Spatial-temporal and physical constrained deep learning model for simultaneous T1 and T2 reconstruction and mapping (STEP)

Magnetic resonance (MR) parametric maps provide quantitative tissue characteristics that are valuable for medical diagnosis. However, existing T1 and T2 measurement techniques confront challenges such as prolonged reconstruction/fitting times and the requirement for multi-sequence image registration, limiting the clinical applicability of quantitative parameter mapping. The development of compressed sensing combined with parallel imaging technology has improved reconstruction efficiency. However, traditional two-step workflow lacks spatial constraints for parameter mapping and suffers from slow pixel-level fitting. Furthermore, deep learning methods are applied to reconstruction to accelerate reconstruction and remove noise but exhibit heavy dependence on training datasets and neglect to incorporate inherent low-rank and sparse data constraints. To address these challenges, this study proposes the spatial-temporal and physical constrained deep learning model for simultaneous T1 and T2 reconstruction and mapping (STEP) method.