Magnetic Resonance Fingerprinting Using a Fast Dictionary Searching Algorithm: MRF-ZOOM

• Published in: IEEE transactions on biomedical engineering Vol. 66; no. 6; pp. 1526 - 1535
• Main Authors: Wang, Ze, Zhang, Jian, Cui, Di, Xie, Jun, Lyu, Mengye, Hui, Edward S., Wu, Ed. X.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.06.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Bloch simulation; Brain - diagnostic imaging; Complexity theory; Computer applications; Computer Simulation; Dictionaries; fast searching; Fingerprint recognition; Fingerprinting; Humans; Image Processing, Computer-Assisted - methods; Information Storage and Retrieval; Linear programming; Magnetic resonance; Magnetic resonance fingerprinting; Magnetic Resonance Imaging - methods; Matching; Mathematical model; MRI; Objective function; Parameters; Radio frequency; Search algorithms; Signal generation; Signal processing; Signal Processing, Computer-Assisted; Spatial discrimination; Spatial resolution
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2018.2874992

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.