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Parameter map error due to normal noise and aliasing artifacts in MR fingerprinting
Author(s) -
Kara Danielle,
Fan Mingdong,
Hamilton Jesse,
Griswold Mark,
Seiberlich Nicole,
Brown Robert
Publication year - 2019
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.27638
Subject(s) - aliasing , computer science , noise (video) , sequence (biology) , algorithm , monte carlo method , pattern recognition (psychology) , artificial intelligence , imaging phantom , mathematics , statistics , undersampling , physics , biology , optics , image (mathematics) , genetics
Purpose To introduce a quantitative tool that enables rapid forecasting of T 1 and T 2 parameter map errors due to normal and aliasing noise as a function of the MR fingerprinting (MRF) sequence, which can be used in sequence optimization. Theory and Methods The variances of normal noise and aliasing artifacts in the collected signal are related to the variances in T 1 and T 2 maps through derived quality factors. This analytical result is tested against the results of a Monte‐Carlo approach for analyzing MRF sequence encoding capability in the presence of aliasing noise, and verified with phantom experiments at 3 T. To further show the utility of our approach, our quality factors are used to find efficient MRF sequences for fewer repetitions. Results Experimental results verify the ability of our quality factors to rapidly assess the efficiency of an MRF sequence in the presence of both normal and aliasing noise. Quality factor assessment of MRF sequences is in agreement with the results of a Monte‐Carlo approach. Analysis of MRF parameter map errors from phantom experiments is consistent with the derived quality factors, with T 1 (T 2 ) data yielding goodness of fit R 2 ≥ 0.92 (0.80). In phantom and in vivo experiments, the efficient pulse sequence, determined through quality factor maximization, led to comparable or improved accuracy and precision relative to a longer sequence, demonstrating quality factor utility in MRF sequence design. Conclusion The here introduced quality factor framework allows for rapid analysis and optimization of MRF sequence design through T 1 and T 2 error forecasting.