Purpose: To evaluate an iterative learning approach for enhanced performance of robust artificial-neural-networks for k-space interpolation (RAKI), when only a limited amount of training data (auto-calibration signals [ACS]) are available for accelerated standard 2D imaging.
Methods:In a first step, the RAKI model was tailored for the case of limited training data amount. In the iterative learning approach (termed iterative RAKI [iRAKI]), the tailored RAKI model is initially trained using original and augmented ACS obtained from a linear parallel imaging reconstruction. Subsequently, the RAKI convolution filters are refined iteratively using original and augmented ACS extracted from the previous RAKI reconstruction. Evaluation was carried out on 200 retrospectively undersampled in vivo datasets from the fastMRI neuro database with different contrast settings.Results: For limited training data (18 and 22 ACS lines for R = 4 and R = 5, respectively), iRAKI outperforms standard RAKI by reducing residual artifacts and yields better noise suppression when compared to standard parallel imaging, underlined by quantitative reconstruction quality metrics. Additionally, iRAKI shows better performance than both GRAPPA and standard RAKI in case of pre-scan calibration with varying contrast between training-and undersampled data.
Conclusion:RAKI benefits from the iterative learning approach, which preserves the noise suppression feature, but requires less original training data for the accurate reconstruction of standard 2D images thereby improving net acceleration.
RAKI is a scan-specific k-space interpolation technique based on deep convolutional networks, which bears superior noise resilience compared to GRAPPA. However, RAKI may introduce severe blurring in image reconstruction due to reduced number of autocalibration signal lines at higher acceleration factors. We propose Gradual RAKI, which exhibits the benefit of mixing RAKI and GRAPPA in a preparatory block for data augmentation purposes prior to a conventional RAKI reconstruction. Data augmentation provides an effective way to create synthetic ACS lines out of 8 original ACS lines at 4-fold acceleration, while valuable features are retained from both RAKI and GRAPPA reconstruction methods.
Recently, the Parallel Imaging method GRAPPA has been generalized by the deep-learning method RAKI, in which Convolutional Neural Networks are used for non-linear k-space interpolation. RAKI uses scan-specific training data, however, due to its increased parameter-space, its reconstruction quality may deteriorate given a limited training-data amount. We evaluate an approach that includes augmented training-data via an initial GRAPPA k-space reconstruction, and weights refinement by iterative training. Thereby, severe residual artefacts are suppressed in RAKI, while preserving its resilience against g-factor noise enhancement in GRAPPA for standard 2D imaging at medium accelerations, for strongly varying contrast between training- and interpolation-data, too.
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