Regularized Joint Reconstruction and Slab Combination for Accelerated Three‐Dimensional Multi‐Slab Diffusion‐Weighted Imaging Using Multi‐Scale Energy Models

Purpose To jointly reconstruct high‐resolution diffusion‐weighted volumes and eliminate slab‐boundary artifacts while preserving fine anatomical detail from undersampled 3D multi‐slab k‐space acquisitions. Methods A bilinear forward model was formulated to describe the 3D multi‐slab acquisition, treating the image volume and slab excitation profiles as joint variables. A maximum a posteriori framework optimized a cost function with a Gaussian data‐fidelity term, a CNN‐based deep energy prior guiding accelerated recovery, and a quadratic regularization term anchoring the slab profiles to an initial estimate. The energy‐based prior models the negative log distribution of clean diffusion‐weighted images, while its gradient guides the accelerated reconstruction toward the artifact‐free distribution. The resulting non‐convex problem was solved via alternating minimization, where the image volume was updated through a majorize–minimize scheme with conjugate gradient optimization, and slab profiles were estimated through regularized least‐squares optimization. Results The proposed Energy‐based Profile Encoding (EPEN) framework substantially reduced slab‐boundary artifacts compared with conventional slab‐boundary correction approaches. Improved structural consistency and contrast preservation were achieved across multiple acceleration factors and slab configurations. Conclusion EPEN enables robust joint 3D multi‐slab diffusion MRI reconstruction with slab profile correction within an alternating optimization framework using deep energy‐based image priors.