Deep Learning regularized SPIRiT reconstruction accelerates joint intracranial and carotid vessel wall imaging into 3.5 minutes

Deep learning regularized SPIRiT reconstruction is developed by unrolling the conventional L1-SPIRiT optimization solved by the projection onto convex sets (POCS) iteration into a multi-layer convolutional neural network. The learnable network regularization with 3D convolution improved the reconstruction accuracy and efficiency compared with the iterative L1-SPIRiT with 2D sparsity regularization in a fixed transform domain. The simplicity of POCS iteration also benefits the design complexity of the DL-SPIRiT network. The proposed DL-SPIRiT could accelerate the joint intracranial and carotid vessel wall imaging of isotropic 0.6 mm resolution by 8-fold, leading to a scan time of only 3.5 minutes.