Mixed-sequence training for deep subspace learning image reconstruction of T1-T2-T2*-FF CMR Multitasking data

Motivation: Multi-parametric mapping using T1-T2-T2*-fat fraction (FF) MR Multitasking is promising but is hindered by lengthy reconstruction times. Goal(s): To improve T1-T2-T2*-FF Multitasking reconstruction time with deep subspace learning, overcoming challenges in training data scarcity and network scalability to high-dimensional spatial factors. Approach: A component-by-component (CBC) network structure was evaluated for three training strategies: 1) large T1 data, 2) limited T1-T2-T2*-FF data, and 3) multi-domain, mixed-sequence learning on both T1 and T1-T2-T2*-FF data. Results: Mixed-domain learning demonstrated superior image reconstruction quality, achieving the lowest normalized root mean squared error, displaying fewer structural artifacts, and narrowing Bland-Altman limits of agreement. Impact: Component-by-component deep-subspace-learning image reconstruction with mixed-sequence training can dramatically speed up T1-T2-T2*-fat fraction (FF) MR Multitasking image reconstruction by approximately 600 times, potentially overcoming a major barrier to clinical translation.