Deep-Learning Based T1 and T2 Quantification from Undersampled Magnetic Resonance Fingerprinting Data to Track Tracer Kinetics in Small Laboratory Animals

Abstract

AbstractIn human MRI studies, magnetic resonance fingerprinting (MRF) allows simultaneous T1 and T2 mapping in 10 s using 48-fold undersampled data. However, when “reverse translated” to preclinical research involving small laboratory animals, the undersampling capacity of the MRF method decreases to 8 fold because of the low SNR associated with high spatial resolution. In this study, we aim to develop a deep-learning based method to reliably quantify T1 and T2 in the mouse brain from highly undersampled MRF data, and to demonstrate its efficacy in tracking T1 and T2 variations induced by MR tracers. The proposed method employs U-Net as the backbone for spatially constrained T1 and T2 mapping. Several strategies to improve the robustness of mapping results are evaluated, including feature extraction with sliding window averaging, implementing physics-guided training objectives, and implementing data-consistency constraint to iteratively refine the inferred maps by a cascade of U-Nets. The quantification network is trained using mouse-brain MRF datasets acquired before and after Manganese (Mn2+) enhancement. Experimental results show that robust T1 and T2 mapping can be achieved from MRF data acquired in 30 s (4-fold further acceleration), by using a simple combination of sliding window averaging for feature extraction and U-Net for parametric quantification. Meanwhile, the T1 variations induced by Mn2+ in mouse brain are faithfully detected. Code is available at https://github.com/guyn-idealab/Mouse-MRF-DL/.KeywordsDeep learningMR fingerprintingContrast-enhanced MRI