Reconstruction of Simulated Magnetic Resonance Fingerprinting Using Accelerated Distance Metric Learning

Reconstruction of Simulated Magnetic Resonance Fingerprinting Using Accelerated Distance Metric Learning Publisher

Yazdani E^{1, 2} ; Sahaf SA^{1, 3} ; Rad HS^{1, 2}

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1. Quantitative Magnetic Resonance Imaging and Spectroscopy Group, Research Center for Cellular and Molecular Imaging, Tehran University of Medical Sciences, Tehran, Iran
2. Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
3. Department of Energy Engineering, Sharif University of Technology, Tehran, Iran

Source: Frontiers in Biomedical Technologies Published:2020

Abstract

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. Materials 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. Copyright © 2020 Tehran University of Medical Sciences

Style	Citing Format
MLA	Yazdani E, Sahaf SA, Rad HS. "Reconstruction of Simulated Magnetic Resonance Fingerprinting Using Accelerated Distance Metric Learning." Frontiers in Biomedical Technologies, vol. 7, no. 1, 2020, pp. 3-13.
APA	Yazdani E, Sahaf SA, Rad HS (2020). Reconstruction of Simulated Magnetic Resonance Fingerprinting Using Accelerated Distance Metric Learning. Frontiers in Biomedical Technologies, 7(1), 3-13.
Chicago	Yazdani E, Sahaf SA, Rad HS. "Reconstruction of Simulated Magnetic Resonance Fingerprinting Using Accelerated Distance Metric Learning." Frontiers in Biomedical Technologies 7, no. 1 (2020): 3-13.
Harvard	Yazdani E, Sahaf SA, Rad HS (2020) 'Reconstruction of Simulated Magnetic Resonance Fingerprinting Using Accelerated Distance Metric Learning', Frontiers in Biomedical Technologies, 7(1), pp. 3-13.
Vancouver	Yazdani E, Sahaf SA, Rad HS. Reconstruction of Simulated Magnetic Resonance Fingerprinting Using Accelerated Distance Metric Learning. Frontiers in Biomedical Technologies. 2020;7(1):3-13.
BibTex	@article{ author = {Yazdani E and Sahaf SA and Rad HS}, title = {Reconstruction of Simulated Magnetic Resonance Fingerprinting Using Accelerated Distance Metric Learning}, journal = {Frontiers in Biomedical Technologies}, volume = {7}, number = {1}, pages = {3-13}, year = {2020} }
RIS	TY - JOUR AU - Yazdani E AU - Sahaf SA AU - Rad HS TI - Reconstruction of Simulated Magnetic Resonance Fingerprinting Using Accelerated Distance Metric Learning JO - Frontiers in Biomedical Technologies VL - 7 IS - 1 SP - 3 EP - 13 PY - 2020 ER -

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