Purpose: To develop and evaluate methods for 1) reconstructing
3D-quantification using an interleaved Look-Locker acquisition sequence with T2
preparation pulse (3D-QALAS) time-series images using a low-rank subspace
method, which enables accurate and rapid T1 and T2 mapping, and 2) improving
the fidelity of subspace QALAS by combining scan-specific deep-learning-based
reconstruction and subspace modeling. Methods: A low-rank subspace method for
3D-QALAS (i.e., subspace QALAS) and zero-shot deep-learning subspace method
(i.e., Zero-DeepSub) were proposed for rapid and high fidelity T1 and T2
mapping and time-resolved imaging using 3D-QALAS. Using an ISMRM/NIST system
phantom, the accuracy of the T1 and T2 maps estimated using the proposed
methods was evaluated by comparing them with reference techniques. The
reconstruction performance of the proposed subspace QALAS using Zero-DeepSub
was evaluated in vivo and compared with conventional QALAS at high reduction
factors of up to 9-fold. Results: Phantom experiments showed that subspace
QALAS had good linearity with respect to the reference methods while reducing
biases compared to conventional QALAS, especially for T2 maps. Moreover, in
vivo results demonstrated that subspace QALAS had better g-factor maps and
could reduce voxel blurring, noise, and artifacts compared to conventional
QALAS and showed robust performance at up to 9-fold acceleration with
Zero-DeepSub, which enabled whole-brain T1, T2, and PD mapping at 1 mm
isotropic resolution within 2 min of scan time. Conclusion: The proposed
subspace QALAS along with Zero-DeepSub enabled high fidelity and rapid
whole-brain multiparametric quantification and time-resolved imaging.Comment: 17 figures, 3 table