reconstruction of simulated magnetic resonance fingerprinting using accelerated distance metric learning

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.