ArticleMagnetic resonance in medicine2019
Wave-LORAKS: Combining wave encoding with structured low-rank matrix modeling for more highly accelerated 3D imaging.
Article in Magnetic resonance in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Group-Patch Joint Compression: Compressing Dynamic BMagnetic resonance in medicine · 2026Article
- A neuroimaging atlas of the nigrosomes in the substantia nigra based on 3D histology.Scientific data · 2026Article
- A New k-Space Model for Non-Cartesian Fourier Imaging.IEEE transactions on computational imaging · 2026Article
- Enhanced Partial Fourier MRI With Zero-Shot Deep Untrained Priors.IEEE access : practical innovations, open solutions · 2024Article
- Highly accelerated intracranial time-of-flight magnetic resonance angiography using wave-encoding.Magnetic resonance in medicine · 2023Article
- Fiber-orientation independent component of RFrontiers in neuroscience · 2023Article
- Wave-Encoded Model-Based Deep Learning for Highly Accelerated Imaging with Joint Reconstruction.Bioengineering (Basel, Switzerland) · 2022Article
- Article
- Scan-specific artifact reduction in k-space (SPARK) neural networks synergize with physics-based reconstruction to accelerate MRI.Magnetic resonance in medicine · 2022Article
- High-dimensional fast convolutional framework (HICU) for calibrationless MRI.Magnetic resonance in medicine · 2021Article
- Improved cortical surface reconstruction using sub-millimeter resolution MPRAGE by image denoising.NeuroImage · 2021Article
- Improved MUSSELS reconstruction for high-resolution multi-shot diffusion weighted imaging.Magnetic resonance in medicine · 2020Article
- Linear Predictability in MRI Reconstruction: Leveraging Shift-Invariant Fourier Structure for Faster and Better Imaging.IEEE signal processing magazine · 2020Article
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Authors and funding
5 authors.
Funding
Abstract
purposeWave-CAIPI is a novel acquisition approach that enables highly accelerated 3D imaging. This paper investigates the combination of Wave-CAIPI with LORAKS-based reconstruction (Wave-LORAKS) to enable even further acceleration.
methodsLORAKS is a constrained image reconstruction framework that can impose spatial support, smooth phase, sparsity, and/or parallel imaging constraints. LORAKS requires minimal prior information, and instead uses the low-rank subspace structure of the raw data to automatically learn which constraints to impose and how to impose them. Previous LORAKS implementations addressed 2D image reconstruction problems. In this work, several recent advances in structured low-rank matrix recovery were combined to enable large-scale 3D Wave-LORAKS reconstruction with improved quality and computational efficiency. Wave-LORAKS was investigated by retrospective subsampling of two fully sampled Wave-encoded 3D MPRAGE datasets, and comparisons were made against existing Wave reconstruction approaches.
resultsResults show that Wave-LORAKS can yield higher reconstruction quality with 16×-accelerated data than is obtained by traditional Wave-CAIPI with 9×-accerated data.
conclusionsThere are strong synergies between Wave encoding and LORAKS, which enables Wave-LORAKS to achieve higher acceleration and more flexible sampling compared to Wave-CAIPI.
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