k-Space Deep Learning for Parallel MRI: Application to Time-Resolved MR Angiography
This addresses a domain-specific problem for MRI practitioners by enabling flexible trade-offs in resolution for time-resolved angiography, though it is incremental as it builds on existing deep learning methods for MRI.
The paper tackled the limited temporal resolution and inflexible view-sharing in time-resolved MR angiography by proposing a k-space deep learning approach, achieving high-quality reconstructions with adjustable view-sharing to balance spatial and temporal resolution.
Time-resolved angiography with interleaved stochastic trajectories (TWIST) has been widely used for dynamic contrast enhanced MRI (DCE-MRI). To achieve highly accelerated acquisitions, TWIST combines the periphery of the k-space data from several adjacent frames to reconstruct one temporal frame. However, this view-sharing scheme limits the true temporal resolution of TWIST. Moreover, the k-space sampling patterns have been specially designed for a specific generalized autocalibrating partial parallel acquisition (GRAPPA) factor so that it is not possible to reduce the number of view-sharing once the k-data is acquired. To address these issues, this paper proposes a novel k-space deep learning approach for parallel MRI. In particular, we have designed our neural network so that accurate k-space interpolations are performed simultaneously for multiple coils by exploiting the redundancies along the coils and images. Reconstruction results using in vivo TWIST data set confirm that the proposed method can immediately generate high-quality reconstruction results with various choices of view- sharing, allowing us to exploit the trade-off between spatial and temporal resolution in time-resolved MR angiography.