MR fingerprinting reconstruction with Kalman filter |
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| Affiliation: | 1. The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Health Sciences Research Building, 1760 Haygood Drive, Suite W200, Atlanta, GA 30322, USA;2. Center for Biomedical Imaging Research, Tsinghua University, Beijing 100084, China;3. Department of Bioengineering, University of California, Riverside, 900 University Avenue, Riverside, CA 92521, USA;1. Istituto Nazionale di Fisica Nucleare (INFN), Pisa, Italy;2. GE Global Research, Munich, Germany;3. Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy;4. IRCCS Stella Maris Foundation, Pisa, Italy;5. IMAGO7 Foundation, Pisa, Italy;1. Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA;3. The Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, NY, USA;4. Siemens Medical Solutions USA Inc., 40 Liberty Boulevard, Malvern, PA 19355, USA;1. Dept. of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA;2. Dept. of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA |
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| Abstract: | Magnetic resonance fingerprinting (MR fingerprinting or MRF) is a newly introduced quantitative magnetic resonance imaging technique, which enables simultaneous multi-parameter mapping in a single acquisition with improved time efficiency. The current MRF reconstruction method is based on dictionary matching, which may be limited by the discrete and finite nature of the dictionary and the computational cost associated with dictionary construction, storage and matching.In this paper, we describe a reconstruction method based on Kalman filter for MRF, which avoids the use of dictionary to obtain continuous MR parameter measurements. With this Kalman filter framework, the Bloch equation of inversion-recovery balanced steady state free-precession (IR-bSSFP) MRF sequence was derived to predict signal evolution, and acquired signal was entered to update the prediction. The algorithm can gradually estimate the accurate MR parameters during the recursive calculation. Single pixel and numeric brain phantom simulation were implemented with Kalman filter and the results were compared with those from dictionary matching reconstruction algorithm to demonstrate the feasibility and assess the performance of Kalman filter algorithm.The results demonstrated that Kalman filter algorithm is applicable for MRF reconstruction, eliminating the need for a pre-define dictionary and obtaining continuous MR parameter in contrast to the dictionary matching algorithm. |
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