Self‐calibrated subspace reconstruction for multidimensional MR fingerprinting for simultaneous relaxation and diffusion quantification

• Published in: Magnetic resonance in medicine Vol. 91; no. 5; pp. 1978 - 1993
• Main Authors: Qiu, Zhilang, Hu, Siyuan, Zhao, Walter, Sakaie, Ken, Sun, Jessie E. P., Griswold, Mark A., Jones, Derek K., Ma, Dan
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.05.2024
• Subjects: Algorithms; Aliasing; Brain - diagnostic imaging; Calibration; Data analysis; Efficiency; Fingerprinting; Gating; Humans; Image Processing, Computer-Assisted - methods; Image reconstruction; In vivo methods and tests; Magnetic Resonance Imaging - methods; Neuroimaging; Outliers (statistics); Phantoms, Imaging; Physiology; Robustness; Shading; Subspaces; MR fingerprinting; diffusion MRI; low-rank matrix completion; low-rank subspace reconstruction; time-resolved image reconstruction
• DOI: 10.1002/mrm.29969
• ISSN: 0740-3194; 1522-2594

To propose a new reconstruction method for multidimensional MR fingerprinting (mdMRF) to address shading artifacts caused by physiological motion-induced measurement errors without navigating or gating. The proposed method comprises two procedures: self-calibration and subspace reconstruction. The first procedure (self-calibration) applies temporally local matrix completion to reconstruct low-resolution images from a subset of under-sampled data extracted from the k-space center. The second procedure (subspace reconstruction) utilizes temporally global subspace reconstruction with pre-estimated temporal subspace from low-resolution images to reconstruct aliasing-free, high-resolution, and time-resolved images. After reconstruction, a customized outlier detection algorithm was employed to automatically detect and remove images corrupted by measurement errors. Feasibility, robustness, and scan efficiency were evaluated through in vivo human brain imaging experiments. The proposed method successfully reconstructed aliasing-free, high-resolution, and time-resolved images, where the measurement errors were accurately represented. The corrupted images were automatically and robustly detected and removed. Artifact-free T1, T2, and ADC maps were generated simultaneously. The proposed reconstruction method demonstrated robustness across different scanners, parameter settings, and subjects. A high scan efficiency of less than 20 s per slice has been achieved. The proposed reconstruction method can effectively alleviate shading artifacts caused by physiological motion-induced measurement errors. It enables simultaneous and artifact-free quantification of T1, T2, and ADC using mdMRF scans without prospective gating, with robustness and high scan efficiency.