Application of a k-Space Interpolating Artificial Neural Network to In-Plane Accelerated Simultaneous Multislice Imaging

Purpose: The goal of this work is to extend the capabilities of RAKI, a k-space interpolating neural network, to reconstruct high-quality images from in-plane accelerated simultaneous multislice imaging acquisitions. This method is referred to as slice-RAKI. Methods: A three-layer convolutional neural network was designed to output k-space signals for separate slices given the input of a multicoil slice-aliased k-space. The output of the slice-interpolation network is passed into a separate in-plane interpolating network for each slice. The proposed framework was tested in retrospective acceleration experiments in vivo, and in prospectively accelerated phantom and in vivo experiments. Results: The neural network interpolation based reconstruction quantitatively outperforms conventional parallel imaging reconstruction algorithms for all tested in-plane and simultaneous multislice acceleration factors. Visually, the neural network reconstructions are of superior quality compared to parallel imaging reconstructions in prospectively accelerated acquisitions. Conclusion: Slice-RAKI provides a patient specific neural network based non-linear reconstruction which improves image quality compared with conventional linear parallel imaging algorithms. It could find use in MR-guided interventions such as MR-guided radiation therapy for use in rapid real-time cine imaging for motion monitoring.