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

Background: 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. Methods: The method utilizes physical models for backpropagation of deep learning features, allowing physical priors to explicitly express the spatiotemporal correlations of images. This integration enables mutual enhancement between low-rank/sparse constraints and deep learning priors, thereby improving both constraint-based reconstruction and deep learning performances. Results: Experimental results from simulated brain data, real phantoms, and healthy volunteers demonstrate that the proposed method produces more accurate T1 and T2 maps than those obtained using conjugate gradient (CG), low-rank plus sparse (LS) matrix factorization least squares fitting, or conventional deep learning-based mapping methods. The method uses only 2-4% (spokes of 1,000 to 2,000) of full k-space data to simultaneously generate quantitative T1 and T2 maps, yet still achieves a structural similarity (SSIM) greater than 0.7 and a normalized root mean square error (nRMSE) lower than 0.1. The proposed method also yielded excellent Pearson correlation coefficients of R Conclusions: The proposed method extracts feature information through low-rank sparse priors and optimizes backpropagation via physical models, integrating the synergistic advantages of deep learning and low-rank sparse iterative processing to achieve concurrent T1 and T2 quantification.