerprint is matched with a pre-calculated dictionary of simulated fingerprints to obtain MR parameters of interest. Currently, a correlation algorithm performs the MRF matching, which is time-consuming. Moreover, MRF suffers from highly undersampled k-space data, thereby reconstructed images have aliasing artifact, propagated to the estimated quantitative maps. We propose using a distance metric learning method as a matching algorithm and a Singular Value Decomposition (SVD) to compress the dictionary, intending to promote the accuracy of MRF and expedite the matching process. Material and Methods: In this investigation, a distance metric learning method, called the Relevant Component Analysis (RCA) was used to match the fingerprints from the undersampled data with a compressed dictionary to create quantitative maps accurately and rapidly. An Inversion Recovery Fast Imaging with Steady-State (IR-FISP) MRF sequence was simulated based on an Extended Phase Graph (EPG) on a digital brain phantom. The performance of our work was compared with the original MRF paper. Results: Effectiveness of our method was evaluated with statistical analysis. Compared with the correlation algorithm and full-sized dictionary, this method acquires tissue parameter maps with more accuracy and better computational speed. Conclusion: Our numerical results show that learning a distance metric of the undersampled training data accompanied by a compressed dictionary improves the accuracy of the MRF matching and overcomes the computation complexity. https://fbt.tums.ac.ir/index.php/fbt/article/view/234 https://fbt.tums.ac.ir/index.php/fbt/article/download/234/149