DeepSNMs for highly accelerated multi-contrast volumetric brain imaging

Motivation: The conventional multi-contrast 3D reconstruction process is time-consuming and lacks sufficient acceleration factors. Goal(s): To achieve highly accelerated multi-contrast brain imaging while enhancing reconstruction efficiency, the learnable CNN network is utilized. Approach: Deep learning regularized SNMs (Deep SNMs) is developed by unrolling parallel imaging reconstruction using spatial nulling maps (SNMs) with CNN regularization. The network is iteratively expanded by gradient descent blocks and 2D convolution blocks. Results: Compared to L1 regularized SNMs, the learnable CNN regularization simplifies reconstruction complexity and attains higher image quality. DeepSNMs achieves multi-contrast volumetric brain imaging reconstruction under caipirinha 9x and 12x acceleration. Impact: This work successfully accomplishes multi-contrast volumetric brain imaging with 9-fold and 12-fold caipirinha acceleration. By addressing the time-consuming challenge of reconstructing multi-contrast 3D images, this work effectively utilizes and integrates information from multiple contrasts concurrently.