Abstract #2793

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

Zheyuan Hu^1,2,3, Tianle Cao^1,2,3, Zihao Chen^1,2,3, Yibin Xie¹, Debiao Li^1,3, and Anthony Christodoulou^1,2,3

¹Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States, ²Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States, ³Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Cardiovascular

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.

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