OBJECTIVE: Magnetic resonance fingerprinting (MRF) is a new technique for simultaneously quantifying multiple MR parameters using one temporally resolved MR scan. In MRF, MR signal is manipulated to have distinct temporal behavior with regard to different combinations of the underlying MR parameters and across spatial regions. The temporal behavior of acquired MR signal is then used as a key to find its unique counterpart in a MR signal dictionary. The dictionary generation and searching (DGS) process represents the most important part of MRF, which however can be intractable because of the disk space requirement and the computational demand exponentially increases with the number of MR parameters, spatial coverage, and spatial resolution. The goal of this paper was to develop a fast and space efficient MRF DGS algorithm. METHODS: The optimal DGS algorithm: MRF ZOOM was designed based on the properties of the parameter matching objective function characterized with full dictionary simulations. Both synthetic data and in-vivo data were used to validate the method. CONCLUSION: MRF ZOOM can dramatically save MRF DGS time without sacrificing matching accuracy. SIGNIFICANCE: MRF ZOOM can facilitate a wide range of MRF applications.
OBJECTIVE: Magnetic resonance fingerprinting (MRF) is a new technique for simultaneously quantifying multiple MR parameters using one temporally resolved MR scan. In MRF, MR signal is manipulated to have distinct temporal behavior with regard to different combinations of the underlying MR parameters and across spatial regions. The temporal behavior of acquired MR signal is then used as a key to find its unique counterpart in a MR signal dictionary. The dictionary generation and searching (DGS) process represents the most important part of MRF, which however can be intractable because of the disk space requirement and the computational demand exponentially increases with the number of MR parameters, spatial coverage, and spatial resolution. The goal of this paper was to develop a fast and space efficient MRF DGS algorithm. METHODS: The optimal DGS algorithm: MRF ZOOM was designed based on the properties of the parameter matching objective function characterized with full dictionary simulations. Both synthetic data and in-vivo data were used to validate the method. CONCLUSION: MRF ZOOM can dramatically save MRF DGS time without sacrificing matching accuracy. SIGNIFICANCE: MRF ZOOM can facilitate a wide range of MRF applications.
Authors: Debra F McGivney; Rasim Boyacıoğlu; Yun Jiang; Megan E Poorman; Nicole Seiberlich; Vikas Gulani; Kathryn E Keenan; Mark A Griswold; Dan Ma Journal: J Magn Reson Imaging Date: 2019-07-25 Impact factor: 4.813
Authors: Pan Su; Hongli Fan; Peiying Liu; Yang Li; Ye Qiao; Jun Hua; Doris Lin; Dengrong Jiang; Jay J Pillai; Argye E Hillis; Hanzhang Lu Journal: NMR Biomed Date: 2019-11-04 Impact factor: 4.044