A Spatial Off-Resonance Correction in Spirals for Magnetic Resonance Fingerprinting

• Published in: IEEE transactions on medical imaging Vol. 40; no. 12; pp. 3832 - 3842
• Main Authors: Coronado, Ronal, Cruz, Gastao, Castillo-Passi, Carlos, Tejos, Cristian, Uribe, Sergio, Prieto, Claudia, Irarrazaval, Pablo
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; balanced steady state free precession (BSSFP) MRF; Blurring; Brain - diagnostic imaging; Dictionaries; Error reduction; Fingerprint recognition; Fingerprinting; Humans; Image Processing, Computer-Assisted; Image reconstruction; Magnetic resonance; magnetic resonance fingerprinting (MRF); Magnetic Resonance Imaging; Magnetic Resonance Spectroscopy; Mathematical model; Off-resonance effects; Phantoms; Phantoms, Imaging; Point spread functions; Resonance; Signal to noise ratio; Simulation; spatial off-resonance correction; Spirals; Standard deviation; Substantia alba; Substantia grisea
• ISSN: 0278-0062; 1558-254X; 1558-254X
• DOI: 10.1109/TMI.2021.3100293

In MR Fingerprinting (MRF), balanced Steady-State Free Precession (bSSFP) has advantages over unbalanced SSFP because it retains the spin history achieving a higher signal-to-noise ratio (SNR) and scan efficiency. However, bSSFP-MRF is not frequently used because it is sensitive to off-resonance, producing artifacts and blurring, and affecting the parametric map quality. Here we propose a novel Spatial Off-resonance Correction (SOC) approach for reducing these artifacts in bSSFP-MRF with spiral trajectories. SOC-MRF uses each pixel's Point Spread Function to create system matrices that encode both off-resonance and gridding effects. We iteratively compute the inverse of these matrices to reduce the artifacts. We evaluated the proposed method using brain simulations and actual MRF acquisitions of a standardized T1/T2 phantom and five healthy subjects. The results show that the off-resonance distortions in T1/T2 maps were considerably reduced using SOC-MRF. For T2, the Normalized Root Mean Square Error (NRMSE) was reduced from 17.3 to 8.3% (simulations) and from 35.1 to 14.9% (phantom). For T1, the NRMS was reduced from 14.7 to 7.7% (simulations) and from 17.7 to 6.7% (phantom). For in-vivo, the mean and standard deviation in different ROI in white and gray matter were significantly improved. For example, SOC-MRF estimated an average T2 for white matter of 77ms (the ground truth was 74ms) versus 50 ms of MRF. For the same example the standard deviation was reduced from 18 ms to 6ms. The corrections achieved with the proposed SOC-MRF may expand the potential applications of bSSFP-MRF, taking advantage of its better SNR property.