Generalizable, sequence-invariant deep learning image reconstruction for subspace-constrained quantitative MRI

PurposeTo develop a deep subspace learning network that can function across different pulse sequences. MethodsA contrast-invariant component-by-component (CBC) network structure was developed and compared against previously reported spatiotemporal multicomponent (MC) structure for reconstructing MR Multitasking images. A total of 130, 167, and 16 subjects were imaged using T-1,T- T-1-T-2, and T-1-T-2- T-2*-fat fraction (FF) mapping sequences, respectively. We compared CBC and MC networks in matched-sequence experiments (same sequence for training and testing), then examined their cross-sequence performance and generalizability by unmatched-sequence experiments (different sequences for training and testing). A "universal" CBC network was also evaluated using mixed-sequence training (combining data from all three sequences). Evaluation metrics included image normalized root mean squared error and Bland-Altman analyses of end-diastolic maps, both versus iteratively reconstructed references. ResultsThe proposed CBC showed significantly better normalized root mean squared error than MC in both matched-sequence and unmatched-sequence experiments (p < 0.001), fewer structural details in quantitative error maps, and tighter limits of agreement. CBC was more generalizable than MC (smaller performance loss; p = 0.006 in T-1 and p < 0.001 in T-1-T-2 from matched-sequence testing to unmatched-sequence testing) and additionally allowed training of a single universal network to reconstruct images from any of the three pulse sequences. The mixed-sequence CBC network performed similarly to matched-sequence CBC in T1 (p = 0.178) and T-1-T-2 (p = 0121), where training data were plentiful, and performed better in T-1-T-2-T-2*-FF (p < 0.001) where training data were scarce. ConclusionContrast-invariant learning of spatial features rather than spatiotemporal features improves performance and generalizability, addresses data scarcity, and offers a pathway to universal supervised deep subspace learning.