Soyak R.Ersoy E.O.Navruz E.Fakultesi M.Unay D.Oksuz I.2023-06-162023-06-1620209.78E+12https://doi.org/10.1109/SIU49456.2020.9302455https://hdl.handle.net/20.500.14365/361928th Signal Processing and Communications Applications Conference, SIU 2020 -- 5 October 2020 through 7 October 2020 -- 166413Magnetic Resonance Fingerprinting is a recent technique which aims at providing simultaneous measurements of multiple parameters. MRF works by varying acquisition parameters in a pseudorandom manner so as to get unique, uncorrelated signal evolutions from each tissue. MRF is a dictionary based approach, and thus requires a database. This database can be created by simulating the signal evolutions from first principles using different physical models for a wide variety of tissue parameter combinations. Having this dictionary, a pattern recognition algorithm is used to match the acquired signal evolutions from each voxel with each signal evolution in the dictionary. In this paper, we compare the efficiency of deep learning based feature extraction method and neural network architectures in order to achieve state-of-the-art accuracy in dictionary matching for MRF. Our results showcase successful dictionary matching with high accuracy both quantitatively and qualitatively. © 2020 IEEE.trinfo:eu-repo/semantics/closedAccessMagnetic Resonance Imaging, MR Fingerprinting, Deep Learning, Medical Image Analysis, Dictionary Matching, Pattern Recognition.Deep learningExtractionFeature extractionMagnetic resonanceMagnetorheological fluidsNetwork architectureSignal processingTissueAcquisition parametersDictionary matchingLearning-based feature extractionsMultiple parametersParameter combinationPattern recognition algorithmsSimultaneous measurementUncorrelated signalsNeural networksConference Object10.1109/SIU49456.2020.93024552-s2.0-85100311462