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Purpose  To develop and evaluate methods for (1) reconstructing 3D‐quantification using an interleaved Look‐Locker acquisition sequence with  T  2  preparation pulse (3D‐QALAS) time‐series images using a low‐rank subspace method, which enables accurate and rapid  T  1  and  T  2  mapping, and (2) improving the fidelity of subspace QALAS by combining scan‐specific deep‐learning‐based reconstruction and subspace modeling.    Theory and 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  T  1  and  T  2  mapping and time‐resolved imaging using 3D‐QALAS. Using an ISMRM/NIST system phantom, the accuracy and reproducibility of the  T  1  and  T  2  maps estimated using the proposed methods were 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 nine‐fold.    Results  Phantom experiments showed that subspace QALAS had good linearity with respect to the reference methods while reducing biases and improving precision compared to conventional QALAS, especially for  T  2  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 nine‐fold acceleration with Zero‐DeepSub, which enabled whole‐brain  T  1 ,  T  2 , 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.

Zero‐DeepSub: Zero‐shot deep subspace reconstruction for rapid multiparametric quantitative MRI using 3D‐QALAS