Residual RAKI: A hybrid linear and non-linear approach for scan-specific k-space deep learning

• Published in: NeuroImage (Orlando, Fla.) Vol. 256; p. 119248
• Main Authors: Zhang, Chi, Moeller, Steen, Demirel, Omer Burak, Uğurbil, Kâmil, Akçakaya, Mehmet
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.08.2022; Elsevier Limited; Elsevier
• Subjects: Deep Learning; Design; Functional magnetic resonance imaging; Image reconstruction; Machine learning; Magnetic resonance imaging; Medical imaging; Neural networks; Noise; Noise reduction; Parallel imaging; Scan-specific; Scan-specific; Parallel imaging; Image reconstruction; Deep Learning
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2022.119248

■A hybrid linear and non-linear deep learning reconstruction approach for parallel imaging is proposed.■Improved image quality in highly accelerated MRI compared to conventional linear methods.■Improved interpretability compared to purely non-linear deep learning methods.■Improved preservation of temporal information in highly-accelerated SMS fMRI. Parallel imaging is the most clinically used acceleration technique for magnetic resonance imaging (MRI) in part due to its easy inclusion into routine acquisitions. In k-space based parallel imaging reconstruction, sub-sampled k-space data are interpolated using linear convolutions. At high acceleration rates these methods have inherent noise amplification and reduced image quality. On the other hand, non-linear deep learning methods provide improved image quality at high acceleration, but the availability of training databases for different scans, as well as their interpretability hinder their adaptation. In this work, we present an extension of Robust Artificial-neural-networks for k-space Interpolation (RAKI), called residual-RAKI (rRAKI), which achieves scan-specific machine learning reconstruction using a hybrid linear and non-linear methodology. In rRAKI, non-linear CNNs are trained jointly with a linear convolution implemented via a skip connection. In effect, the linear part provides a baseline reconstruction, while the non-linear CNN that runs in parallel provides further reduction of artifacts and noise arising from the linear part. The explicit split between the linear and non-linear aspects of the reconstruction also help improve interpretability compared to purely non-linear methods. Experiments were conducted on the publicly available fastMRI datasets, as well as high-resolution anatomical imaging, comparing GRAPPA and its variants, compressed sensing, RAKI, Scan Specific Artifact Reduction in K-space (SPARK) and the proposed rRAKI. Additionally, highly-accelerated simultaneous multi-slice (SMS) functional MRI reconstructions were also performed, where the proposed rRAKI was compred to Read-out SENSE-GRAPPA and RAKI. Our results show that the proposed rRAKI method substantially improves the image quality compared to conventional parallel imaging, and offers sharper images compared to SPARK and ℓ1-SPIRiT. Furthermore, rRAKI shows improved preservation of time-varying dynamics compared to both parallel imaging and RAKI in highly-accelerated SMS fMRI.